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Background and Aims: A significant source of man-made radiation is now linked to medical devices especially X-ray imaging based ones like CT scans which expose the body to cumulative ionizing radiation and thus attendant cancer risks. The aim of this study was to determine whether using a combination of Automatic Tube Current Modulation (ATCM) and Automatic Tube Voltage Selection (ATVS) during two-phase whole-body CT (2PWBCT) examinations would reduce the radiation dose while preserving the image quality., Patients and Methods: This was a prospective, observational, single-centre study of 127 adult patients who had undergone the 2PWBCT polytraumatic protocol. All were examined on a Somatom Drive scanner (Siemens). The patients were divided into two groups: ATCM only (42 patients) and ATCM +ATVS (85 patients). Patients' arm positions during examination and the examination dose length product (DLP) values were recorded, as well the standard deviations (SD) of the density in reference areas on CT scans for the image quality assessment. The DLP values and image quality in the groups were compared using ANOVA., Results: Mean Total DLP (in mGy*cm): ATCM only: 3337 +/-797, ATCM+ATVS: 3402 +/-830; P=0.674. No effect of arm position (P=0.586). Mean density SD values in reference areas (in HU) in ATCM only: 49 +/-45, 15 +/-6, 9 +/-2, 12 +/-4, 10 +/-3, in ATCM+ATVS: 48 +/-45, 17 +/-6, 11 +/-3, 15 +/-6, 12 +/-4. SD values was higher in ATCM+ATVS group (P<0.001)., Conclusion: Combination of ATVS and ATCM in polytraumatic 2PWBCT leads to no significant radiation load reduction compared with ATCM only but does lead to a slight degradation of image quality. The radiation load is significantly reduced if the patient has their arms behind the head when scanning, regardless of the activation of ATVS., Competing Interests: The authors report no conflicts of interest in this work.

Purpose: To develop a deep learning-based cascaded HRNet model, in order to automatically measure X-ray imaging parameters of lumbar sagittal curvature and to evaluate its prediction performance., Methods: A total of 3730 lumbar lateral digital radiography (DR) images were collected from picture archiving and communication system (PACS). Among them, 3150 images were randomly selected as the training dataset and validation dataset, and 580 images as the test dataset. The landmarks of the lumbar curve index (LCI), lumbar lordosis angle (LLA), sacral slope (SS), lumbar lordosis index (LLI), and the posterior edge tangent angle of the vertebral body (PTA) were identified and marked. The measured results of landmarks on the test dataset were compared with the mean values of manual measurement as the reference standard. Percentage of correct key-points (PCK), intra-class correlation coefficient (ICC), Pearson correlation coefficient (r), mean absolute error (MAE), mean square error (MSE), root-mean-square error (RMSE), and Bland-Altman plot were used to evaluate the performance of the cascade HRNet model., Results: The PCK of the cascaded HRNet model was 97.9-100% in the 3 mm distance threshold. The mean differences between the reference standard and the predicted values for LCI, LLA, SS, LLI, and PTA were 0.43 mm, 0.99°, 1.11°, 0.01 mm, and 0.23°, respectively. There were strong correlation and consistency of the five parameters between the cascaded HRNet model and manual measurements (ICC = 0.989-0.999, R = 0.991-0.999, MAE = 0.63-1.65, MSE = 0.61-4.06, RMSE = 0.78-2.01)., Conclusion: The cascaded HRNet model based on deep learning algorithm could accurately identify the sagittal curvature-related landmarks on lateral lumbar DR images and automatically measure the relevant parameters, which is of great significance in clinical application., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Hyponatremia is the most common electrolyte abnormality in hospitalized patients. Treatment of hyponatremia is associated with improved outcomes, but more than one in three cases of new onset hyponatremia is not corrected by the time of hospital discharge. Nephrologist input may improve the diagnosis and treatment of hyponatremia, but specialist resources are limited. Targeted automatic electronic consultations (TACos) may be one approach to provide expert nephrologist guidance to the workup and management of hyponatremia using a scalable model., Objective: Evaluate the feasibility and acceptability of a TACo intervention for hospitalized patients with hyponatremia., Design: Single-site, parallel-group cluster randomized trial., Participants: Adult inpatients with hyponatremia on the hospital medicine service., Interventions: A nephrologist conducted TACos on intervention patients, making diagnostic and therapeutic recommendations daily (if warranted) until discharge or resolution of hyponatremia., Main Measures: Measures of feasibility included the number of eligible participants, percentage receiving TACos, number of TACos per participant, and percentage of formal nephrology consults. Acceptability was assessed by a post-intervention survey. Clinical outcomes, including the percentage of hyponatremia cases that resolved by discharge, were also assessed., Key Results: We identified 62 patients who met inclusion criteria: 38 in the intervention group and 24 in the control group. A nephrologist determined that 26 of 38 intervention patients (68%) would likely benefit from diagnostic and management recommendations; 67 TACos were performed (mean 2.6 per patient). Fourteen of 18 primary team physicians (78%) reported that the e-consults changed their management, and 15 of 18 (83%) wanted TACOs to continue. Resolution of hyponatremia, length of stay, 30-day readmissions, and costs were similar in the intervention and control groups., Conclusions: Inpatient TACos for hyponatremia were feasible and acceptable to primary teams, and frequently led to changes in diagnosis and management. Further studies are needed to determine the impact of the TACo model on clinical outcomes and cost-effectiveness., (© 2024. The Author(s).)

Recent years have seen a dramatic rise in the use of passive acoustic monitoring (PAM) for biological and ecological applications, and a corresponding increase in the volume of data generated. However, data sets are often becoming so sizable that analysing them manually is increasingly burdensome and unrealistic. Fortunately, we have also seen a corresponding rise in computing power and the capability of machine learning algorithms, which offer the possibility of performing some of the analysis required for PAM automatically. Nonetheless, the field of automatic detection of acoustic events is still in its infancy in biology and ecology. In this review, we examine the trends in bioacoustic PAM applications, and their implications for the burgeoning amount of data that needs to be analysed. We explore the different methods of machine learning and other tools for scanning, analysing, and extracting acoustic events automatically from large volumes of recordings. We then provide a step-by-step practical guide for using automatic detection in bioacoustics. One of the biggest challenges for the greater use of automatic detection in bioacoustics is that there is often a gulf in expertise between the biological sciences and the field of machine learning and computer science. Therefore, this review first presents an overview of the requirements for automatic detection in bioacoustics, intended to familiarise those from a computer science background with the needs of the bioacoustics community, followed by an introduction to the key elements of machine learning and artificial intelligence that a biologist needs to understand to incorporate automatic detection into their research. We then provide a practical guide to building an automatic detection pipeline for bioacoustic data, and conclude with a discussion of possible future directions in this field., (© 2024 The Author(s). Biological Reviews published by John Wiley & Sons Ltd on behalf of Cambridge Philosophical Society.)

Objective: Recognition of auditory brainstem response (ABR) waveforms may be challenging, particularly for older individuals or those with hearing loss. This study aimed to investigate deep learning frameworks to improve the automatic recognition of ABR waveforms in participants with varying ages and hearing levels., Study Design: The research used a descriptive study design to collect and analyze pure tone audiometry and ABR data from 100 participants., Setting: The research was conducted at a tertiary academic medical center, specifically at the Clinical Audiology Center of Tsinghua Chang Gung Hospital (Beijing, China)., Methods: Data from 100 participants were collected and categorized into four groups based on age and hearing level. Features from both time-domain and frequency-domain ABR signals were extracted and combined with demographic factors, such as age, sex, pure-tone thresholds, stimulus intensity, and original signal sequences to generate feature vectors. An enhanced Wide&Deep model was utilized, incorporating the Light-multi-layer perceptron (MLP) model to train the recognition of ABR waveforms. The recognition accuracy (ACC) of each model was calculated for the overall data set and each group., Results: The ACC rates of the Light-MLP model were 97.8%, 97.2%, 93.8%, and 92.0% for Groups 1 to 4, respectively, with a weighted average ACC rate of 95.4%. For the Wide&Deep model, the ACC rates were 93.4%, 90.8%, 92.0%, and 88.3% for Groups 1 to 4, respectively, with a weighted average ACC rate of 91.0%., Conclusion: Both the Light-MLP model and the Wide&Deep model demonstrated excellent ACC in automatic recognition of ABR waveforms across participants with diverse ages and hearing levels. While the Wide&Deep model's performance was slightly poorer than that of the Light-MLP model, particularly due to the limited sample size, it is anticipated that with an expanded data set, the performance of Wide&Deep model may be further improved., (© 2024 The Author(s). Otolaryngology–Head and Neck Surgery published by Wiley Periodicals LLC on behalf of American Academy of Otolaryngology–Head and Neck Surgery Foundation.)

In addition to higher-order executive functions, underlying sensory processing ability is also thought to play an important role in Attention-Deficit/Hyperactivity Disorder (AD/HD). An event-related potential feature, the mismatch negativity, reflects the ability of automatic sensory change processing and may be correlated with AD/HD symptoms and executive functions. This study aims to investigate the characteristics of visual mismatch negativity (vMMN) in adults with AD/HD. Twenty eight adults with AD/HD and 31 healthy controls were included in this study. These two groups were matched in age, IQ and sex. In addition, both groups completed psychiatric evaluations, a visual ERP task used to elicit vMMN, and psychological measures about AD/HD symptoms and day-to-day executive functions. Compared to trols, the late vMMN (230-330 ms) was significantly reduced in the AD/HD group. Correlation analyses showed that late vMMN was correlated with executive functions but not AD/HD symptoms. However, further mediation analyses showed that different executive functions had mediated the relationships between late vMMN and AD/HD symptoms. Our findings indicate that the late vMMN, reflecting automatic sensory change processing ability, was impaired in adults with AD/HD. This impairment could have negative impact on AD/HD symptoms via affecting day-to-day executive functions., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany.)

