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Current technologies as correlation analysis, regression analysis and classification model, exhibited various limitations in the evaluation of soybean possessing potentials, including single, vague evaluation and failure of quantitative prediction, and thereby hindering more efficient and profitable soymilk industry. To solve this problem, 54 soybean cultivars and their corresponding soymilks were subjected to chemical, textural, and sensory analyses to obtain the soybean physicochemical nature (PN) and the soymilk profit and quality attribute (PQA) datasets. A deep-learning based model was established to quantitatively predict PQA data using PN data. Through 45 rounds of training with the stochastic gradient descent optimization, 9 remaining pairs of PN and PQA data were used for model validation. Results suggested that the overall prediction performance of the model showed significant improvements through iterative training, and the trained model eventually reached satisfying predictions (|relative error| ≤ 20%, standard deviation of relative error ≤ 40%) on 78% key soymilk PQAs. Future model training using big data may facilitate better prediction on soymilk odor qualities., 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.)

Bone and soft tissue tumors occur in the musculoskeletal system, and malignant bone tumors of bone and soft tissue account for 0.2% of all human malignant tumors, and if not diagnosed and treated in a timely manner, patients may be at risk of a poor prognosis. Image interpretation plays an increasingly important role in the diagnosis of bone and soft tissue tumors. Artificial intelligence (AI) can be applied in clinical treatment to integrate large amounts of multidimensional data, derive models, predict outcomes, and improve treatment decisions. Among these methods, deep learning is a widely employed technique in AI that predominantly utilizes convolutional neural networks (CNN). The network is implemented through repeated training of datasets and iterative parameter adjustments. Deep learning-based AI models have successfully been applied to various aspects of bone and soft tissue tumors, encompassing but not limiting in image segmentation, tumor detection, classification, grading and staging, chemotherapy effect evaluation, recurrence and prognosis prediction. This paper provides a comprehensive review of the principles and current state of AI in the medical image diagnosis and treatment of bone and soft tissue tumors. Additionally, it explores the present challenges and future prospects in this field.

Background: International Classification of Diseases codes are widely used to describe diagnosis information, but manual coding relies heavily on human interpretation, which can be expensive, time consuming, and prone to errors. With the transition from the International Classification of Diseases, Ninth Revision, to the International Classification of Diseases, Tenth Revision (ICD-10), the coding process has become more complex, highlighting the need for automated approaches to enhance coding efficiency and accuracy. Inaccurate coding can result in substantial financial losses for hospitals, and a precise assessment of outcomes generated by a natural language processing (NLP)-driven autocoding system thus assumes a critical role in safeguarding the accuracy of the Taiwan diagnosis related groups (Tw-DRGs)., Objective: This study aims to evaluate the feasibility of applying an International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM), autocoding system that can automatically determine diagnoses and codes based on free-text discharge summaries to facilitate the assessment of Tw-DRGs, specifically principal diagnosis and major diagnostic categories (MDCs)., Methods: By using the patient discharge summaries from Kaohsiung Medical University Chung-Ho Memorial Hospital (KMUCHH) from April 2019 to December 2020 as a reference data set we developed artificial intelligence (AI)-assisted ICD-10-CM coding systems based on deep learning models. We constructed a web-based user interface for the AI-assisted coding system and deployed the system to the workflow of the certified coding specialists (CCSs) of KMUCHH. The data used for the assessment of Tw-DRGs were manually curated by a CCS with the principal diagnosis and MDC was determined from discharge summaries collected at KMUCHH from February 2023 to April 2023., Results: Both the reference data set and real hospital data were used to assess performance in determining ICD-10-CM coding, principal diagnosis, and MDC for Tw-DRGs. Among all methods, the GPT-2 (OpenAI)-based model achieved the highest F 1 -score, 0.667 (F 1 -score 0.851 for the top 50 codes), on the KMUCHH test set and a slightly lower F 1 -score, 0.621, in real hospital data. Cohen κ evaluation for the agreement of MDC between the models and the CCS revealed that the overall average κ value for GPT-2 (κ=0.714) was approximately 12.2 percentage points higher than that of the hierarchy attention network (κ=0.592). GPT-2 demonstrated superior agreement with the CCS across 6 categories of MDC, with an average κ value of approximately 0.869 (SD 0.033), underscoring the effectiveness of the developed AI-assisted coding system in supporting the work of CCSs., Conclusions: An NLP-driven AI-assisted coding system can assist CCSs in ICD-10-CM coding by offering coding references via a user interface, demonstrating the potential to reduce the manual workload and expedite Tw-DRG assessment. Consistency in performance affirmed the effectiveness of the system in supporting CCSs in ICD-10-CM coding and the judgment of Tw-DRGs., (©Hong-Jie Dai, Chen-Kai Wang, Chien-Chang Chen, Chong-Sin Liou, An-Tai Lu, Chia-Hsin Lai, Bo-Tsz Shain, Cheng-Rong Ke, William Yu Chung Wang, Tatheer Hussain Mir, Mutiara Simanjuntak, Hao-Yun Kao, Ming-Ju Tsai, Vincent S Tseng. Originally published in the Journal of Medical Internet Research (https://www.jmir.org), 20.09.2024.)

Purpose: The use of postoperative radiotherapy (PORT) in patients with oral squamous cell carcinoma (OCSCC) lacks clear boundaries due to the non-negligible toxicity accompanying its remarkable cancer-killing effect. This study aims at validating the ability of deep learning models to develop individualized PORT recommendations for patients with OCSCC and quantifying the impact of patient characteristics on treatment selection., Methods: Participants were categorized into two groups based on alignment between model-recommended and actual treatment regimens, with their overall survival compared. Inverse probability treatment weighting was used to reduce bias, and a mixed-effects multivariate linear regression illustrated how baseline characteristics influenced PORT selection., Results: 4990 patients with OCSCC met the inclusion criteria. Deep Survival regression with Mixture Effects (DSME) demonstrated the best performance among all the models and National Comprehensive Cancer Network guidelines. The efficacy of PORT is enhanced as the lymph node ratio (LNR) increases. Similar enhancements in efficacy are observed in patients with advanced age, large tumors, multiple positive lymph nodes, tongue involvement, and stage IVA. Early-stage (stage 0-II) OCSCC may safely omit PORT., Conclusions: This is the first study to incorporate LNR as a tumor character to make personalized recommendations for patients. DSME can effectively identify potential beneficiaries of PORT and provide quantifiable survival benefits., (© 2024 Wiley Periodicals LLC.)

Objectives: The assessment of lumbar central canal stenosis (LCCS) is crucial for diagnosing and planning treatment for patients with low back pain and neurogenic pain. However, manual assessment methods are time-consuming, variable, and require axial MRIs. The aim of this study is to develop and validate an AI-based model that automatically classifies LCCS using sagittal T2-weighted MRIs., Methods: A pre-existing 3D AI algorithm was utilized to segment the spinal canal and intervertebral discs (IVDs), enabling quantitative measurements at each IVD level. Four musculoskeletal radiologists graded 683 IVD levels from 186 LCCS patients using the 4-class Lee grading system. A second consensus reading was conducted by readers 1 and 2, which, along with automatic measurements, formed the training dataset for a multiclass (grade 0-3) and binary (grade 0-1 vs. 2-3) random forest classifier with tenfold cross-validation., Results: The multiclass model achieved a Cohen's weighted kappa of 0.86 (95% CI: 0.82-0.90), comparable to readers 3 and 4 with 0.85 (95% CI: 0.80-0.89) and 0.73 (95% CI: 0.68-0.79) respectively. The binary model demonstrated an AUC of 0.98 (95% CI: 0.97-0.99), sensitivity of 93% (95% CI: 91-96%), and specificity of 91% (95% CI: 87-95%). In comparison, readers 3 and 4 achieved a specificity of 98 and 99% and sensitivity of 74 and 54%, respectively., Conclusion: Both the multiclass and binary models, while only using sagittal MR images, perform on par with experienced radiologists who also had access to axial sequences. This underscores the potential of this novel algorithm in enhancing diagnostic accuracy and efficiency in medical imaging., Key Points: Question How can the classification of lumbar central canal stenosis (LCCS) be made more efficient? Findings Multiclass and binary AI models, using only sagittal MR images, performed on par with experienced radiologists who also had access to axial sequences. Clinical relevance Our AI algorithm accurately classifies LCCS from sagittal MRI, matching experienced radiologists. This study offers a promising tool for automated LCCS assessment from sagittal T2 MRI, potentially reducing the reliance on additional axial imaging., (© 2024. The Author(s).)

This study aimed to design a simple and efficient automatic segmentation model for medical images, so as to facilitate doctors to make more accurate diagnosis and treatment plan. A hybrid lightweight network LIT-Unet with symmetric encoder-decoder U-shaped architecture is proposed. Synapse multi-organ segmentation dataset and automated cardiac diagnosis challenge (ACDC) dataset were used to test the segmentation performance of the method. Two indexes, Dice similarity coefficient (DSC ↑) and 95% Hausdorff distance (HD95 ↓), were used to evaluate and compare the segmentation ability with the current advanced methods. Ablation experiments were conducted to demonstrate the lightweight nature and effectiveness of our model. For Synapse dataset, our model achieves a higher DSC score (80.40%), an improvement of 3.8% over the typical hybrid model (TransUnet). The 95 HD value is low at 20.67%. For ACDC dataset, LIT-Unet achieves the optimal average DSC (%) of 91.84 compared with other networks listed. Compared to patch expanding, the DSC of our model is intuitively improved by 1.62% with the help of deformable token merging (DTM). These results show that the proposed hierarchical LIT-Unet can achieve significant accuracy and is expected to provide a reliable basis for clinical diagnosis and treatment., (© 2024. The Author(s), under exclusive licence to Japanese Society of Radiological Technology and Japan Society of Medical Physics.)

Background: To explore an artificial intelligence (AI) technology employing YOLOv8 for quality control (QC) on elbow joint radiographs., Methods: From January 2022 to August 2023, 2643 consecutive elbow radiographs were collected and randomly assigned to the training, validation, and test sets in a 6:2:2 ratio. We proposed the anteroposterior (AP) and lateral (LAT) models to identify target detection boxes and key points on elbow radiographs using YOLOv8. These identifications were transformed into five quality standards: (1) AP elbow positioning coordinates (X A and Y A ); (2) olecranon fossa positioning distance parameters (S 17 and S 27 ); (3) key points of joint space (Y 3 , Y 4 , Y 5 and Y 6 ); (4) LAT elbow positioning coordinates (X 2 and Y 2 ); and (5) flexion angle. Models were trained and validated using 2,120 radiographs. A test set of 523 radiographs was used for assessing the agreement between AI and physician and to evaluate clinical efficiency of models., Results: The AP and LAT models demonstrated high precision, recall, and mean average precision for identifying boxes and points. AI and physicians showed high intraclass correlation coefficient (ICC) in evaluating: AP coordinates X A (0.987) and Y A (0.991); olecranon fossa parameters S 17 (0.964) and S 27 (0.951); key points Y 3 (0.998), Y 4 (0.997), Y 5 (0.998) and Y 6 (0.959); LAT coordinates X 2 (0.994) and Y 2 (0.986); and flexion angle (0.865). Compared to manual methods, using AI, QC time was reduced by 43% for AP images and 45% for LAT images (p < 0.001)., Conclusion: YOLOv8-based AI technology is feasible for QC of elbow radiography with high performance., Relevance Statement: This study proposed and validated a YOLOv8-based AI model for automated quality control in elbow radiography, obtaining high efficiency in clinical settings., Key Points: QC of elbow joint radiography is important for detecting diseases. Models based on YOLOv8 are proposed and perform well in image QC. Models offer objective and efficient solutions for QC in elbow joint radiographs., (© 2024. The Author(s).)

Objectives: The selection of treatment for dental plaque is closely related to the condition of the plaque on different teeth. This study validated the ability of CNN models in assessing the dental plaque indices., Materials and Methods: In 70 (20 male and 50 female) healthy adults (18 to 55 years old), frontal and lateral view intraoral images (210) of plaque disclosing agent stained permanent and deciduous dentitions were obtained. A three-stage method was employed, where the You Look Only Once version 8 (YOLOv8) model was first used to detect the target teeth, followed by the prompt-based Segment Anything Model (SAM) segmentation algorithm to segment teeth. A new single-tooth dataset consisting of 1400 photographs was obtained after applying a two-stage method. Finally, a multi-class classification model DeepPlaq was trained and evaluated on the accuracy of dental plaque indexing based on the Quigley-Hein Index (QHI) scoring system. Classification performance was measured using accuracy, recall, precision, and F1-score., Results: The teeth detector exhibited an accuracy (mean average precision, mAP) of approximately 0.941 ± 0.005 in identifying teeth with plaque disclosing agents. The maximum accuracy attained in the plaque indexing through DeepPlaq was 0.84 (probability that DeepPlaq scored identical to experts), and the smallest average scoring error was less than 0.25 on a 0 to 5 scale for scoring., Conclusions: A three-stage approach demonstrated excellent performance in detecting and segmenting target teeth, and DeepPlaq model also showed strong performance in assessing dental plaque indices., Clinical Relevance: Application of artificial intelligence to the evaluation of dental plaque distribution could enhance diagnostic accuracy and treatment efficiency and accuracy., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Background: Perfusion magnetic resonance imaging (MRI)s plays a central role in the diagnosis and monitoring of neurovascular or neurooncological disease. However, conventional processing techniques are limited in their ability to capture relevant characteristics of the perfusion dynamics and suffer from a lack of standardization., Purpose: We propose a physics-informed deep learning framework which is capable of analyzing dynamic susceptibility contrast perfusion MRI data and recovering the dynamic tissue response with high accuracy., Methods: The framework uses physics-informed neural networks (PINNs) to learn the voxel-wise TRF, which represents the dynamic response of the local vascular network to the contrast agent bolus. The network output is stabilized by total variation and elastic net regularization. Parameter maps of normalized cerebral blood flow (nCBF) and volume (nCBV) are then calculated from the predicted residue functions. The results are validated using extensive comparisons to values derived by conventional Tikhonov-regularized singular value decomposition (TiSVD), in silico simulations and an in vivo dataset of perfusion MRI exams of patients with high-grade gliomas., Results: The simulation results demonstrate that PINN-derived residue functions show a high concordance with the true functions and that the calculated values of nCBF and nCBV converge towards the true values for higher contrast-to-noise ratios. In the in vivo dataset, we find high correlations between conventionally derived and PINN-predicted perfusion parameters (Pearson's rho for nCBF: 0.84 ± 0.03 $0.84 \pm 0.03$ and nCBV: 0.92 ± 0.03 $0.92 \pm 0.03$ ) and very high indices of image similarity (structural similarity index for nCBF: 0.91 ± 0.03 $0.91 \pm 0.03$ and for nCBV: 0.98 ± 0.00 $0.98 \pm 0.00$ )., Conclusions: PINNs can be used to analyze perfusion MRI data and stably recover the response functions of the local vasculature with high accuracy., (© 2024 American Association of Physicists in Medicine.)