Native T1 mapping is a non-invasive technique used for early detection of diffused myocardial abnormalities, and it provides baseline tissue characterization. Post-contrast T1 mapping enhances tissue differentiation, enables extracellular volume (ECV) calculation, and improves myocardial viability assessment. Accurate and precise segmenting of the left ventricular (LV) myocardium on T1 maps is crucial for assessing myocardial tissue characteristics and diagnosing cardiovascular diseases (CVD). This study presents a deep learning (DL)-based pipeline for automatically segmenting LV myocardium on T1 maps and automatic computation of radial T1 and ECV values. The study employs a multicentric dataset consisting of retrospective multiparametric MRI data of 332 subjects to develop and assess the performance of the proposed method. The study compared DL architectures U-Net and Deep Res U-Net for LV myocardium segmentation, which achieved a dice similarity coefficient of 0.84 ± 0.43 and 0.85 ± 0.03, respectively. The dice similarity coefficients computed for radial sub-segmentation of the LV myocardium on basal, mid-cavity, and apical slices were 0.77 ± 0.21, 0.81 ± 0.17, and 0.61 ± 0.14, respectively. The t-test performed between ground truth vs. predicted values of native T1, post-contrast T1, and ECV showed no statistically significant difference (p > 0.05) for any of the radial sub-segments. The proposed DL method leverages the use of quantitative T1 maps for automatic LV myocardium segmentation and accurately computing radial T1 and ECV values, highlighting its potential for assisting radiologists in objective cardiac assessment and, hence, in CVD diagnostics., (© 2024 John Wiley & Sons Ltd.)

Diffusion tensor imaging (DTI) can provide unique contrast and insight into microstructural changes with age or disease of the hippocampus, although it is difficult to measure the hippocampus because of its comparatively small size, location, and shape. This has been markedly improved by the advent of a clinically feasible 1-mm isotropic resolution 6-min DTI protocol at 3 T of the hippocampus with limited brain coverage of 20 axial-oblique slices aligned along its long axis. However, manual segmentation is too laborious for large population studies, and it cannot be automatically segmented directly on the diffusion images using traditional T 1 or T 2 image-based methods because of the limited brain coverage and different contrast. An automatic method is proposed here that segments the hippocampus directly on high-resolution diffusion images based on an extension of well-known deep learning architectures like UNet and UNet++ by including additional dense residual connections. The method was trained on 100 healthy participants with previously performed manual segmentation on the 1-mm DTI, then evaluated on typical healthy participants (n = 53), yielding an excellent voxel overlap with a Dice score of ~ 0.90 with manual segmentation; notably, this was comparable with the inter-rater reliability of manually delineating the hippocampus on diffusion magnetic resonance imaging (MRI) (Dice score of 0.86). This method also generalized to a different DTI protocol with 36% fewer acquisitions. It was further validated by showing similar age trajectories of volumes, fractional anisotropy, and mean diffusivity from manual segmentations in one cohort (n = 153, age 5-74 years) with automatic segmentations from a second cohort without manual segmentations (n = 354, age 5-90 years). Automated high-resolution diffusion MRI segmentation of the hippocampus will facilitate large cohort analyses and, in future research, needs to be evaluated on patient groups., (© 2024 The Author(s). NMR in Biomedicine published by John Wiley & Sons Ltd.)

Purpose: To evaluate the feasibility and efficiency between the two intelligent auto-registrations (based on hepatic vessels or based on liver surface) and manual registration for US-CT/MR fusion imaging of liver tumours., Methods: From May 2017 to December 2017, 30 patients with 30 liver tumours were enrolled in this prospectively study. Two intelligent auto-registrations (based on hepatic vessels or based on liver surface) and manual registration were randomly performed, the registration success rate and efficiency were compared., Results: In terms of success rate, auto-registrations based on the hepatic vessels (80%) was lower than auto-registration base on liver surface and manual registration (96.67%), but with no statistical difference (P = 0.125). In comparison of the registration efficiency, the efficiency of the auto-registration based on the hepatic vessels was superior to auto-registration based on liver surface and manual registration (P < 0.05). The one-step success rate of auto-registration based on the hepatic vessels (53.33%, 16/30) was higher than that of other two registrations (P < 0.05). Stratified analysis showed that, for the lesion with display of hepatic vessels in grade 3, the success rate of auto-registration based on vessels (0%) was lower than that of auto-registration based on liver surface and manual registration (100%) (P = 0.031)., Conclusion: Intelligent auto-registration based on hepatic vessels is a feasible and efficient registration method for US-CT/MR fusion imaging of liver tumours for the patients with clear hepatic vessels. The auto-registration based on liver surface and manual registration can be an effective supplement for cases with poor hepatic vessels display., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

The automatic detection and counting of wheat spike images are of great significance for yield prediction and variety evaluation. Therefore, accurate and timely estimation of spike numbers is crucial for wheat production. However, in actual production, due to the susceptibility of wheat spike images to factors such as lighting conditions, shooting angles, occlusion, and overlap, the contour and features of wheat spike is unclear, which affects the accuracy of automatic detection and counting of wheat spike. In order to solve the above problems and further improve the accuracy of wheat spike counting, an improved wheat spike counting model DMseg-Count was proposed by enhancing local contextual supervision information based on existing target object counting model DM-Count. Firstly, wheat spike local segmentation branch was introduced to improve the network architecture of DM-Count, so as to extract the local contextual supervision information of wheat spike. Secondly, an element-by-element point multiplication mechanism was designed to fuse global and local contextual supervision information of wheat spike. Finally, the total loss function was constructed to optimize the model. The test results showed that the mean absolute error (MAE) and root mean square error (RMSE) of the proposed DMseg-Count model were 5.79 and 7.54, respectively, which were 9.76 and 10.91 higher than the standard distribution matching for crowd counting (DM-Count) model. Compared with other deep learning models, the proposed DMseg-Count model can detect wheat spike image in challenging situations, and has better computer vision processing capabilities and performance evaluation detection effect. In summary, the proposed DMseg-Count model can effectively detect wheat spike and has good counting performance, which provides a new method for automatic counting of wheat spike and yield prediction in complex field environments., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Objectives: This study developed and evaluated a two-stage deep learning-based system for automatic segmentation of mandibular cortical bone, mandibular cancellous bone, maxillary cortical bone and maxillary cancellous bone on cone beam computed tomography (CBCT) images., Materials and Methods: A dataset containing 155 CBCT scans acquired with different parameters was obtained. A two-stage deep learning-based system was developed for automatically segmenting jawbone structures. The Dice similarity coefficient (DSC) and average symmetric surface distance (ASSD) were used to assess the segmentation performance of the system by comparing the automatic segmentation results with the ground truth. The impact of dental and quality abnormalities on segmentation performance was analysed, and a comparison of automatic segmentation (AS) with manually refined segmentation (MRS) was reported., Results: The system achieved promising segmentation performance, with average DSC values of 93.69%, 96.83%, 86.14% and 95.57% and average ASSD values of 0.13 mm, 0.16 mm, 0.29 mm and 0.41 mm for the mandibular cortical bone, mandibular cancellous bone, maxillary cortical bone and maxillary cancellous bone, respectively. Quality abnormalities had a negative impact on segmentation performance. The performance metrics (DSCs > 98.8% and ASSDs < 0.1 mm) indicated high overlap between the AS and MRS., Conclusion: The proposed system offers an accurate and time-efficient method for segmenting jawbone structures on CBCT images., Clinical Relevance: Automatically segmenting jawbone structures is essential in most digital dental workflows. The proposed system has considerable potential for application in digital clinical workflows to assist dentists in making more accurate diagnoses and developing patient-specific treatment plans., Competing Interests: Declarations. Ethical approval: This study was approved by the institutional review boards of West China College of Stomatology, Sichuan University (WCHSIRB-D-2021-331). This study was performed in accordance with the ethical standards as laid down in the 1964 Declaration of Helsinki and its later amendments or comparable ethical standards. Competing interests: The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Purpose: To predict survival and tumor recurrence following image-guided thermal ablation (IGTA) of lung tumors segmented using a deep learning approach., Methods and Materials: A total of 113 patients who underwent IGTA for primary and metastatic lung tumors at a single institution between January 1, 2004 and July 14, 2022 were retrospectively identified. A pretrained U-Net model was applied to the dataset of pre- and post-procedure CT scans to segment lung zones. Following lung segmentation, a U-shaped encoder-decoder transformer architecture (UNETR) was trained to segment lung tumors from pre- and post-procedure CT scans, and radiomic features were automatically extracted. These features were input into a support vector machine (SVM)-based survival prediction model trained to assign rank scores to samples based on binary survival or recurrence label and follow-up time. C-index and time-dependent AUC were subsequently calculated to evaluate model performance., Results: Initial tumor segmentation using UNETR achieved a Dice score of 0.75. Applying a radiomics-based survivability prediction model to the post-procedure scans resulted in a c-index of 0.71 and a time-dependent AUC of 0.75. In contrast, when this model was applied to pre-procedure scans, it achieved a 0.56 for both metrics. For predicting time to recurrence, the radiomics-based model achieved a c-index of 0.65 and a time-dependent AUC of 0.72 on post-procedure imaging. In contrast, when this model was applied to pre-procedure scans, it achieved a 0.54 for both metrics., Conclusion: Radiomic feature analysis of lung tumors following automatic segmentation by a state-of-the-art transformer-based U-NET may predict survival and recurrence following image-guided thermal ablation of pulmonary malignancies., Level of Evidence: Level 3, Retrospective cohort study., Competing Interests: Declarations. Conflict of interest: The authors declare that they have no conflict of interest. Ethical Approval: For this type of study, formal consent is not required. Informed Consent: This study has obtained IRB approval from Brown University Health Institutional Review Board and the need for informed consent was waived. Consent for Publication: For this type of study, consent for publication is not required., (© 2024. Springer Science+Business Media, LLC, part of Springer Nature and the Cardiovascular and Interventional Radiological Society of Europe (CIRSE).)

Objective: The ACR Thyroid Imaging, Reporting, and Data System (TI-RADS) uses a score based on ultrasound (US) imaging to stratify the risk of nodule malignancy and recommend appropriate follow-up. This study aims to analyze US reports and explore how Natural Language Processing (NLP) leveraging Transformers models can classify ACR TI-RADS from text reports using the description of thyroid nodule features., Materials and Methods: This retrospective study evaluated 16,847 thyroid-free text reports from our institution. An automated system, followed by manual review by a radiologist, established baseline annotations by assigning ACR TI-RADS categories from 1 to 5. Two types of systems were evaluated and compared in the dataset. The first by performing a multiclass classification to detect the associated ACR TI-RADS, and the second by extracting thyroid nodule features from the textual reports and incorporating them into the classifier., Results: Our study showed that models enhanced with specific features systematically outperformed those without. Particularly, the BERTIN model, to which additional features were added, achieved the highest level of accuracy, with a score of 0.8426. Moreover, we found a correlation between the presence of punctate echogenic foci, a feature often linked to malignant thyroid lesions, and increased ACR TI-RADS scores., Conclusions: The features of the thyroid nodules described in thyroid US reports, such as composition, echogenicity, shape, margin or echogenic foci, help the NLP classifier to predict the associated ACR TI-RADS most accurately., (© 2024. The Author(s) under exclusive licence to Japan Radiological Society.)