Objective . Previous methods for robustness evaluation rely on dose calculation for a number of uncertainty scenarios, which either fails to provide statistical meaning when the number is too small (e.g., ∼8) or becomes unfeasible in daily clinical practice when the number is sufficiently large (e.g., >100). Our proposed deep learning (DL)-based method addressed this issue by avoiding the intermediate dose calculation step and instead directly predicting the percentile dose distribution from the nominal dose distribution using a DL model. In this study, we sought to validate this DL-based statistical robustness evaluation method for efficient and accurate robustness quantification in head and neck (H&N) intensity-modulated proton therapy with diverse beam configurations and multifield optimization. Approach . A dense, dilated 3D U-net was trained to predict the 5th and 95th percentile dose distributions of uncertainty scenarios using the nominal dose and planning CT images. The data set comprised proton therapy plans for 582 H&N cancer patients. Ground truth percentile values were estimated for each patient through 600 dose recalculations, representing randomly sampled uncertainty scenarios. The comprehensive comparisons of different models were conducted for H&N cancer patients, considering those with and without a beam mask and diverse beam configurations, including varying beam angles, couch angles, and beam numbers. The performance of our model trained based on a mixture of patients with H&N and prostate cancer was also assessed in contrast with models trained based on data specific for patients with cancer at either site. Results . The DL-based model's predictions of percentile dose distributions exhibited excellent agreement with the ground truth dose distributions. The average gamma index with 2 mm / 2%, consistently exceeded 97% for both 5th and 95th percentile dose volumes. Mean dose-volume histogram error analysis revealed that predictions from the combined training set yielded mean errors and standard deviations that were generally similar to those in the specific patient training data sets. Significance . Our proposed DL-based method for evaluation of the robustness of proton therapy plans provides precise, rapid predictions of percentile dose for a given confidence level regardless of the beam arrangement and cancer site. This versatility positions our model as a valuable tool for evaluating the robustness of proton therapy across various cancer sites., (Creative Commons Attribution license.)

N-terminal coding sequence (NCS) influences gene expression by impacting the translation initiation rate. The NCS optimization problem is to find an NCS that maximizes gene expression. The problem is important in genetic engineering. However, current methods for NCS optimization such as rational design and statistics-guided approaches are labor-intensive yield only relatively small improvements. This paper introduces a deep learning/synthetic biology codesigned few-shot training workflow for NCS optimization. Our method utilizes k -nearest encoding followed by word2vec to encode the NCS, then performs feature extraction using attention mechanisms, before constructing a time-series network for predicting gene expression intensity, and finally a direct search algorithm identifies the optimal NCS with limited training data. We took green fluorescent protein (GFP) expressed by Bacillus subtilis as a reporting protein of NCSs, and employed the fluorescence enhancement factor as the metric of NCS optimization. Within just six iterative experiments, our model generated an NCS (MLD 62 ) that increased average GFP expression by 5.41-fold, outperforming the state-of-the-art NCS designs. Extending our findings beyond GFP, we showed that our engineered NCS (MLD 62 ) can effectively boost the production of N-acetylneuraminic acid by enhancing the expression of the crucial rate-limiting GNA1 gene, demonstrating its practical utility. We have open-sourced our NCS expression database and experimental procedures for public use.

Ultrasound imaging has shown promise in assessing synovium inflammation associated early stages of rheumatoid arthritis (RA). The precise identification of the synovium and the quantification of inflammation-specific imaging biomarkers is a crucial aspect of accurately quantifying and grading RA. In this study, a deep learning-based approach is presented that automates the segmentation of the synovium in ultrasound images of finger joints affected by RA. Two convolutional neural network architectures for image segmentation were trained and validated in a limited number of 2-D images, extracted from N  = 18 3-D ultrasound volumes acquired from N  = 9 RA patients, with sparse ground truth annotations of the synovium. Various augmentation strategies were employed to enhance the diversity and size of the training dataset. The utilization of geometric and noise augmentation transforms resulted in the highest dice score (0.768 ± 0 . 031 , N = 6 ) , and intersection over union ( 0 . 624 ± 0.040, N  = 6), as determined via six-fold cross-validation. In addition, the segmentation model is used to generate dense 3-D segmentation maps in the ultrasound volumes, based on the available sparse annotations. The developed technique shows promise in facilitating more efficient and standardized workflow for RA screening using ultrasound imaging., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Disorders of consciousness (DoC) represent a challenging and complex group of neurological conditions characterised by profound disturbances in consciousness. The current range of treatments for DoC is limited. This has sparked growing interest in developing new treatments, including the use of psychedelic drugs. Nevertheless, clinical investigations and the mechanisms behind them are methodologically and ethically constrained. To tackle these limitations, we combined biologically plausible whole-brain models with deep learning techniques to characterise the low-dimensional space of DoC patients. We investigated the effects of model pharmacological interventions by including the whole-brain dynamical consequences of the enhanced neuromodulatory level of different neurotransmitters, and providing geometrical interpretation in the low-dimensional space. Our findings show that serotonergic and opioid receptors effectively shifted the DoC models towards a dynamical behaviour associated with a healthier state, and that these improvements correlated with the mean density of the activated receptors throughout the brain. These findings mark an important step towards the development of treatments not only for DoC but also for a broader spectrum of brain diseases. Our method offers a promising avenue for exploring the therapeutic potential of pharmacological interventions within the ethical and methodological confines of clinical research., (© 2024. The Author(s).)

Background: This study aims to evaluate the integration of optical coherence tomography (OCT) and peripheral blood immune indicators for predicting oral cancer prognosis by artificial intelligence., Methods: In this study, we examined patients undergoing radical oral cancer resection and explored inherent relationships among clinical data, OCT images, and peripheral immune indicators for oral cancer prognosis. We firstly built a peripheral blood immune indicator-guided deep learning feature representation method for OCT images, and further integrated a multi-view prognostic radiomics model incorporating feature selection and logistic modeling. Thus, we can assess the prognostic impact of each indicator on oral cancer by quantifying OCT features., Results: We collected 289 oral mucosal samples from 68 patients, yielding 1,445 OCT images. Using our deep radiomics-based prognosis model, it achieved excellent discrimination for oral cancer prognosis with the area under the receiver operating characteristic curve (AUC) of 0.886, identifying systemic immune-inflammation index (SII) as the most informative feature for prognosis prediction. Additionally, the deep learning model also performed excellent results with 85.26% accuracy and 0.86 AUC in classifying the SII risk., Conclusions: Our study effectively merged OCT imaging with peripheral blood immune indicators to create a deep learning-based model for inflammatory risk prediction in oral cancer. Additionally, we constructed a comprehensive multi-view radiomics model that utilizes deep learning features for accurate prognosis prediction. The study highlighted the significance of the SII as a crucial indicator for evaluating patient outcomes, corroborating our clinical statistical analyses. This integration underscores the potential of combining imaging and blood indicators in clinical decision-making., Trial Registration: The clinical trial associated with this study was prospectively registered in the Chinese Clinical Trial Registry with the trial registration number (TRN) ChiCTR2200064861. The registration was completed on 2021., (© 2024. The Author(s).)

Conventional methods for detecting unsaturated fatty acids (UFAs) pose challenges for rapid analyses due to the need for complex pretreatment and expensive instruments. Here, we developed an intelligent platform for facile and low-cost analysis of UFAs by combining a smartphone-assisted colorimetric sensor array (CSA) based on MnO 2 nanozymes with "image segmentation-feature extraction" deep learning (ISFE-DL). Density functional theory predictions were validated by doping experiments using Ag, Pd, and Pt, which enhanced the catalytic activity of the MnO 2 nanozymes. A CSA mimicking mammalian olfactory system was constructed with the principle that UFAs competitively inhibit the oxidization of the enzyme substrate, resulting in color changes in the nanozyme-ABTS substrate system. Through linear discriminant analysis coupled with the smartphone App "Quick Viewer" that utilizes multihole parallel acquisition technology, oleic acid (OA), linoleic acid (LA), α-linolenic acid (ALA), and their mixtures were clearly discriminated; various edible vegetable oils, different camellia oils (CAO), and adulterated CAOs were also successfully distinguished. Furthermore, the ISFE-DL method was combined in multicomponent quantitative analysis. The sensing elements of the CSA (3 × 4) were individually segmented for single-hole feature extraction containing information from 38,868 images of three UFAs, thereby allowing for the extraction of more features and augmenting sample size. After training with the MobileNetV3 small model, the determination coefficients of OA, LA, and ALA were 0.9969, 0.9668, and 0.7393, respectively. The model was embedded in the smartphone App "Intelligent Analysis Master" for one-click quantification. We provide an innovative approach for intelligent and efficient qualitative and quantitative analysis of UFAs and other compounds with similar characteristics.

As the global transition towards clean energy accelerates, the demand for the widespread adoption of solar energy continues to rise. However, traditional object detection models prove inadequate for handling photovoltaic cell electroluminescence (EL) images, which are characterized by high levels of noise. To address this challenge, we developed an advanced defect detection model specifically designed for photovoltaic cells, which integrates topological knowledge extraction. Our approach begins with the introduction of a multi-scale dynamic context-based feature extraction method, capable of generating static context by thoroughly capturing the local texture and structural information of multi-scale defects. This static context is then combined with dynamic context to produce fine-grained local features. Subsequently, we developed a centralized feature pyramid structure, enhanced by spatial semantics, which models the explicit visual center. This structure effectively elucidates the relationship between local and global features in defect images, thereby improving the representation of defect characteristics. Finally, we implemented a feature enhancement strategy grounded in spatial semantic knowledge extraction. This strategy uncovers potential correlations among defect targets by constructing a spatial semantic topology of features, mapping these features to a higher-order representation, and ultimately delivering precise defect detection results., (© 2024. The Author(s).)

To compare the image quality and fat attenuation index (FAI) of coronary artery CT angiography (CCTA) under different tube voltages between deep learning image reconstruction (DLIR) and adaptive statistical iterative reconstruction V (ASIR-V). Three hundred one patients who underwent CCTA with automatic tube current modulation were prospectively enrolled and divided into two groups: 120 kV group and low tube voltage group. Images were reconstructed using ASIR-V level 50% (ASIR-V50%) and high-strength DLIR (DLIR-H). In the low tube voltage group, the voltage was selected according to Chinese BMI classification: 70 kV (BMI < 24 kg/m 2 ), 80 kV (24 kg/m 2  ≤ BMI < 28 kg/m 2 ), 100 kV (BMI ≥ 28 kg/m 2 ). At the same tube voltage, the subjective and objective image quality, edge rise distance (ERD), and FAI between different algorithms were compared. Under different tube voltages, we used DLIR-H to compare the differences between subjective, objective image quality, and ERD. Compared with the 120 kV group, the DLIR-H image noise of 70 kV, 80 kV, and 100 kV groups increased by 36%, 25%, and 12%, respectively (all P < 0.001); contrast-to-noise ratio (CNR), subjective score, and ERD were similar (all P > 0.05). In the 70 kV, 80 kV, 100 kV, and 120 kV groups, compared with ASIR-V50%, DLIR-H image noise decreased by 50%, 53%, 47%, and 38-50%, respectively; CNR, subjective score, and FAI value increased significantly (all P < 0.001), ERD decreased. Compared with 120 kV tube voltage, the combination of DLIR-H and low tube voltage maintains image quality. At the same tube voltage, compared with ASIR-V, DLIR-H improves image quality and FAI value., (© 2024. The Author(s) under exclusive licence to Society for Imaging Informatics in Medicine.)

Objective: Cachexia is a devastating condition, characterized by involuntary loss of muscle mass with or without loss of adipose tissue mass. It affects more than half of patients with lung cancer, diminishing treatment effects and increasing mortality. Cone-beam computed tomography (CBCT) images, routinely acquired during radiotherapy treatment, might contain valuable anatomical information for monitoring body composition changes associated with cachexia. For this purpose, we propose an automatic artificial intelligence (AI)-based workflow, consisting of CBCT to CT conversion, followed by segmentation of pectoralis muscles. &#xD;Approach. Data from 140 stage III non-small cell lung cancer patients was used. Two deep learning models, cycle-consistent generative adversarial network (CycleGAN) and contrastive unpaired translation (CUT), were used for unpaired training of CBCT to CT conversion, to generate synthetic CT (sCT) images. The no-new U-Net (nnU-Net) model was used for automatic pectoralis muscle segmentation. To evaluate tissue segmentation performance in the absence of ground truth labels, an uncertainty metric (UM) based on Monte Carlo dropout was developed and validated. &#xD;Main results. Both CycleGAN and CUT restored the Hounsfield unit fidelity of the CBCT images compared to the planning CT (pCT) images and visually reduced streaking artefacts. The nnU-Net model achieved a Dice Similarity Coefficient (DSC) of 0.93, 0.94, 0.92 for the CT, sCT and CBCT images, respectively, on an independent test set. The UM showed a high correlation with DSC with a correlation coefficient of -0.84 for the pCT dataset and -0.89 for the sCT dataset. &#xD;Significance. This paper shows a proof-of-concept for automatic AI-based monitoring of the pectoralis muscle area of lung cancer patients during radiotherapy treatment based on CBCT images, which provides an unprecedented time resolution of muscle mass loss during cachexia progression. Ultimately, the proposed workflow could provide valuable information for early intervention of cachexia, ideally resulting in improved cancer treatment outcome., (Creative Commons Attribution license.)

In the agriculture and real estate industries, land lot shapes have mostly been classified by visual inspection or hard-crafted rules. These conventional methods are time-consuming, resource-intensive, and subject to human bias. This study aims to fill this gap and alleviate problems inherent in traditional lot classification approaches. This study attempts to classify lot shapes automatically, using a convolutional neural network. A study area was chosen, image data of the lots in the area were collected and preprocessed, and an Xception neural network was specified to classify land lots according to their shapes. The test applied to a different area adjacent to the study area achieved an accuracy of 90.1% and area under the curve (AUC) of 0.96. Additionally, this study demonstrated that shape regularity can be quantified using the output scores from the neural network analysis. This is the first attempt to employ a deep learning algorithm for land management on a micro-spatial scale. The classification approach proposed in this study is expected to encourage the rapid and accurate classification of various lot shapes., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Ham, Lee. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Objectives: To investigate the feasibility of a deep learning-constrained compressed sensing (DL-CS) method in non-contrast-enhanced modified DIXON (mDIXON) coronary magnetic resonance angiography (MRA) and compare its diagnostic accuracy using coronary CT angiography (CCTA) as a reference standard., Methods: Ninety-nine participants were prospectively recruited for this study. Thirty healthy subjects (age range: 20-65 years; 50% female) underwent three non-contrast mDIXON-based coronary MRA sequences including DL-CS, CS, and conventional sequences. The three groups were compared based on the scan time, subjective image quality score, signal-to-noise ratio (SNR), and contrast-to-noise ratio (CNR). The remaining 69 patients suspected of coronary artery disease (CAD) (age range: 39-83 years; 51% female) underwent the DL-CS coronary MRA and its diagnostic performance was compared with that of CCTA., Results: The scan time for the DL-CS and CS sequences was notably shorter than that of the conventional sequence (9.6 ± 3.1 min vs 10.0 ± 3.4 min vs 13.0 ± 4.9 min; p < 0.001). The DL-CS sequence obtained the highest image quality score, mean SNR, and CNR compared to CS and conventional methods (all p < 0.001). Compared to CCTA, the accuracy, sensitivity, and specificity of DL-CS mDIXON coronary MRA per patient were 84.1%, 92.0%, and 79.5%; those per vessel were 90.3%, 82.6%, and 92.5%; and those per segment were 98.0%, 85.1%, and 98.0%, respectively., Conclusion: The DL-CS mDIXON coronary MRA provided superior image quality and short scan time for visualizing coronary arteries in healthy individuals and demonstrated high diagnostic value compared to CCTA in CAD patients., Critical Relevance Statement: DL-CS resulted in improved image quality with an acceptable scan time, and demonstrated excellent diagnostic performance compared to CCTA, which could be an alternative to enhance the workflow of coronary MRA., Key Points: Current coronary MRA techniques are limited by scan time and the need for noise reduction. DL-CS reduced the scan time in coronary MR angiography. Deep learning achieved the highest image quality among the three methods. Deep learning-based coronary MR angiography demonstrated high performance compared to CT angiography., (© 2024. The Author(s).)