Objective: To comprehensively examine the influence of auto-patient centering technologies on positioning accuracy, radiation dose, image quality, and time efficiency of computed tomography (CT) scans., Materials and Methods: A systematic search of peer-reviewed English publications was performed between January 2000 and November 2023 in PubMed, Embase, CINAHL, Scopus, and Web of Science. Two postgraduate students and an academic lecturer independently reviewed the articles to verify adherence to the inclusion criteria. The QUADAS-2 tool was employed to evaluate study quality. We derived summary estimates on positioning accuracy, radiation dose reduction, image quality, and time efficiency using proportion and meta-analysis methodologies., Results: Nine studies were identified comparing automatic and manual CT positioning. Automatic positioning improved accuracy by reducing vertical offsets to 7 mm and 4 mm for thorax and abdominal CTs, compared to 19 mm and 18 mm with manual methods. Most studies showed significant reductions in radiation dose, ranging from 5.71 to 31%. Image quality results were mixed, automatic methods generally produced images with less noise, but differences were minimal. Time efficiency was better, with automatic positioning reducing preparation time from 0.48 min versus 0.67 min for manual positioning., Conclusions: This review confirms that automatic patient-centering technologies enhance positioning accuracy and decrease preparation times in CT scans. While reductions in radiation doses and some improvements in image quality were observed, the evidence remains mixed. Findings support integrating these technologies into clinical practice to optimize patient care., Key Points: Question Does automatic patient centering in CT enhance positioning accuracy, reduce radiation exposure, and improve image quality? Findings Findings indicate that automatic centering can optimize image quality, reduce examination times and contribute to overall improvements in imaging efficiency. Clinical relevance Automatic patient centering in CT improves positioning accuracy, minimizes radiation exposure, enhances image quality, and accelerates imaging workflows, contributing to safer, more efficient imaging procedures that benefit patient care., Competing Interests: Compliance with ethical standards. Guarantor: The scientific guarantor of this publication is Professor Mark McEntee. Conflict of interest: The authors of this manuscript declare no relationships with any companies, whose products or services may be related to the subject matter of the article. Statistics and biometry: No complex statistical methods were necessary for this paper. Informed consent: Written informed consent was not required for this study because it is a systematic review and meta-analysis. Ethical approval: Institutional Review Board approval was not required because it is a systematic review and meta-analysis. Study subjects or cohorts overlap: We did not identify any direct overlap in the subjects or cohorts among the included studies. Methodology: Systematic literature review and meta-analysis, (© 2024. The Author(s), under exclusive licence to European Society of Radiology.)

Objectives: During mandibular third molar (MTM) extraction surgery, preoperative analysis to quantify the proximity of the MTM to the surrounding inferior alveolar nerve (IAN) is essential to minimize the risk of IAN injury. This study aims to propose an automated tool to quantitatively measure the proximity of IAN and MTM in cone-beam computed tomography (CBCT) images., Materials and Methods: Using the dataset including 302 CBCT scans with 546 MTMs, a deep-learning-based network was developed to support the automatic detection of the IAN, MTM, and intersection region IR. To ensure accurate proximity detection, a distance detection algorithm and a volume measurement algorithm were also developed., Results: The deep learning-based model showed encouraging segmentation accuracy of the target structures (Dice similarity coefficient: 0.9531 ± 0.0145, IAN; 0.9832 ± 0.0055, MTM; 0.8336 ± 0.0746, IR). In addition, with the application of the developed algorithms, the distance between the IAN and MTM and the volume of the IR could be equivalently detected (90% confidence interval (CI): - 0.0345-0.0014 mm, distance; - 0.0155-0.0759 mm 3 , volume). The total time for the IAN, MTM, and IR segmentation was 2.96 ± 0.11 s, while the accurate manual segmentation required 39.01 ± 5.89 min., Conclusions: This study presented a novel, fast, and accurate model for the detection and proximity quantification of the IAN and MTM on CBCT., Clinical Relevance: This model illustrates that a deep learning network may assist surgeons in evaluating the risk of MTM extraction surgery by detecting the proximity of the IAN and MTM at a quantitative level that was previously unparalleled., Competing Interests: Declarations. Ethics approval: All experimental procedures involving patient radiographic data were approved by the West China Hospital Stomatology Ethics Committee (WCHSIRB-D-2021–331) of Chengdu, Sichuan, China. Informed consent was signed by each patient and at least one parent. Informed consent (consent to participate, consent to publish): Consent for publication was signed by each patient whose data was contained in the manuscript. Conflict of interest: The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Intraoperative hemorrhage during laparoscopic hepatectomy (LH) is a risk factor for negative postoperative outcomes. Ensuring appropriate hemostasis enhances the safety of surgical procedures. An automatic bleeding recognition system based on deep learning can lead to safer surgeries; however, deep learning models that are useful for detecting and stopping bleeding in LH have not yet been reported. In this study, we aimed to develop a deep learning model to automatically recognize bleeding regions during liver transection in LH., Methods: In this retrospective feasibility study, bleeding scenes were randomly selected from LH videos, and the videos were divided into frames at 30 frames per second. Bleeding regions within the images were annotated by pixels, and subsequently, all images were assigned to the training, validation, and test datasets to develop the deep learning model. A convolutional neural network algorithm was used to perform semantic segmentation. After training and validation, the model was evaluated using images from the test dataset. Precision, recall, and Dice coefficients served as the evaluation metrics for the model., Results: In total, 2203 annotated images from 44 LH videos were utilized and divided into 1500, 400, and 303 frames for the training, validation, and test datasets, respectively. The precision, recall, and Dice coefficient values of the model were 0.76, 0.79, and 0.77, respectively., Conclusions: We developed an automatic bleeding recognition model based on semantic segmentation and verified its performance. The proposed model is potentially useful for intraoperative alerting or evaluating surgical skills in the future., Competing Interests: Declarations Disclosures Taiki Sunakawa, Daichi Kitaguchi, Shin Kobayashi, Keishiro Aoki, Manabu Kujiraoka, Kimimasa Sasaki, Lena Azuma, Atsushi Yamada, Masashi Kudo, Motokazu Sugimoto, Hiro Hasegawa, Nobuyoshi Takeshita, Naoto Gotohda, and Masaaki Ito declare that they have no financial conflicts of interest or financial ties to disclose concerning this research., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Automatic primary gross tumor volume (GTVp) segmentation for nasopharyngeal carcinoma (NPC) is a quite challenging task because of the existence of similar visual characteristics between tumors and their surroundings, especially on computed tomography (CT) images with severe low contrast resolution. Therefore, most recently proposed methods based on radiomics or deep learning (DL) is difficult to achieve good results on CT datasets., Purpose: A peritumoral radiomics-guided generative adversarial network (PRG-GAN) was proposed to address this challenge., Methods: A total of 157 NPC patients with CT images was collected and divided into training, validation, and testing cohorts of 108, 9, and 30 patients, respectively. The proposed model was based on a standard GAN consisting of a generator network and a discriminator network. Morphological dilation on the initial segmentation results from GAN was first conducted to delineate annular peritumoral region, in which radiomics features were extracted as priori guide knowledge. Then, radiomics features were fused with semantic features by the discriminator's fully connected layer to achieve the voxel-level classification and segmentation. The dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and average symmetric surface distance (ASSD) were used to evaluate the segmentation performance using a paired samples t-test with Bonferroni correction and Cohen's d (d) effect sizes. A two-sided p-value of less than 0.05 was considered statistically significant., Results: The model-generated predictions had a high overlap ratio with the ground truth. The average DSC, HD95, and ASSD were significantly improved from 0.80 ± 0.12, 4.65 ± 4.71  mm, and 1.35 ± 1.15  mm of GAN to 0.85 ± 0.18 (p = 0.001, d = 0.71), 4.15 ± 7.56 mm (p = 0.002, d = 0.67), and 1.11 ± 1.65 mm (p < 0.001, d = 0.46) of PRG-GAN, respectively., Conclusion: Integrating radiomics features into GAN is promising to solve unclear border limitations and increase the delineation accuracy of GTVp for patients with NPC., (© 2024 American Association of Physicists in Medicine.)

Accurate and automated diagnosis of focal liver lesions is critical for effective radiological practice and patient treatment planning. This study presents a deep learning model specifically developed for classifying focal liver lesions across eight different MRI sequences, categorizing them into seven distinct classes. The model includes a feature extraction module that derives multi-level representations of the lesions, a feature fusion attention module to integrate contextual information from the various sequences, and an attention-guided data augmentation module to enrich the training dataset. The proposed model achieved a patient-wise classification accuracy of 0.9302 and a lesion-wise accuracy of 0.8592, along with an F1-score of 0.8395, a recall of 0.8296, and a precision of 0.8551. These findings demonstrate the effectiveness of combining multi-sequence MRI with advanced deep learning methodologies, providing a robust tool to support radiologists in accurately classifying liver lesions in clinical settings., Competing Interests: Declarations Ethics Approval This study has been granted ethical approval by the Institutional Review Board (IRB) of The Affiliated LiHuiLi Hospital of Ningbo University, Ningbo, Zhejiang, China, under the approval number KY2022SL446-01. The study was conducted in strict compliance with the ethical standards outlined in the Declaration of Helsinki. Given the non-invasive approach employed by the research and the anonymization process applied to the data, the IRB has provided an exemption from the requirement for informed consent. Competing Interests The authors declare no competing interests., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

Objectives: To develop a deep learning-based automatic segmentation method for cortex and marrow in mandibular condyle on cone-beam computed tomography (CBCT) images and explore its clinical application., Methods: 825 condyles of 490 CBCT images from 3 centers of Stomatology hospital affliated to Zhejiang University School of Medicine were collected. A deep learning model was developed for simultaneous segmentation of cortex and marrow in mandibular condyle. It included a region of interest extraction network and a segmentation network based on 3D U-net, with modifications made to improve the segmentation boundaries. To evaluate its clinical potential, the model's segmentation efficiency and accuracy were compared with those of both junior and senior oral and maxillofacial radiologists. Additionally, the model's ability to assist junior radiologists in diagnosis through visualization and quantitative analysis of the generated 3D model was also assessed., Results: The Dice similarity coefficient of the deep learning model was 0.901 (cortex), 0.969 (marrow), and 0.982 (entire condyle). Hausdorff distance was 0.755 mm (cortex), 0.826 mm (marrow), and 0.760 mm (entire condyle). The model outperformed radiologists across all segmentation metrics, completing the task in merely 15.06 s. With the assistance of visualization and quantitative analysis generated from the model's segmentation, the diagnostic accuracy of junior radiologists significantly improved., Conclusions: The proposed deep learning-based model achieved accurate and efficient segmentation for mandibular condylar cortex and marrow. It possessed capability to generate precise 3D models, facilitating visual quantitative measurement and aiding in the diagnosis of condylar bony changes. This model holds potential for clinical applications in orthognathic surgery, orthodontic treatment, and other TMJ-related interventions., (© 2024. The Author(s) under exclusive licence to Japanese Society for Oral and Maxillofacial Radiology.)