Purpose: To develop fully-automated abdominal organ segmentation algorithms from non-enhanced abdominal CT and low-dose chest CT and assess their feasibility for automated CT volumetry and 3D radiomics analysis of abdominal solid organs., Methods: Fully-automated nnU-Net-based models were developed to segment the liver, spleen, and both kidneys in non-enhanced abdominal CT, and the liver and spleen in low-dose chest CT. 105 abdominal CTs and 60 low-dose chest CTs were used for model development, and 55 abdominal CTs and 10 low-dose chest CTs for external testing. The segmentation performance for each organ was assessed using the Dice similarity coefficients, with manual segmentation results serving as the ground truth. Agreements between ground-truth measurements and model estimates of organ volume and 3D radiomics features were assessed using the Bland-Altman analysis and intraclass correlation coefficients (ICC)., Results: The models accurately segmented the liver, spleen, right kidney, and left kidney in abdominal CT and the liver and spleen in low-dose chest CT, showing mean Dice similarity coefficients in the external dataset of 0.968, 0.960, 0.952, and 0.958, respectively, in abdominal CT, and 0.969 and 0.960, respectively, in low-dose chest CT. The model-estimated and ground truth volumes of these organs exhibited mean differences between - 0.7% and 2.2%, with excellent agreements. The automatically extracted mean and median Hounsfield units (ICCs, 0.970-0.999 and 0.994-0.999, respectively), uniformity (ICCs, 0.985-0.998), entropy (ICCs, 0.931-0.993), elongation (ICCs, 0.978-0.992), and flatness (ICCs, 0.973-0.997) showed excellent agreement with ground truth measurements for each organ; however, skewness (ICCs, 0.210-0.831), kurtosis (ICCs, 0.053-0.933), and sphericity (ICCs, 0.368-0.819) displayed relatively low and inconsistent agreement., Conclusion: Our nnU-Net-based models accurately segmented abdominal solid organs in non-enhanced abdominal and low-dose chest CT, enabling reliable automated measurements of organ volume and specific 3D radiomics features., (© 2024. The Author(s).)

The authors introduce a novel framework that integrates federated learning with Graph Neural Networks (GNNs) to classify diseases, incorporating Human-in-the-Loop methodologies. This advanced framework innovatively employs collaborative voting mechanisms on subgraphs within a Protein-Protein Interaction (PPI) network, situated in a federated ensemble-based deep learning context. This methodological approach marks a significant stride in the development of explainable and privacy-aware Artificial Intelligence, significantly contributing to the progression of personalized digital medicine in a responsible and transparent manner., (© 2024. The Author(s).)

Background: Significant challenges are arising around how to best enable peer communities, broaden educational reach, and innovate in pedagogy. While digital education can address these challenges, digital elements alone do not guarantee effective learning. This study reports a blended learning approach integrating online and face-to-face components, guided by the Student Approaches to Learning framework., Methods: This study was carried out investigating learning in first and second year medical students over two academic years, 2019/20 and 2020/21. We evaluated: (1) comparison of students engaged with blended learning and traditional learning; and (2) student learning engaged with blended learning approach over a two-year preclinical curriculum. A revised two-factor study process questionnaire (R-SPQ-2F) evaluated students' surface/deep learning before and after an academic year. Learning experience (LE) questionnaire was administered over the domains of learning engagement, and outcomes of learning approach. In-depth interviews were carried out to understand the context of students' responses to the R-SPQ-2F and LE questionnaires., Results: The R-SPQ-2F analysis indicated first year students maintained deep learning but second year students became neutral across the academic year, regardless of learning approach, with workload contributing to this outcome. R-SPQ-2F sub-scales showed that students engaged with blended learning maintained an intrinsic interest to learning, as compared to traditional learning which led to surface learning motives. The LE questionnaire showed students engaged with blended learning had deeper subject interest, and more positive perceptions of workload, feedback, and effectively developed skills and knowledge. However, peer interactions from blended learning were significantly lacking. In-depth interviews revealed that the flexibility and multi-modality of blended learning enabled learning, but the best use of these features require teacher support. Online interactions could be cultivated through intentional institutional efforts., Conclusions: This study highlights the importance of designing blended learning that leverages technology-enabled flexibility while prioritising collaborative, learner-centred spaces for deep engagement and knowledge construction., (© 2024. The Author(s).)

The gallbladder (GB) is a small pouch and a deep tissue placed under the liver. GB Cancer (GBC) is a deadly illness that is complex to discover in an initial phase. Initial diagnosis can significantly enhance the existence rate. Non-ionizing energy, low cost, and convenience make the US a general non-invasive analytical modality for patients with GB diseases. Automatic recognition of GBC from US imagery is a significant issue that has gained much attention from researchers. Recently, machine learning (ML) techniques dependent on convolutional neural network (CNN) architectures have prepared transformational growth in radiology and medical analysis for illnesses like lung, pancreatic, breast, and melanoma. Deep learning (DL) is a region of artificial intelligence (AI), a functional medical tomography model that can help in the initial analysis of GBC. This manuscript presents an Automated Gall Bladder Cancer Detection using an Artificial Gorilla Troops Optimizer with Transfer Learning (GBCD-AGTOTL) technique on Ultrasound Images. The GBCD-AGTOTL technique examines the US images for the presence of gall bladder cancer using the DL model. In the initial stage, the GBCD-AGTOTL technique preprocesses the US images using a median filtering (MF) approach. The GBCD-AGTOTL technique applies the Inception module for feature extraction, which learns the complex and intrinsic patterns in the pre-processed image. Besides, the AGTO algorithm-based hyperparameter tuning procedure takes place, which optimally picks the hyperparameter values of the Inception technique. Lastly, the bidirectional gated recurrent unit (BiGRU) model helps classify gall bladder cancer. A series of simulation analyses were performed to ensure the performance of the GBCD-AGTOTL technique on the GBC dataset. The experimental outcomes inferred the enhanced abilities of the GBCD-AGTOTL in detecting gall bladder cancer., (© 2024. The Author(s).)

Motivation: Detection of germline variants in next-generation sequencing data is an essential component of modern genomics analysis. Variant detection tools typically rely on statistical algorithms such as de Bruijn graphs or Hidden Markov Models, and are often coupled with heuristic techniques and thresholds to maximize accuracy. Despite significant progress in recent years, current methods still generate thousands of false positive detections in a typical human whole genome, creating a significant manual review burden., Results: We introduce a new approach that replaces the handcrafted statistical techniques of previous methods with a single deep generative model. Using a standard transformer-based encoder and double-decoder architecture, our model learns to construct diploid germline haplotypes in a generative fashion identical to modern Large Language Models (LLMs). We train our model on 37 Whole Genome Sequences (WGS) from Genome-in-a-Bottle samples, and demonstrate that our method learns to produce accurate haplotypes with correct phase and genotype for all classes of small variants. We compare our method, called Jenever, to FreeBayes, GATK HaplotypeCaller, Clair3 and DeepVariant, and demonstrate that our method has superior overall accuracy compared to other methods. At F1-maximizing quality thresholds, our model delivers the highest sensitivity, precision, and the fewest genotyping errors for insertion and deletion variants. For single nucleotide variants our model demonstrates the highest sensitivity but at somewhat lower precision, and achieves the highest overall F1 score among all callers we tested., Availability and Implementation: Jenever is implemented as a python-based command line tool. Source code is available at https://github.com/ARUP-NGS/jenever/., (© The Author(s) 2024. Published by Oxford University Press.)

Objective: To evaluate radiation dose and image quality of a double-low CCTA protocol reconstructed utilizing high-strength deep learning image reconstructions (DLIR-H) compared to standard adaptive statistical iterative reconstruction (ASiR-V) protocol in non-obese patients., Materials and Methods: From June to October 2022, consecutive patients, undergoing clinically indicated CCTA, with BMI < 30 kg/m 2 were prospectively included and randomly assigned into three groups: group A (100 kVp, ASiR-V 50%, iodine delivery rate [IDR] = 1.8 g/s), group B (80 kVp, DLIR-H, IDR = 1.4 g/s), and group C (80 kVp, DLIR-H, IDR = 1.2 g/s). High-concentration contrast medium was administered. Image quality analysis was evaluated by two radiologists. Radiation and contrast dose, and objective and subjective image quality were compared across the three groups., Results: The final population consisted of 255 patients (64 ± 10 years, 161 men), 85 per group. Group B yielded 42% radiation dose reduction (2.36 ± 0.9 mSv) compared to group A (4.07 ± 1.2 mSv; p < 0.001) and achieved a higher signal-to-noise ratio (30.5 ± 11.5), contrast-to-noise-ratio (27.8 ± 11), and subjective image quality (Likert scale score: 4, interquartile range: 3-4) compared to group A and group C (all p ≤ 0.001). Contrast medium dose in group C (44.8 ± 4.4 mL) was lower than group A (57.7 ± 6.2 mL) and B (50.4 ± 4.3 mL), all the comparisons were statistically different (all p < 0.001)., Conclusion: DLIR-H combined with 80-kVp CCTA with an IDR 1.4 significantly reduces radiation and contrast medium exposure while improving image quality compared to conventional 100-kVp with 1.8 IDR protocol in non-obese patients., Clinical Relevance Statement: Low radiation and low contrast medium dose coronary CT angiography protocol is feasible with high-strength deep learning reconstruction and high-concentration contrast medium without compromising image quality., Key Points: Minimizing the radiation and contrast medium dose while maintaining CT image quality is highly desirable. High-strength deep learning iterative reconstruction protocol yielded 42% radiation dose reduction compared to conventional protocol. "Double-low" coronary CTA is feasible with high-strength deep learning reconstruction without compromising image quality in non-obese patients., (© 2024. The Author(s).)

In modern industrial production, permanent magnet motors are an indispensable part of industrial manufacturing. The quality of the magnetic tiles directly affects the working performance of the permanent magnet motors, making the detection of defects on the surface of magnetic tiles critically important. However, due to the small size of defects on the tile image and the reflectivity of the defective surface, the details of image characteristics are not prominently acquired.These problems bring a lot of difficulties for the recognition of magnetic tile defects. In this paper, a magnetic tile defect detection method is proposed for the probAlems of unclear image features and small defects. First, the image is processed using linear variation to enhance the image detail features. Then, by introducing the inverted bottleneck block structure in MobileNetV2, the Attention Parallel Residual Convolution Block (APR) is proposed, and the Lightweight Parallel Attention Residual Network (LPAR-Net) is built. In APR Block, 7 × 7 convolution is introduced so that the model can extract spatial features from a larger range, and weighted fusion of input images by residual structure. In addition, in this paper, CBAM is improved, split into two parts and inserted into APR Block. Finally, the mainstream image classification models and the LPAR-Net proposed in this paper are used for comparison, respectively. The experimental results show that the method achieves 93.63% accuracy on the adopted dataset, which is better than the existing mainstream image classification network models DenseNet, MobileNet, ConvNext and so on. In addition, this paper introduces a strip steel surface defect dataset and compares it with the above image classification model, which verifies that the detection method proposed in this paper still has strong recognition capability., (© 2024. The Author(s).)

Objective. Various artifacts in electroencephalography (EEG) are a big hurdle to prevent brain-computer interfaces from real-life usage. Recently, deep learning-based EEG denoising methods have shown excellent performance. However, existing deep network designs inadequately leverage inter-channel relationships in processing multi-channel EEG signals. Typically, most methods process multi-channel signals in a channel-by-channel way. Considering the correlations among EEG channels during the same brain activity, this paper proposes utilizing channel relationships to enhance denoising performance. Approach. We explicitly model the inter-channel relationships using the self-attention mechanism, hypothesizing that these correlations can support and improve the denoising process. Specifically, we introduce a novel denoising network, named spatial-temporal fusion network (STFNet), which integrates stacked multi-dimension feature extractor to explicitly capture both temporal dependencies and spatial relationships. Main results. The proposed network exhibits superior denoising performance, with a 24.27% reduction in relative root mean squared error compared to other methods on a public benchmark. STFNet proves effective in cross-dataset denoising and downstream classification tasks, improving accuracy by 1.40%, while also offering fast processing on CPU. Significance. The experimental results demonstrate the importance of integrating spatial and temporal characteristics. The computational efficiency of STFNet makes it suitable for real-time applications and a potential tool for deployment in realistic environments., (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Objective. Monotherapy with antiepileptic drugs (AEDs) is the preferred strategy for the initial treatment of epilepsy. However, an inadequate response to the initially prescribed AED is a significant indicator of a poor long-term prognosis, emphasizing the importance of precise prediction of treatment outcomes with the initial AED regimen in patients with epilepsy. Approach . We introduce OxcarNet, an end-to-end neural network framework developed to predict treatment outcomes in patients undergoing oxcarbazepine monotherapy. The proposed predictive model adopts a Sinc Module in its initial layers for adaptive identification of discriminative frequency bands. The derived feature maps are then processed through a Spatial Module, which characterizes the scalp distribution patterns of the electroencephalography (EEG) signals. Subsequently, these features are fed into an attention-enhanced Temporal Module to capture temporal dynamics and discrepancies. A channel module with an attention mechanism is employed to reveal inter-channel dependencies within the output of the Temporal Module, ultimately achieving response prediction. OxcarNet was rigorously evaluated using a proprietary dataset of retrospectively collected EEG data from newly diagnosed epilepsy patients at Nanjing Drum Tower Hospital. This dataset included patients who underwent long-term EEG monitoring in a clinical inpatient setting. Main results. OxcarNet demonstrated exceptional accuracy in predicting treatment outcomes for patients undergoing Oxcarbazepine monotherapy. In the ten-fold cross-validation, the model achieved an accuracy of 97.27%, and in the validation involving unseen patient data, it maintained an accuracy of 89.17%, outperforming six conventional machine learning methods and three generic neural decoding networks. These findings underscore the model's effectiveness in accurately predicting the treatment responses in patients with newly diagnosed epilepsy. The analysis of features extracted by the Sinc filters revealed a predominant concentration of predictive frequencies in the high-frequency range of the gamma band. Significance . The findings of our study offer substantial support and new insights into tailoring early AED selection, enhancing the prediction accuracy for the responses of AEDs., (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Accurate identification and classification of equipment defects are essential for assessing the health of power equipment and making informed maintenance decisions. Traditional defect classification methods, which rely on subjective manual records and intricate defect descriptions, have proven to be inefficient. Existing approaches often evaluate equipment status solely based on defect grade. To enhance the precision of power equipment defect identification, we developed a multi-label classification dataset by compiling historical defect records. Furthermore, we assessed the performance of 11 established multi-label classification methods on this dataset, encompassing both traditional machine learning and deep learning methods. Experimental results reveal that methods considering label correlations exhibit significant performance advantages. By employing balanced loss functions, we effectively address the challenge of sample imbalance across various categories, thereby enhancing classification accuracy. Additionally, segmenting the power equipment defect classification task, which involves numerous labels, into label recall and ranking stages can substantially improve classification performance. The dataset we have created is available for further research by other scholars in the field., (© 2024. The Author(s).)

Feature Selection (FS) is essential in the Internet of Things (IoT)-based Clinical Decision Support Systems (CDSS) to improve the accuracy and efficiency of the system. With the increasing number of sensors and devices used in healthcare, the volume of data generated is vast and complex. Relevant FS from this data is crucial in reducing computational overhead, improving the system's interpretability, and enhancing the Decision-Making System (DMS) quality. FS also aids in addressing the problems of data redundancy and noise, which can negatively impact the system's performance. FS is critical to developing practical and dependable CDSS in IoT-based healthcare sectors. This research proposes a two-phase FS model. Phase-I employs an ensemble of five Filter Methods (FM), followed by a Pearson Correlation Method (PCM). Phase-II uses the Binary Optimized Genetic Grey Wolf Optimization Algorithm (BOGGWOA) as a Wrapper Method (WM). This recommended model integrates the most valuable features of each filter. Then, it uses the Pearson Correlation Coefficient (PCC) to get rid of features that aren't needed, a Support Vector Machine (SVM) to guess how accurate their classification will be, and BOGGWOA as the Wrapper Method (WM) to pick the most essential features with the best CA., (© 2024. The Author(s).)