Background: In MR-guided in-bore percutaneous needle interventions, typically 2D interactive real-time imaging is used for navigating the needle into the target. Misaligned 2D imaging planes can result in losing visibility of the needle in the 2D images, which impedes successful targeting. Necessary iterative manual slice adjustment can prolong interventional workflows. Therefore, rapid automatic alignment of the imaging planes with the needle would be preferable to improve such workflows., Purpose: To investigate rapid 3D localization of needles in MR-guided interventions via a convolutional neural network (CNN)-based localization algorithm using an undersampled white-marker contrast acquisition for the purpose of automatic imaging slice alignment., Methods: A radial 3D rf-spoiled gradient echo MR pulse sequence with white-marker encoding was implemented and a CNN-based localization algorithm was employed to extract position and orientation of an aspiration needle from the undersampled white-marker images. The CNN was trained using porcine tissue phantoms (257 needle trajectories, four-fold data augmentation, 90%/10% split into training and validation dataset). Achievable localization times and accuracy were evaluated retrospectively in an ex vivo study (109 needle trajectories) for a range of needle orientations between 78° and 90° relative to the B 0 field. A proof-of-concept in vivo experiment was performed in two porcine animal models and feasibility of automatic imaging slice alignment was evaluated retrospectively., Results: Ex vivo needle localization was achieved with a median localization accuracy of 1.9 mm (distance needle tip to detected needle axis) and a median angular deviation of 2.6° for needle orientations between 86° and 90° to the B 0 field from fully sampled WM images (resolution of (4 mm) 3 , 6434 acquired radial k-space spokes, acquisition time of 80.4 s) in a field-of-view of (256 mm) 3 . Localization accuracy decreased with increasing undersampling and needle trajectory increasingly aligned with B 0 . For needle orientations between 86° and 90° to the B 0 field, a highly accelerated acquisition of only 32 k-space spokes (acquisition time of 0.4 s) yielded a median localization accuracy of 3.1 mm and a median angular deviation of 4.7°. For needle orientations between 78° and 82° to the B 0 field, a median accuracy and angular deviation of 3.5 mm and 6.8° could still be achieved with 64 sampled spokes (acquisition time of 0.8 s). In vivo, a localization accuracy of 1.4 mm and angular deviation of 3.4° was achieved sampling 32 k-space spokes (acquisition time of 0.48 s) with the needle oriented at 87.7° to the B 0 field. For a needle oriented at 77.6° to the B 0 field, localization accuracy of 5.3 mm and angular deviation of 6.8° were still achieved sampling 128 k-space spokes (acquisition time of 1.92 s), allowing for retrospective slice alignment., Conclusion: The investigated approach enables passive biopsy needle localization in 3D. Acceleration of the localization to real-time applicability is feasible for needle orientations approximately perpendicular to B 0 . The method can potentially facilitate MR-guided needle interventions by enabling automatic imaging slice alignment with the needle., (© 2024 Siemens Healthineers AG, Cook Advanced Technologies and The Author(s). Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Objective: The accuracy of automatic tumor segmentation in PET/computed tomography (PET/CT) images is crucial for the effective treatment and monitoring of Hodgkin lymphoma. This study aims to address the challenges faced by certain segmentation algorithms in accurately differentiating lymphoma from normal organ uptakes due to PET image resolution and tumor heterogeneity., Materials and Methods: Variants of the encoder-decoder architectures are state-of-the-art models for image segmentation. Among these kinds of architectures, U-Net is one of the most famous and predominant for medical image segmentation. In this study, we propose a fully automatic approach for Hodgkin lymphoma segmentation that combines U-Net and DenseNet architectures to reduce network loss for very small lesions, which is trained using the Tversky loss function. The hypothesis is that the fusion of these two deep learning models can improve the accuracy and robustness of Hodgkin lymphoma segmentation. A dataset with 141 samples was used to train our proposed network. Also, to test and evaluate the proposed network, we allocated two separate datasets of 20 samples., Results: We achieved 0.759 as the mean Dice similarity coefficient with a median value of 0.767, and interquartile range (0.647-0.837). A good agreement was observed between the ground truth of test images against the predicted volume with precision and recall scores of 0.798 and 0.763, respectively., Conclusion: This study demonstrates that the integration of U-Net and DenseNet architectures, along with the Tversky loss function, can significantly enhance the accuracy of Hodgkin lymphoma segmentation in PET/CT images compared to similar studies., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Background: Automatic speech recognition (ASR) can potentially help older adults and people with disabilities reduce their dependence on others and increase their participation in society. However, maxillectomy patients with reduced speech intelligibility may encounter some problems using such technologies., Objectives: To investigate the accuracy of three commonly used ASR platforms when used by Japanese maxillectomy patients with and without their obturator placed., Methods: Speech samples were obtained from 29 maxillectomy patients with and without their obturator and 17 healthy volunteers. The samples were input into three speaker-independent speech recognition platforms and the transcribed text was compared with the original text to calculate the syllable error rate (SER). All participants also completed a conventional speech intelligibility test to grade their speech using Taguchi's method. A comprehensive articulation assessment of patients without their obturator was also performed., Results: Significant differences in SER were observed between healthy and maxillectomy groups. Maxillectomy patients with an obturator showed a significant negative correlation between speech intelligibility scores and SER. However, for those without an obturator, no significant correlations were observed. Furthermore, for maxillectomy patients without an obturator, significant differences were found between syllables grouped by vowels. Syllables containing /i/, /u/ and /e/ exhibited higher error rates compared to those containing /a/ and /o/. Additionally, significant differences were observed when syllables were grouped by consonant place of articulation and manner of articulation., Conclusion: The three platforms performed well for healthy volunteers and maxillectomy patients with their obturator, but the SER for maxillectomy patients without their obturator was high, rendering the platforms unusable. System improvement is needed to increase accuracy for maxillectomy patients., (© 2024 John Wiley & Sons Ltd.)

Tracheal collapse is a chronic and progressively worsening disease; the severity of clinical symptoms experienced by affected individuals depends on the degree of airway collapse. Cutting-edge automated tools are necessary to modernize disease screening using radiographs across various veterinary settings, such as animal clinics and hospitals. This is primarily due to the inherent challenges associated with interpreting uncertainties among veterinarians. In this study, an artificial intelligence model was developed to screen canine tracheal collapse using archived lateral cervicothoracic radiographs. This model can differentiate between a normal and collapsed trachea, ranging from early to severe degrees. The you-only-look-once (YOLO) models, including YOLO v3, YOLO v4, and YOLO v4 tiny, were used to train and test data sets under the in-house XXX platform. The results showed that the YOLO v4 tiny-416 model had satisfactory performance in screening among the normal trachea, grade 1-2 tracheal collapse, and grade 3-4 tracheal collapse with 98.30% sensitivity, 99.20% specificity, and 98.90% accuracy. The area under the curve of the precision-recall curve was >0.8, which demonstrated high diagnostic accuracy. The intraobserver agreement between deep learning and radiologists was κ = 0.975 (P < .001), with all observers having excellent agreement (κ = 1.00, P < .001). The intraclass correlation coefficient between observers was >0.90, which represented excellent consistency. Therefore, the deep learning model can be a useful and reliable method for effective screening and classification of the degree of tracheal collapse based on routine lateral cervicothoracic radiographs., (© 2024 American College of Veterinary Radiology.)

The presence of a normal large blood vessel (LBV) in a tumor region can impact the evaluation of quantitative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) parameters and tumor classification. Hence, there is a need for automatic removal of LBVs from brain tissues including intratumoral regions for achieving an objective assessment of tumors. This retrospective study included 103 histopathologically confirmed brain tumor patients who underwent MRI, including DCE-MRI data acquisition. Quantitative DCE-MRI analysis was performed for computing various parameters such as wash-out slope (Slope-2), relative cerebral blood volume (rCBV), relative cerebral blood flow (rCBF), blood plasma volume fraction (V p ), and volume transfer constant (K trans ). An approach based on data-clustering algorithm, morphological operations, and quantitative DCE-MRI maps was proposed for the segmentation of normal LBVs in brain tissues, including the tumor region. Here, three widely used data-clustering algorithms were evaluated on two types of quantitative maps: (a) Slope-2, and (b) a new proposed combination of rCBV and Slope-2 maps. Fluid-attenuated inversion recovery-MRI hyperintense lesions were also automatically segmented using deep learning-based architecture. The accuracy of LBV segmentation was qualitatively assessed blindly by two experienced observers, and Likert scoring was also obtained from each individual and compared using Cohen's Kappa test, and multiple statistical features from quantitative DCE-MRI parameters were obtained in the segmented tumor. t-test and receiver operating characteristic (ROC) curve analysis were performed for comparing the effect of removal of LBVs on parameters as well as on tumor grading. k-means clustering exhibited better accuracy and computational efficiency. Tumors, in particular high-grade gliomas (HGGs), showed a high contrast compared with normal tissues (relative % difference = 18.5%) on quantitative maps after the removal of LBVs. Statistical features (95 th percentile values) of all parameters in the tumor region showed a statistically significant difference (p < 0.05) between with and without LBV maps. Similar results were obtained for the ROC curve analysis for differentiation between low-grade gliomas and HGGs. Moreover, after the removal of LBVs, the rCBV, rCBF, and V p maps show better visualization of tumor regions., (© 2024 John Wiley & Sons Ltd.)