Aim: To develop a deep learning-based model for automatic retinal vascular segmentation, analyzing and comparing parameters under diverse glucose metabolic status (normal, prediabetes, diabetes) and to assess the potential of artificial intelligence (AI) in image segmentation and retinal vascular parameters for predicting prediabetes and diabetes., Methods: Retinal fundus photos from 200 normal individuals, 200 prediabetic patients, and 200 diabetic patients (600 eyes in total) were used. The U-Net network served as the foundational architecture for retinal artery-vein segmentation. An automatic segmentation and evaluation system for retinal vascular parameters was trained, encompassing 26 parameters., Results: Significant differences were found in retinal vascular parameters across normal, prediabetes, and diabetes groups, including artery diameter ( P =0.008), fractal dimension ( P =0.000), vein curvature ( P =0.003), C-zone artery branching vessel count ( P =0.049), C-zone vein branching vessel count ( P =0.041), artery branching angle ( P =0.005), vein branching angle ( P =0.001), artery angle asymmetry degree ( P =0.003), vessel length density ( P =0.000), and vessel area density ( P =0.000), totaling 10 parameters., Conclusion: The deep learning-based model facilitates retinal vascular parameter identification and quantification, revealing significant differences. These parameters exhibit potential as biomarkers for prediabetes and diabetes., Competing Interests: Conflicts of Interest: Jiang Y, None; Gong D, None; Chen XH, None; Yang L, None; Xu JJ, None; Wei QJ, None; Chen BB, None; Cai YJ, None; Xi WQ, None; Zhang Z, None., (International Journal of Ophthalmology Press.)

Image stitching is a traditional but challenging computer vision task. The goal is to stitch together multiple images with overlapping areas into a single, natural-looking, high-resolution image without ghosts or seams. This article aims to increase the field of view of gastroenteroscopy and reduce the missed detection rate. To this end, an improved depth framework based on unsupervised panoramic image stitching of the gastrointestinal tract is proposed. In addition, preprocessing for aberration correction of monocular endoscope images is introduced, and a C2f module is added to the image reconstruction network to improve the network's ability to extract features. A comprehensive real image data set, GASE-Dataset, is proposed to establish an evaluation benchmark and training learning framework for unsupervised deep gastrointestinal image splicing. Experimental results show that the MSE, RMSE, PSNR, SSIM and RMSE&#95;SW indicators are improved, while the splicing time remains within an acceptable range. Compared with traditional image stitching methods, the performance of this method is enhanced. In addition, improvements are proposed to address the problems of lack of annotated data, insufficient generalization ability and insufficient comprehensive performance in image stitching schemes based on supervised learning. These improvements provide valuable aids in gastrointestinal examination., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Yan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Age estimation plays a crucial role in various fields, including forensic science and anthropology. This study aims to develop and validate DentAge, a deep-learning model for automated age prediction using panoramic dental X-ray images. DentAge was trained on a dataset comprising 21,007 panoramic dental X-ray images sourced from a private dental center in Slovenia. The dataset included subjects aged 4 to 97 years with various dental conditions. Transfer learning was employed, initializing the model with ImageNet weights and fine-tuning on the dental image dataset. The model was trained using stochastic gradient descent with momentum, and mean absolute error (MAE) served as the objective function. Across the test dataset, DentAge achieved an MAE of 3.12 years, demonstrating its efficacy in age prediction. Notably, the model performed well across different age groups, with MAEs ranging from 1.94 (age group [10-20]) to 13.40 years (age group [90-100]). Visual evaluation revealed factors contributing to prediction errors, including prosthetic restorations, tooth loss, and bone resorption. DentAge represents a significant advancement in automated age prediction within dentistry. The model's robust performance across diverse age groups and dental conditions underscores its potential utility in real-world scenarios. Our model will be accessible to the public for further adjustments and validation, ensuring DentAge's effectiveness and trustworthiness in practical scenarios., (© 2024 The Author(s). Journal of Forensic Sciences published by Wiley Periodicals LLC on behalf of American Academy of Forensic Sciences.)

Background: Pilomatricoma, a benign childhood skin tumor, presents diagnostic challenges due to its manifestation variations and requires surgical excision upon histological confirmation of its characteristic cellular features. Recent artificial intelligence (AI) advancements in pathology promise enhanced diagnostic accuracy and treatment approaches for this neoplasm., Methods: We employed a multiscale transfer learning model, initiating the training process at high resolutions and adapting to broader scales. For evaluation purposes, we applied metrics such as accuracy, precision, recall, the F1 score, and the area under the receiver operating characteristic curve (AUROC) to measure the performance of the model, with the statistical significance of the results assessed via two-sided P tests. Our novel approach also included a retrosynthetic saliency mapping technique to achieve enhanced lesion visualization in whole-slide images (WSIs), supporting pathologists' diagnostic processes., Results: Our model effectively navigated the challenges of global-scale classification, achieving a high validation accuracy of up to 0.973 despite some initial fluctuations. This method displayed excellent accuracy in terms of identifying basaloid and ghost cells, especially at lower scales, with slight variability in its ghost cell accuracy and more noticeable changes in the 'Other' category at higher scales. The consistent performance attained for basaloid cells was clear across all scales, whereas areas for improvement were identified in the 'Other' category. The model also excelled at generating detailed and interpretable saliency maps for lesion visualization purposes, thereby enhancing its value in digital pathology diagnostics., Conclusion: Our pilomatricoma study demonstrates the efficacy of a deep learning-based histopathological diagnosis model, as validated by its high performance across various scales, and it is enhanced by an innovative retrosynthetic approach for saliency mapping., Competing Interests: Declaration of competing interest The authors state that they have no conflicts of interest., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background and Aims: Alzheimer's disease (AD) is a degenerative neurological condition that worsens over time and leads to deterioration in cognitive abilities, reduced memory, and, eventually, a decrease in overall functioning. Timely and correct identification of Alzheimer's is essential for effective treatment. The systematic study specifically examines the application of deep learning (DL) algorithms in identifying AD using three-dimensional (3D) imaging methods. The main goal is to evaluate these methods' current state, efficiency, and potential enhancements, offering valuable insights into how DL could improve AD's rapid and precise diagnosis., Methods: We searched different online repositories, such as IEEE Xplore, Elsevier, MDPI, PubMed Central, Science Direct, ACM, Springer, and others, to thoroughly summarize current research on DL methods to diagnose AD by analyzing 3D imaging data published between 2020 and 2024. We use PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to ensure the organization and understandability of the information collection process. We thoroughly analyzed the literature to determine the primary techniques used in these investigations and their findings., Results and Conclusion: The ability of convolutional neural networks (CNNs) and their variations, including 3D CNNs and recurrent neural networks, to detect both temporal and spatial characteristics in volumetric data has led to their widespread use. Methods such as transfer learning, combining multimodal data, and using attention procedures have improved models' precision and reliability. We selected 87 articles for evaluation. Out of these, 31 papers included various concepts, explanations, and elucidations of models and theories, while the other 56 papers primarily concentrated on issues related to practical implementation. This article introduces popular imaging types, 3D imaging for Alzheimer's detection, discusses the benefits and restrictions of the DL-based approach to AD assessment, and gives a view toward future developments resulting from critical evaluation., Competing Interests: The authors declare no conflict of interest., (© 2024 The Author(s). Health Science Reports published by Wiley Periodicals LLC.)

To identify patterns in big medical datasets and use Deep Learning and Machine Learning (ML) to reliably diagnose Cardio Vascular Disease (CVD), researchers are currently delving deeply into these fields. Training on large datasets and producing highly accurate validation results is exceedingly difficult. Furthermore, early and precise diagnosis is necessary due to the increased global prevalence of cardiovascular disease (CVD). However, the increasing complexity of healthcare datasets makes it challenging to detect feature connections and produce precise predictions. To address these issues, the Intelligent Cardiovascular Disease Diagnosis based on Ant Colony Optimisation with Enhanced Deep Learning (ICVD-ACOEDL) model was developed. This model employs feature selection (FS) and hyperparameter optimization to diagnose CVD. Applying a min-max scaler, medical data is first consistently prepared. The key feature that sets ICVD-ACOEDL apart is the use of Ant Colony Optimisation (ACO) to select an optimal feature subset, which in turn helps to upgrade the performance of the ensuring deep learning enhanced neural network (DLENN) classifier. The model reforms the hyperparameters of DLENN for CVD classification using Bayesian optimization. Comprehensive evaluations on benchmark medical datasets show that ICVD-ACOEDL exceeds existing techniques, indicating that it could have a significant impact on CVD diagnosis. The model furnishes a workable way to increase CVD classification efficiency and accuracy in real-world medical situations by incorporating ACO for feature selection, min-max scaling for data pre-processing, and Bayesian optimization for hyperparameter tweaking., (© 2024. The Author(s).)