This study evaluates the accuracy of the Hipermax-BF model A7101 (Combiomed, Havana, Cuba) automatic oscillometric upper-arm sphygmomanometer for office and home use in general population as part of the HEARTS in the Americas initiative. The research was developed according to the Universal Standard AAMI/ESH/ISO ISO 81060-2:2018/Amd 1:2020. The subjects were recruited according to the requirements of age, gender, blood pressure values and upper-arm circumference. The same upper-arm sequential blood pressure measurement method was used. For measurements with the device under test, the 2-piece cuff from 22-44 cm limb circumference range was used. 92 subjects were recruited and 85 were analyzed. Mean age was 44.8 ± 14.7 years, mean upper-arm circumference was 32.3 ± 6.2, and 56.5% were female. For Validation Criterion 1, the mean value ± standard deviation of the differences in readings between the device under test and the reference device was 1.2 ± 4.9/0.8 ± 4.9 mmHg (systolic/diastolic). For both pressures, in criterion 1 the standard requires a mean value of the differences ≤ ± 5 mmHg and a standard deviation ≤ ± 8 mmHg. For Validation Criterion 2, the standard deviation of the mean blood pressure differences per subject was 4.2/4.2 mmHg (systolic/diastolic). According to Table 1 of criterion 2, for the mean values of 1.2/0.8 mmHg (systolic/diastolic), the maximum allowable standard deviation had to be < 6.84 for systolic and < 6.89 for diastolic pressure. The Combiomed Hipermax-BF A7101 automatic sphygmomanometer meets the requirements of the AAMI/ESH/ISO Universal Standard (ISO 81060-2:2018/Amd 1:2020) in the general population., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Background: Medical imaging segmentation is the use of image processing techniques to expand specific structures or areas in medical images. This technique is used to separate and display different textures or shapes in an image. The aim of this study is to develop a deep learning-based method to perform maxillary sinus segmentation using cone beam computed tomography (CBCT) images. The proposed segmentation method aims to provide better image guidance to surgeons and specialists by determining the boundaries of the maxillary sinus cavities. In this way, more accurate diagnoses can be made and surgical interventions can be performed more successfully., Methods: In the study, axial CBCT images of 100 patients (200 maxillary sinuses) were used. These images were marked to identify the maxillary sinus walls. The marked regions are masked for use in the maxillary sinus segmentation model. U-Net, one of the deep learning methods, was used for segmentation. The training process was carried out for 10 epochs and 100 iterations per epoch. The epoch and iteration numbers in which the model showed maximum success were determined using the early stopping method., Results: After the segmentation operations performed with the U-Net model trained using CBCT images, both visual and numerical results were obtained. In order to measure the performance of the U-Net model, IoU (Intersection over Union) and F1 Score metrics were used. As a result of the tests of the model, the IoU value was found to be 0.9275 and the F1 Score value was 0.9784., Conclusion: The U-Net model has shown high success in maxillary sinus segmentation. In this way, fast and highly accurate evaluations are possible, saving time by reducing the workload of clinicians and eliminating subjective errors., (© 2024. The Author(s).)

Objective: This study aimed to evaluate the utility of left atrial volume and function in uraemic patients using four-dimensional automatic left atrial quantification (4D auto LAQ) technology., Methods: Thirty-four undialysed uraemic patients (U-ND group), 60 dialysed uraemic patients (U-D group), and 32 healthy volunteers (N group) were enrolled in our current study. Conventional echocardiographic parameters were recorded, and left atrial volume and strain parameters were analysed to determine statistical differences among the three groups. The Pearson correlation coefficient was employed to assess the relationships between left atrial ejection fraction and left atrial strain parameters., Results: Compared to the N group, uraemic patients often displayed left atrial enlargement and left ventricular hypertrophy. Significant increases were noted in left atrial diameter, interventricular septum thickness, left ventricular posterior wall thickness, E/e', diastolic blood pressure, systolic blood pressure, left atrial minimum volume, left atrial maximum volume, left atrial pre-atrial contraction volume, left atrial emptying volume and left atrial maximum volume index (P < 0.05). Conversely, the e', E/A ratio and left atrial reservoir longitudinal strain were significantly decreased (P < 0.05). However, no statistically significant differences were observed in the aforementioned parameters between the U-ND and U-D groups. The absolute values of left atrial conduit longitudinal strain and left atrial conduit circumferential strain, as well as left atrial passive ejection fraction, were notably lower in the U-D group compared to the N and U-ND groups, with statistically significant differences identified among the three groups (P < 0.05)., Conclusions: Uraemic patients exhibit marked left atrial enlargement and left ventricular hypertrophy, coupled with altered atrial function, particularly ductal dysfunction in the U-D group. The 4D auto LAQ technology proves advantageous in detecting these alterations, offering a promising tool for thorough cardiac assessment in this patient cohort., (© 2024 The Author(s). ESC Heart Failure published by John Wiley & Sons Ltd on behalf of European Society of Cardiology.)

Background: Accurate preoperative prediction of cervical lymph node metastasis (LNM) for papillary thyroid carcinoma (PTC) patients is essential for disease staging and individualized treatment planning, which can improve prognosis and facilitate better management., Purpose: To establish a fully automated deep learning-enabled model (FADLM) for automated tumor segmentation and cervical LNM prediction in PTC using ultrasound (US) video keyframes., Methods: The bicentral study retrospective enrolled 518 PTC patients, who were then randomly divided into the training (Hospital 1, n = 340), internal test (Hospital 1, n = 83), and external test cohorts (Hospital 2, n = 95). The FADLM integrated mask region-based convolutional neural network (Mask R-CNN) for automatic thyroid primary tumor segmentation and ResNet34 with Bayes strategy for cervical LNM diagnosis. A radiomics model (RM) using the same automated segmentation method, a traditional radiomics model (TRM) using manual segmentation, and a clinical-semantic model (CSM) were developed for comparison. The dice similarity coefficient (DSC) was used to evaluate segmentation performance. The prediction performance of the models was validated in terms of discrimination and clinical utility with the area under the receiver operator characteristic curve (AUC), heatmap analysis, and decision curve analysis (DCA). The comparison of the predictive performance among different models was conducted by DeLong test. The performances of two radiologists compared with FADLM and the diagnostic augmentation with FADLM's assistance were analyzed in terms of accuracy, sensitivity and specificity using McNemar's x 2 test. The p-value less than 0.05 was defined as a statistically significant difference. The Benjamini-Hochberg procedure was applied for multiple comparisons to deal with Type I error., Results: The FADLM yielded promising segmentation results in training (DSC: 0.88 ± 0.23), internal test (DSC: 0.88 ± 0.23), and external test cohorts (DSC: 0.85 ± 0.24). The AUCs of FADLM for cervical LNM prediction were 0.78 (95% CI: 0.73, 0.83), 0.83 (95% CI: 0.74, 0.92), and 0.83 (95% CI: 0.75, 0.92), respectively. It all significantly outperformed the RM (AUCs: 0.78 vs. 0.72; 0.83 vs. 0.65; 0.83 vs. 0.68, all adjusted p-values < 0.05) and CSM (AUCs: 0.78 vs. 0.71; 0.83 vs. 0.62; 0.83 vs. 0.68, all adjusted p-values < 0.05) across the three cohorts. The RM offered similar performance to that of TRM (AUCs: 0.61 vs. 0.63, adjusted p-value = 0.60) while significantly reducing the segmentation time (3.3 ± 3.8 vs. 14.1 ± 4.2 s, p-value < 0.001). Under the assistance of FADLM, the accuracies of junior and senior radiologists were improved by 18% and 15% (all adjusted p-values < 0.05) and the sensitivities by 25% and 21% (all adjusted p-values < 0.05) in the external test cohort., Conclusion: The FADLM with elaborately designed automated strategy using US video keyframes holds good potential to provide an efficient and consistent prediction of cervical LNM in PTC. The FADLM displays superior performance to RM, CSM, and radiologists with promising efficacy., (© 2024 American Association of Physicists in Medicine.)

Objectives: Introducing SPINEPS, a deep learning method for semantic and instance segmentation of 14 spinal structures (ten vertebra substructures, intervertebral discs, spinal cord, spinal canal, and sacrum) in whole-body sagittal T2-weighted turbo spin echo images., Material and Methods: This local ethics committee-approved study utilized a public dataset (train/test 179/39 subjects, 137 female), a German National Cohort (NAKO) subset (train/test 1412/65 subjects, mean age 53, 694 female), and an in-house dataset (test 10 subjects, mean age 70, 5 female). SPINEPS is a semantic segmentation model, followed by a sliding window approach utilizing a second model to create instance masks from the semantic ones. Segmentation evaluation metrics included the Dice score and average symmetrical surface distance (ASSD). Statistical significance was assessed using the Wilcoxon signed-rank test., Results: On the public dataset, SPINEPS outperformed a nnUNet baseline on every structure and metric (e.g., an average over vertebra instances: dice 0.933 vs 0.911, p < 0.001, ASSD 0.21 vs 0.435, p < 0.001). SPINEPS trained on automated annotations of the NAKO achieves an average global Dice score of 0.918 on the combined NAKO and in-house test split. Adding the training data from the public dataset outperforms this (average instance-wise Dice score over the vertebra substructures 0.803 vs 0.778, average global Dice score 0.931 vs 0.918)., Conclusion: SPINEPS offers segmentation of 14 spinal structures in T2w sagittal images. It provides a semantic mask and an instance mask separating the vertebrae and intervertebral discs. This is the first publicly available algorithm to enable this segmentation., Key Points: Question No publicly available automatic approach can yield semantic and instance segmentation masks for the whole spine (including posterior elements) in T2-weighted sagittal TSE images. Findings Segmenting semantically first and then instance-wise outperforms a baseline trained directly on instance segmentation. The developed model produces high-resolution MRI segmentations for the whole spine. Clinical relevance This study introduces an automatic approach to whole spine segmentation, including posterior elements, in arbitrary fields of view T2w sagittal MR images, enabling easy biomarker extraction, automatic localization of pathologies and degenerative diseases, and quantifying analyses as downstream research., (© 2024. The Author(s).)

Background: Periodontics and gingivitis are two of the most widely prevalent illnesses that affect people nowadays. The sixth most common disease in the world is periodontitis, and detecting periodontal bone loss is essential in the earlier condition and is crucial for the development of the proper diagnosis. Early bone loss detection can be assisted by using computer-assisted radiography examination. Understanding disease progression helps to select the most effective treatment action., Objectives: An effective deep model is suggested to detect periodontal bone loss at an earlier stage for preventing the progression of Periodontics bone loss., Methods: This work is intimated by collecting images from online resources. Further, the images gathered from the dataset are preceded by the tooth segmentation which is done using DenseUNet +  + . Further, the segmented images are given to the Adaptive DenseNet with Gated Recurrent Unit (AD-GRU) for detecting periodontal bone loss and this diagnosis is used for the periodontitis stage, where the ADGRU performance is augmented by optimizing the attributes using the Refined Red Kite Optimization Algorithm (RRKOA)., Results: The offered approach attained an accuracy of 94.45% which is higher than the88.63%, 90.58%, 89.54%, and 92.96% attained by the LSTM, DenseNet, GRU, DenseNet-GRU., Data Conclusion: The findings of the simulation proved the designed framework outperformed the traditional model with high accuracy., Clinical Relevance: The developed effectual deep model-based periodontal bone loss and stage periodontitis diagnosis structure is used in healthcare applications., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Coronary artery calcium (CAC) scoring CT is a useful tool for screening coronary artery disease and for cardiovascular risk stratification. However, its efficacy in patients with coronary stents, who had pre-existing coronary artery disease, remains uncertain. Historically, CAC CT scans of these patients have been manually excluded from the CAC scoring process, even though most of the CAC scoring process is now fully automated. Therefore, we hypothesized that automating the filtering of patients with coronary stents using artificial intelligence could streamline the entire CAC workflow, eliminating the need for manual intervention. Consequently, we aimed to develop and evaluate a deep learning-based coronary stent filtering algorithm (StentFilter) in CAC scoring CT scans using a multicenter CAC dataset. We developed StentFilter comprising two main processes: stent identification and false-positive reduction. Development utilized 108 non-enhanced echocardiography-gated CAC scans (including 74 with manually labeled stents), and for false positive reduction, 2063 CAC scans with significant coronary calcium (average Agatston score: 523.8) but no stents were utilized. StentFilter's performance was evaluated on two independent internal test sets (Asan cohort- and 2; n = 355 and 396; one without coronary stents) and two external test sets from different institutions (n = 105 and 62), each with manually labeled stents. We calculated the per-patient sensitivity, specificity, and false-positive rate of StentFilter. StentFilter demonstrated a high overall per-patient sensitivity of 98.8% (511/517 cases with stents) and a false-positive rate of 0.022 (20/918). Notably, the false-positive ratio was significantly lower in the dataset containing stents (Asan cohort-1; 0.008 [3/355]) compared with the dataset without stents (Asan cohort-2; 0.043 [17/396], p = 0.008). All false-positive identifications were attributed to dense coronary calcifications, with no false positives identified in extracoronary locations. The automated StentFilter accurately distinguished coronary stents from pre-existing coronary calcifications. This approach holds potential as a filter within a fully automated CAC scoring workflow, streamlining the process efficiently., (© 2024. The Author(s).)

State-of-the-art automatic sleep staging methods have demonstrated comparable reliability and superior time efficiency to manual sleep staging. However, fully automatic black-box solutions are difficult to adapt into clinical workflow due to the lack of transparency in decision-making processes. Transparency would be crucial for interaction between automatic methods and the work of sleep experts, i.e., in human-in-the-loop applications. To address these challenges, we propose an automatic sleep staging model (aSAGA) that effectively utilises both electroencephalography and electro-oculography channels while incorporating transparency of uncertainty in the decision-making process. We validated the model through extensive retrospective testing using a range of datasets, including open-access, clinical, and research-driven sources. Our channel-wise ensemble model, trained on both electroencephalography and electro-oculography signals, demonstrated robustness and the ability to generalise across various types of sleep recordings, including novel self-applied home polysomnography. Additionally, we compared model uncertainty with human uncertainty in sleep staging and studied various uncertainty mapping metrics to identify ambiguous regions, or "grey areas", that may require manual re-evaluation. The validation of this grey area concept revealed its potential to enhance sleep staging accuracy and to highlight regions in the recordings where sleep experts may struggle to reach a consensus. In conclusion, this study provides a technical basis and understanding of automatic sleep staging uncertainty. Our approach has the potential to improve the integration of automatic sleep staging into clinical practice; however, further studies are needed to test the model prospectively in real-world clinical settings and human-in-the-loop scoring applications., (© 2024 The Author(s). Journal of Sleep Research published by John Wiley & Sons Ltd on behalf of European Sleep Research Society.)

Aim: To develop an automated model for subfoveal choroidal thickness (SFCT) detection in optical coherence tomography (OCT) images, addressing manual fovea location and choroidal contour challenges., Methods: Two procedures were proposed: defining the fovea and segmenting the choroid. Fovea localization from B-scan OCT image sequence with three-dimensional reconstruction (LocBscan-3D) predicted fovea location using central foveal depression features, and fovea localization from two-dimensional en-face OCT (LocEN-2D) used a mask region-based convolutional neural network (Mask R-CNN) model for optic disc detection, and determined the fovea location based on optic disc relative position. Choroid segmentation also employed Mask R-CNN., Results: For 53 eyes in 28 healthy subjects, LocBscan-3D's mean difference between manual and predicted fovea locations was 170.0 µm, LocEN-2D yielded 675.9 µm. LocEN-2D performed better in non-high myopia group ( P =0.02). SFCT measurements from Mask R-CNN aligned with manual values., Conclusion: Our models accurately predict SFCT in OCT images. LocBscan-3D excels in precise fovea localization even with high myopia. LocEN-2D shows high detection rates but lower accuracy especially in the high myopia group. Combining both models offers a robust SFCT assessment approach, promising efficiency and accuracy for large-scale studies and clinical use., Competing Interests: Conflicts of Interest: Lin CY, None; Chen HJ, None; Chan YK, None; Hsia WP, None; Huang YL, None; Chang CJ, None., (International Journal of Ophthalmology Press.)

Background: Cervical cancer is fourth the most common cancer in women worldwide. Due to the prevalence of human papillomavirus (HPV) in the population (80-90%), scientists are likely to discover even more associations of this pathogen with other diseases in the future. In recent years, In Situ Hybridization (ISH) assays that use automated signal-detecting methods in formalin-fixed, paraffin-embedded (FFPE) cervical tissue, such as the enzyme-categorized signal-detecting system, have shown a higher sensitivity. Objectives and Methods: To evaluate automatic signal detection of ISH assay for detecting HPV DNA, we compared the ability of an ISH probe, Inform HPV II and III (Ventana Medical Systems, Tucson, AZ), to that of PCR assays to detect HPV DNA in cervical tissue specimens with cervical intraepithelial neoplasia (CIN; CIN 1, 28 cases; CIN 2, 22 cases; and CIN 3, 20 cases) and normal cervix (2 cases). Results: Our findings showed a significant relation was confirmed between ISH III level and HPV outcome (positive/negative). Patients with positive HPV outcomes had significantly lower ISH III levels, MD = -7961.82 CI 95 [-17,230.00; -199.21], p = 0.005. Conclusions: Automatic signal detection of ISH assay is not particularly applicable to cervical tissue material. A more useful method of confirming the presence of HPV in the cervix is the HPV test with genotyping, as it allows for collecting a larger amount of material from the cervical disc and canal. The interpretation of a positive or negative ISH test must be guided in the context of clinical history and morphology.

Purpose: To explore the accuracy and feasibility of a proposed deep learning (DL) algorithm for clinical target volume (CTV) delineation in cervical cancer radiotherapy and evaluate whether it can perform well in external cervical cancer and endometrial cancer cases for generalization validation., Methods: A total of 332 patients were enrolled in this study. A state-of-the-art network called ResCANet, which added the cascade multi-scale convolution in the skip connections to eliminate semantic differences between different feature layers based on ResNet-UNet. The atrous spatial pyramid pooling in the deepest feature layer combined the semantic information of different receptive fields without losing information. A total of 236 cervical cancer cases were randomly grouped into 5-fold cross-training (n = 189) and validation (n = 47) cohorts. External validations were performed in a separate cohort of 54 cervical cancer and 42 endometrial cancer cases. The performances of the proposed network were evaluated by dice similarity coefficient (DSC), sensitivity (SEN), positive predictive value (PPV), 95% Hausdorff distance (95HD), and oncologist clinical score when comparing them with manual delineation in validation cohorts., Results: In internal validation cohorts, the mean DSC, SEN, PPV, 95HD for ResCANet achieved 74.8%, 81.5%, 73.5%, and 10.5 mm. In external independent validation cohorts, ResCANet achieved 73.4%, 72.9%, 75.3%, 12.5 mm for cervical cancer cases and 77.1%, 81.1%, 75.5%, 10.3 mm for endometrial cancer cases, respectively. The clinical assessment score showed that minor and no revisions (delineation time was shortened to within 30 min) accounted for about 85% of all cases in DL-aided automatic delineation., Conclusions: We demonstrated the problem of model generalizability for DL-based automatic delineation. The proposed network can improve the performance of automatic delineation for cervical cancer and shorten manual delineation time at no expense to quality. The network showed excellent clinical viability, which can also be even generalized for endometrial cancer with excellent performance., (© 2024 The Author(s). Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Background: Computer-assisted planning methods have increasingly contributed to preoperative ablation planning; however, these methods cannot automatically obtain the final optimal solution within a short time and are rarely validated in practice, greatly limiting their clinical applicability., Purpose: We aimed to propose a full-automatic multi-stage ablation and needle trajectory planning method for CT-guided percutaneous liver ablation to attain the final optimal plans under multiple clinical constraints rapidly., Methods: Our proposed method integrates the ablation zone planning fulfilling complete tumor coverage and critical structure avoidance while reaching a trade-off between ablation number and healthy tissue damage, and needle trajectory planning under multiple clinical constraints. Our needle trajectory planning determines feasible skin entry regions based on hard constraints, where the multi-objective optimization (MOO) considering soft constraints is performed using the Pareto Optimality and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methods for the final optimal solution. The performance of our proposed method was evaluated on 30 tumors of various characteristics from 23 patients and clinically validated in five clinical cases., Results: Our proposed method achieved 99.8% treatment zone coverage and 40.5% ablation efficiency without involving critical structures, and completely satisfied multiple clinical constraints in all needle trajectory planning results. The average planning time was 23.6 s for tumors of different sizes. All the plans were considered clinically acceptable by the doctors' evaluation. Our method achieved complete tumor coverage without complications in clinical case validation., Conclusion: Our proposed planning method can generate a final optimal plan satisfying multiple clinical constraints within a short time, potentially facilitating preoperative planning for hepatic tumor ablation., (© 2024 The Author(s). Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

This study aimed to develop an automatic methodology for mandibular landmarking and measurement using non-rigid registration as well as analyze the accuracy of automatic landmarking and measurements., Statistical analysis., Digital technology center, tertiary hospital., 130 healthy Chinese adults with equal gender distribution, average age 28.2 ± 5.6 years., Four mean shape mesh templates were generated from 100 head CT scans. Following manual indication of landmarks, these templates were applied for automatic landmark annotation and measurements on mandibles from another 30 head CT scans, using non-rigid iterative closest point registration., Differences of landmark coordinates and measurements between automatic and manual annotation were analyzed using mean difference, centroid size, Euclidean distances and intraclass correlation coefficient (ICC), assessing the accuracy and validity of automatic landmark annotation., The majority of automatic landmarks (16/22) did not exhibit consistent displacement to specific direction. ICCs of all landmark coordinates exceed 0.950, with 87.9% larger than 0.990. The average Euclidean distance between manual and automatic landmarks was 2.038 ± 0.947 mm. Most ICCs of linear and angular measurements between manual and automatic annotation (20/26) exceeded 0.900, with the average errors being 1.425 ± 0.973 mm and 2.257 ± 0.649 °, respectively., A novel and efficient method for automatic landmark annotation was established based on non-rigid registration. Its credibility and accuracy in mandibular annotation and measurements were demonstrated., Competing Interests: Declaration of Conflicting InterestsThe authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: Fluoroscopy guided interventions (FGIs) pose a risk of prolonged radiation exposure; personalized patient dosimetry is necessary to improve patient safety during these procedures. However, current FGIs systems do not capture the precise exposure regions of the patient, making it challenging to perform patient-procedure-specific dosimetry. Thus, there is a pressing need to develop approaches to extract and use this information to enable personalized radiation dosimetry for interventional procedures., Purpose: To propose a deep learning (DL) approach for the automatic localization of 3D anatomical landmarks on randomly collimated and magnified 2D head fluoroscopy images., Materials and Methods: The model was developed with datasets comprising 800 000 pseudo 2D synthetic images (mixture of vessel-enhanced and non-enhancement), each with 55 annotated anatomical landmarks (two are landmarks for eye lenses), generated from 135 retrospectively collected head computed tomography (CT) volumetric data. Before training, dynamic random cropping was performed to mimic the varied field-size collimation in FGI procedures. Gaussian-distributed additive noise was applied to each individual image to enhance the robustness of the DL model in handling image degradation that may occur during clinical image acquisition in a clinical environment. The model was trained with 629 370 synthetic images for approximately 275 000 iterations and evaluated against a synthetic image test set and a clinical fluoroscopy test set., Results: The model shows good performance in estimating in- and out-of-image landmark positions and shows feasibility to instantiate the skull shape. The model successfully detected 96.4% and 92.5% 2D and 3D landmarks, respectively, within a 10 mm error on synthetic test images. It demonstrated an average of 3.6 ± 2.3 mm mean radial error and successfully detected 96.8% 2D landmarks within 10 mm error on clinical fluoroscopy images., Conclusion: Our deep-learning model successfully localizes anatomical landmarks and estimates the gross shape of skull structures from collimated 2D projection views. This method may help identify the exposure region required for patient-specific organ dosimetry in FGIs procedures., (© 2024 American Association of Physicists in Medicine.)

Background: Automatic segmentation of vertebrae in spinal x-ray images is crucial for clinical diagnosis, case analysis, and surgical planning of spinal lesions., Purpose: However, due to the inherent characteristics of x-ray images, including low contrast, high noise, and uneven grey scale, it remains a critical and challenging problem in computer-aided spine image analysis and disease diagnosis applications., Methods: In this paper, a Multiscale Feature Enhancement Network (MFENet), is proposed for segmenting whole spinal x-ray images, to aid doctors in diagnosing spinal-related diseases. To enhance feature extraction, the network incorporates a Dual-branch Feature Extraction Module (DFEM) and a Semantic Aggregation Module (SAM). The DFEM has a parallel dual-branch structure. The upper branch utilizes multiscale convolutional kernels to extract features from images. Employing convolutional kernels of different sizes helps capture details and structural information at different scales. The lower branch incorporates attention mechanisms to further optimize feature representation. By modeling the feature maps spatially and across channels, the network becomes more focused on key feature regions and suppresses task-irrelevant information. The SAM leverages contextual semantic information to compensate for details lost during pooling and convolution operations. It integrates high-level feature information from different scales to reduce segmentation result discontinuity. In addition, a hybrid loss function is employed to enhance the network's feature extraction capability., Results: In this study, we conducted a multitude of experiments utilizing dataset provided by the Spine Surgery Department of Henan Provincial People's Hospital. The experimental results indicate that our proposed MFENet demonstrates superior segmentation performance in spinal segmentation on x-ray images compared to other advanced methods, achieving 92.61 ± 0.431 for MIoU, 92.42 ± 0.329 for DSC, and 99.51 ± 0.037 for Global&#95;accuracy., Conclusions: Our model is able to more effectively learn and extract global contextual semantic information, significantly improving spinal segmentation performance, further aiding doctors in analyzing patient conditions., (© 2024 American Association of Physicists in Medicine.)

Background: X-ray radiography is a widely used imaging technique worldwide, and its image quality directly affects diagnostic accuracy. Therefore, X-ray image quality control (QC) is essential. However, subjectively assessing image quality is inefficient and inconsistent, especially when large amounts of image data are being evaluated. Thus, subjective assessment cannot meet current QC needs., Purpose: To meet current QC needs and improve the efficiency of image quality assessment, a complete set of quality assessment criteria must be established and implemented using artificial intelligence (AI) technology. Therefore, we proposed a multi-criteria AI system for automatically assessing the image quality of knee radiographs., Methods: A knee radiograph QC knowledge graph containing 16 "acquisition technique" labels representing 16 image quality defects and five "clarity" labels representing five grades of clarity were developed. Ten radiographic technologists conducted three rounds of QC based on this graph. The single-person QC results were denoted as QC1 and QC2, and the multi-person QC results were denoted as QC3. Each technologist labeled each image only once. The ResNet model structure was then used to simultaneously perform classification (detection of image quality defects) and regression (output of a clarity score) tasks to construct an image QC system. The QC3 results, comprising 4324 anteroposterior and lateral knee radiographs, were used for model training (70% of the images), validation (10%), and testing (20%). The 865 test set data were used to evaluate the effectiveness of the AI model, and an AI QC result, QC4, was automatically generated by the model after training. Finally, using a double-blind method, the senior QC expert reviewed the final QC results of the test set with reference to the results QC3 and QC4 and used them as a reference standard to evaluate the performance of the model. The precision and mean absolute error (MAE) were used to evaluate the quality of all the labels in relation to the reference standard., Results: For the 16 "acquisition technique" features, QC4 exhibited the highest weighted average precision (98.42% ± 0.81%), followed by QC3 (91.39% ± 1.35%), QC2 (87.84% ± 1.68%), and QC1 (87.35% ± 1.71%). For the image clarity features, the MAEs between QC1, QC2, QC3, and QC4 and the reference standard were 0.508 ± 0.021, 0.475 ± 0.019, 0.237 ± 0.016, and 0.303 ± 0.018, respectively., Conclusions: The experimental results show that our automated quality assessment system performed well in classifying the acquisition technique used for knee radiographs. The image clarity quality evaluation accuracy of the model must be further improved but is generally close to that of radiographic technologists. Intelligent QC methods using knowledge graphs and convolutional neural networks have the potential for clinical applications., (© 2024 American Association of Physicists in Medicine.)

Background: Despite the widespread implementation of analytical hardware capable of recording large-scale datasets for botanical natural products, the data processing procedures for compound annotation remain a bothersome obstacle that demand a tremendous amount of time and expert knowledge., Methods: Herein, an automatic LC-MS/MS data analysis workflow with AutoAnnotatoR was introduced for the compound annotation of plant derived natural products, which has the merits of great efficiency, high accuracy, saving time and simplified process. This procedure enabled automatic matching of MS 2 data with characteristic fragment ions, as well as MS 1 data with compound libraries, which improves the accuracy of structural elucidation. Notably, the optimization of collision energy for each target ion was successfully performed for the first time, facilitating the acquisition of comprehensive fragmentation information., Results: The automatic analysis workflow with AutoAnnotatoR was successfully applied for the annotation of alkaloids from 10 botanical origins of Fritillaria species. Consequently, a total of 2684 chemical constituents were tentatively characterized, with 23 components being unambiguously validated by reference standards and 2434 being probable novel chemicals., Conclusion: The entire data analysis procedure takes only a few hours, vastly improving analysis speed while assuring high accuracy. This method provides a powerful tool for the rapid and precise annotation of complex natural products. The workflow is publicly accessible on Github as an open-source R package called AutoAnnotatoR (https://github.com/anyaling2022/AutoAnnotatoR)., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier GmbH.)

Background: Automatic High Tibial Osteotomy (HTO) landmark detection methods promise to improve the effectiveness and standardisation of HTO preoperative planning. Unfortunately, due to the limited number of HTO datasets, existing methods are less robust when dealing with patients with varied deformities than traditional manual planning, severely limiting their clinical viability and application in practical surgical settings., Methods: Here, we present a new HTO landmark detection framework using an integration of optimised heatmap-offset aggregation method and traditional feature extraction. Subjective and objective approaches were employed to reflect the final clinical acceptance of our model., Results: Average Mean Absolute Error of prediction results compared to the surgeon's gold standard was 0.35° for the hip-knee-ankle angle. The objective score rated by surgeons reached 4.4 on a scale of 5., Conclusion: The study demonstrated that the automatic detection method has great potential serving as an alternative to manual radiological analysis in practical surgical pre-operative planning., (© 2024 John Wiley & Sons Ltd.)

Introduction: This study investigated the effects of automatic dual rotational Risley prisms (ADRRPs), a mobile phone application-operated device, on vergence abilities in young adults., Methods: Fifty-six participants aged 20-24 performed vergence exercises. The test group used prisms with power changes from 30Δ base-out to 10Δ base-in, while the control group used plano lenses for 10 min. Ophthalmic examinations included lateral heterophoria, near point of convergence (NPC), vergence facility (VF), negative fusional vergence (NFV), and positive fusional vergence (PFV), all measured before and after the vergence exercises. Pre- and post-test results were analyzed using a paired sample t test. Additionally, three cases with convergence insufficiency (CI) performed similar exercises for 12 weeks., Results: Participants were divided into the test group (n = 39; age 21.82 ± 1.10 years) and control group (n = 17; age 20.53 ± 0.51 years). In the test group, NPC improved from 6.11 ± 2.52 cm to 5.77 ± 2.30 cm (p = 0.023). VF increased from 13.75 ± 4.10 cpm to 16.50 ± 4.42 cpm (p = 0.007). PFV at 6 m and 0.4 m increased from 19.49 ± 6.77∆ to 22.19 ± 6.64∆ (p < 0.001) and 20.51 ± 7.05∆ to 22.69 ± 6.44∆ (p = 0.012), respectively. After 12 weeks, convergence insufficiency symptom survey scores for cases with CI decreased significantly, with NPC improving from 7.0 to 0 cm, 6.0 to 5.8 cm, and 6.0 to 4.7 cm. PFV increased from 10 to 25∆, 20 to 30∆, and 25 to 50∆., Conclusion: This preliminary study showed the effect of ADRRPs on improving vergence abilities. Further studies are needed to investigate the long-term sustainability and effects in a larger population of individuals with CI of this approach., Competing Interests: Declarations Conflict of Interest A Taiwan invention patent (I781072) has been registered for the automatic dual rotational Risley prisms system. Ya-Yu Chen, Yun-Shao Hu, Shuan-Yu Huang and Tzu-Hsun Tsai declare that they have no competing interests. Ethical Approval This experimental study was approved by the Institutional Review Board of Chung Shan Medical University Hospital in Taichung, Taiwan (approval number CS2-22104) and National Taiwan University Hospital in Taipei, Taiwan (approval number 202207058RINB). Written informed consent was obtained from all patients. The permission for use the participant’s photo in this study was also obtained (Fig. 2D)., (© 2024. The Author(s).)

Objective: A recent International Organization for Standardization (ISO) Task Group report calls for research to investingate potential special populations in validation studies of automated blood pressure (BP) devices. Accordingly, we aimed to determine the accuracy of two previously validated BP monitors passed in a general population when measured in adults with a mid-upper arm circumference (MUAC) ≤ 22 cm., Methods: Test device A was the OMRON HEM 7121 equipped with the HEM CS24 cuff designed for an arm circumference of 17-22 cm. Test device B was the YuWell YE660Е with the YuWell '360°' cuff (18-36 cm). Data from 37 participants aged 20.14 ± 2.23 (18-28) years were analyzed according to criterion 1 of ISO Standard 81060-2 : 2018., Results: According to criterion 1, the mean ± SD of the BP differences for the device A was -7.81 ± 5.20/-10.66 ± 5.48 mmHg (systolic/diastolic) and for the device B was -8.00 ± 6.30/-16.11 ± 5.15 mmHg (systolic/diastolic), respectively. This means that neither device met the requirements., Conclusion: Since devices A and B, which had passed in a general population study, failed in adults aged 18-28 years with an MUAC ≤ 22 cm, such individuals might be considered as new special population for validation studies., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Background: To establish the automatic soft-tissue analysis model based on deep learning that performs landmark detection and measurement calculations on orthodontic facial photographs to achieve a more comprehensive quantitative evaluation of soft tissues., Methods: A total of 578 frontal photographs and 450 lateral photographs of orthodontic patients were collected to construct datasets. All images were manually annotated by two orthodontists with 43 frontal-image landmarks and 17 lateral-image landmarks. Automatic landmark detection models were established, which consisted of a high-resolution network, a feature fusion module based on depthwise separable convolution, and a prediction model based on pixel shuffle. Ten measurements for frontal images and eight measurements for lateral images were defined. Test sets were used to evaluate the model performance, respectively. The mean radial error of landmarks and measurement error were calculated and statistically analysed to evaluate their reliability., Results: The mean radial error was 14.44 ± 17.20 pixels for the landmarks in the frontal images and 13.48 ± 17.12 pixels for the landmarks in the lateral images. There was no statistically significant difference between the model prediction and manual annotation measurements except for the mid facial-lower facial height index. A total of 14 measurements had a high consistency., Conclusion: Based on deep learning, we established automatic soft-tissue analysis models for orthodontic facial photographs that can automatically detect 43 frontal-image landmarks and 17 lateral-image landmarks while performing comprehensive soft-tissue measurements. The models can assist orthodontists in efficient and accurate quantitative soft-tissue evaluation for clinical application., (© 2024 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

To investigate whether automatic segmentation based on DCE-MRI with a deep learning (DL) algorithm enabled advantages over manual segmentation in differentiating BI-RADS 4 breast lesions. A total of 197 patients with suspicious breast lesions from two medical centers were enrolled in this study. Patients treated at the First Hospital of Qinhuangdao between January 2018 and April 2024 were included as the training set (n = 138). Patients treated at Lanzhou University Second Hospital were assigned to an external validation set (n = 59). Areas of suspicious lesions were delineated based on DL automatic segmentation and manual segmentation, and evaluated consistency through the Dice correlation coefficient. Radiomics models were constructed based on DL and manual segmentations to predict the nature of BI-RADS 4 lesions. Meanwhile, the nature of the lesions was evaluated by both a professional radiologist and a non-professional radiologist. Finally, the area under the curve value (AUC) and accuracy (ACC) were used to determine which prediction model was more effective. Sixty-four malignant cases (32.5%) and 133 benign cases (67.5%) were included in this study. The DL-based automatic segmentation model showed high consistency with manual segmentation, achieving a Dice coefficient of 0.84 ± 0.11. The DL-based radiomics model demonstrated superior predictive performance compared to professional radiologists, with an AUC of 0.85 (95% CI 0.79-0.92). The DL model significantly reduced working time and improved efficiency by 83.2% compared to manual segmentation, further demonstrating its feasibility for clinical applications. The DL-based radiomics model for automatic segmentation outperformed professional radiologists in distinguishing between benign and malignant lesions in BI-RADS category 4, thereby helping to avoid unnecessary biopsies. This groundbreaking progress suggests that the DL model is expected to be widely applied in clinical practice in the near future, providing an effective auxiliary tool for the diagnosis and treatment of breast cancer., Competing Interests: Declarations. Ethics Approval: This study was performed in line with the principles of the Declaration of Helsinki. Approval was granted by the Ethics Committee of First Hospital of Qinhuangdao (2024K006). Consent to Participate: Informed consent was obtained from all individual participants included in the study. Competing Interests: The authors declare no competing interests., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

A 96-well microplate-based protein microarray chip which can simultaneously detect three different families of antibiotics using a multiplexed approach has been developed for the first time. All steps in the microarray chip can be completed through an automated biochip analyzer, enabling high throughput analysis of 96 samples within 60 min according to the set program. The microarray chip can be visually evaluated by the color depth of array points, and can also be quantified through a scanner. Under the optimal conditions, the limits of detection (LODs) of nitrofuran metabolites (NFMs), chloramphenicol (CAP), and fluoroquinolones (FQs) were 0.11-0.15 μg/kg, 0.07 μg/kg and 6.42 μg/kg, respectively. This microarray chip can be applied to different aquatic products, including crucian carp, shrimp, and scallops, with the recovery ranged from 89.4 % to 111.2 %. These results demonstrate new strategies for the high-throughput detection of multi-antibiotic residues and present potential of protein chip technologies for daily supervision., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

The rapid emergence of large language model (LLM) technology presents promising opportunities to facilitate the development of synthetic reactions. In this work, we leveraged the power of GPT-4 to build an LLM-based reaction development framework (LLM-RDF) to handle fundamental tasks involved throughout the chemical synthesis development. LLM-RDF comprises six specialized LLM-based agents, including Literature Scouter, Experiment Designer, Hardware Executor, Spectrum Analyzer, Separation Instructor, and Result Interpreter, which are pre-prompted to accomplish the designated tasks. A web application with LLM-RDF as the backend was built to allow chemist users to interact with automated experimental platforms and analyze results via natural language, thus, eliminating the need for coding skills and ensuring accessibility for all chemists. We demonstrated the capabilities of LLM-RDF in guiding the end-to-end synthesis development process for the copper/TEMPO catalyzed aerobic alcohol oxidation to aldehyde reaction, including literature search and information extraction, substrate scope and condition screening, reaction kinetics study, reaction condition optimization, reaction scale-up and product purification. Furthermore, LLM-RDF's broader applicability and versability was validated on various synthesis tasks of three distinct reactions (S N Ar reaction, photoredox C-C cross-coupling reaction, and heterogeneous photoelectrochemical reaction)., Competing Interests: Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Automating the segmentation of nasopharyngeal carcinoma (NPC) is crucial for therapeutic procedures but presents challenges given the hurdles in amassing extensively annotated datasets. Although previous studies have applied self-supervised learning to capitalize on unlabeled data to improve segmentation performance, these methods often overlooked the benefits of dual-sequence magnetic resonance imaging (MRI). In the present study, we incorporated self-supervised learning with a saliency transformation module using unlabeled dual-sequence MRI for accurate NPC segmentation. 44 labeled and 72 unlabeled patients were collected to develop and evaluate our network. Impressively, our network achieved a mean Dice similarity coefficient (DSC) of 0.77, which is consistent with a previous study that relied on a training set of 4,100 annotated cases. The results further revealed that our approach required minimal adjustments, primarily < 20% tweak in the DSC, to meet clinical standards. By enhancing the automatic segmentation of NPC, our method alleviates the annotation burden on oncologists, curbs subjectivity, and ensures reliable NPC delineation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)