Leukemia is a type of blood tumour that occurs because of abnormal enhancement in WBCs (white blood cells) in the bone marrow of the human body. Blood-forming tissue cancer influences the lymphatic and bone marrow system. The early diagnosis and detection of leukaemia, i.e., the accurate difference of malignant leukocytes with little expense at the beginning of the disease, is a primary challenge in the disease analysis field. Despite the higher occurrence of leukemia, there is a lack of flow cytometry tools, and the procedures accessible at medical diagnostics centres are time-consuming. Distinct researchers have implemented computer-aided diagnostic (CAD) and machine learning (ML) methods for laboratory image analysis, aiming to manage the restrictions of late leukemia analysis. This study proposes a new Falcon optimization algorithm with deep convolutional neural network for Leukemia detection and classification (FOADCNN-LDC) technique. The main objective of the FOADCNN-LDC technique is to classify and recognize leukemia. The FOADCNN-LDC technique utilizes a median filtering (MF) based noise removal process to eradicate the image noise. Besides, the FOADCNN-LDC technique employs the ShuffleNetv2 model for the feature extraction process. Moreover, the detection and classification of the leukemia process are performed by utilizing the convolutional denoising autoencoder (CDAE) model. The FOA is implemented to select the hyperparameter of the CDAE model. The simulation process of the FOADCNN-LDC approach is performed on a benchmark medical dataset. The investigational analysis of the FOADCNN-LDC approach highlighted a superior accuracy value of 99.62% over existing techniques., (© 2024. The Author(s).)

There is a growing number of publicly available ophthalmic imaging datasets and open-source code for Machine Learning algorithms. This allows ophthalmic researchers and practitioners to independently perform various deep-learning tasks. With the advancement in artificial intelligence (AI) and in the field of imaging, the choice of the most appropriate AI architecture for different tasks will vary greatly. The best-performing AI-dataset combination will depend on the specific problem that needs to be solved and the type of data available. The article discusses different machine learning models and deep learning architectures currently used for various ophthalmic imaging modalities and for different machine learning tasks. It also proposes the most appropriate models based on accuracy and other important factors such as training time, the ability to deploy the model on clinical devices/smartphones, heatmaps that enhance the self-explanatory nature of classification decisions, and the ability to train/adapt on small image datasets to determine if further data collection is worthwhile. The article extensively reviews the existing state-of-the-art AI methods focused on useful machine-learning applications for ophthalmology. It estimates their performance and viability through training and evaluating architectures with different public and private image datasets of different modalities, such as full-color retinal images, OCT images, and 3D OCT scans. The article is expected to benefit the readers by enriching their knowledge of artificial intelligence applied to ophthalmology., (© 2024. The Author(s).)

Atherosclerosis causes heart disease by forming plaques in arterial walls. IVUS imaging provides a high-resolution cross-sectional view of coronary arteries and plaque morphology. Healthcare professionals diagnose and quantify atherosclerosis physically or using VH-IVUS software. Since manual or VH-IVUS software-based diagnosis is time-consuming, automated plaque characterization tools are essential for accurate atherosclerosis detection and classification. Recently, deep learning (DL) and computer vision (CV) approaches are promising tools for automatically classifying plaques on IVUS images. With this motivation, this manuscript proposes an automated atherosclerotic plaque classification method using a hybrid Ant Lion Optimizer with Deep Learning (AAPC-HALODL) technique on IVUS images. The AAPC-HALODL technique uses the faster regional convolutional neural network (Faster RCNN)-based segmentation approach to identify diseased regions in the IVUS images. Next, the ShuffleNet-v2 model generates a useful set of feature vectors from the segmented IVUS images, and its hyperparameters can be optimally selected by using the HALO technique. Finally, an average ensemble classification process comprising a stacked autoencoder (SAE) and deep extreme learning machine (DELM) model can be utilized. The MICCAI Challenge 2011 dataset was used for AAPC-HALODL simulation analysis. A detailed comparative study showed that the AAPC-HALODL approach outperformed other DL models with a maximum accuracy of 98.33%, precision of 97.87%, sensitivity of 98.33%, and F score of 98.10%., (© 2024. International Federation for Medical and Biological Engineering.)

Background: Genome DNA methylation profiling is a promising yet costly method for cancer classification, involving substantial data. We developed an ensemble learning model to identify cancer types using methylation profiles from a limited number of CpG sites., Methods: Analyzing methylation data from 890 samples across 10 cancer types from the TCGA database, we utilized ANOVA and Gain Ratio to select the most significant CpG sites, then employed Gradient Boosting to reduce these to just 100 sites., Results: This approach maintained high accuracy across multiple machine learning models, with classification accuracy rates between 87.7% and 93.5% for methods including Extreme Gradient Boosting, CatBoost, and Random Forest. This method effectively minimizes the number of features needed without losing performance, helping to classify primary organs and uncover subgroups within specific cancers like breast and lung., Conclusions: Using a gradient boosting feature selector shows potential for streamlining methylation-based cancer classification., (© 2024. The Author(s).)

Computational modeling of cardiovascular function has become a critical part of diagnosing, treating and understanding cardiovascular disease. Most strategies involve constructing anatomically accurate computer models of cardiovascular structures, which is a multistep, time-consuming process. To improve the model generation process, we herein present SeqSeg (sequential segmentation): a novel deep learning-based automatic tracing and segmentation algorithm for constructing image-based vascular models. SeqSeg leverages local U-Net-based inference to sequentially segment vascular structures from medical image volumes. We tested SeqSeg on CT and MR images of aortic and aortofemoral models and compared the predictions to those of benchmark 2D and 3D global nnU-Net models, which have previously shown excellent accuracy for medical image segmentation. We demonstrate that SeqSeg is able to segment more complete vasculature and is able to generalize to vascular structures not annotated in the training data., (© 2024. The Author(s).)

Objectives: The current understanding of survival prediction of lung transplant (LTx) patients with systemic sclerosis (SSc) is limited. This study aims to identify novel image features from preoperative chest CT scans associated with post-LTx survival in SSc patients and integrate them into comprehensive prediction models., Materials and Methods: We conducted a retrospective study based on a cohort of SSc patients with demographic information, clinical data, and preoperative chest CT scans who underwent LTx between 2004 and 2020. This cohort consists of 102 patients (mean age, 50 years ± 10, 61% (62/102) females). Five CT-derived body composition features (bone, skeletal muscle, visceral, subcutaneous, and intramuscular adipose tissues) and three CT-derived cardiopulmonary features (heart, arteries, and veins) were automatically computed using 3-D convolutional neural networks. Cox regression was used to identify post-LTx survival factors, generate composite prediction models, and stratify patients based on mortality risk. Model performance was assessed using the area under the receiver operating characteristics curve (ROC-AUC)., Results: Muscle mass ratio, bone density, artery-vein volume ratio, muscle volume, and heart volume ratio computed from CT images were significantly associated with post-LTx survival. Models using only CT-derived features outperformed all state-of-the-art clinical models in predicting post-LTx survival. The addition of CT-derived features improved the performance of traditional models at 1-year, 3-year, and 5-year survival prediction with maximum AUC scores of 0.77 (0.67-0.86), 0.85 (0.77-0.93), and 0.90 (95% CI: 0.83-0.97), respectively., Conclusion: The integration of CT-derived features with demographic and clinical features can significantly improve t post-LTx survival prediction and identify high-risk SSc patients., Key Points: Question What CT features can predict post-lung-transplant survival for SSc patients? Finding CT body composition features such as muscle mass, bone density, and cardiopulmonary volumes significantly predict survival. Clinical relevance Our individualized risk assessment tool can better guide clinicians in choosing and managing patients requiring lung transplant for systemic sclerosis., (© 2024. The Author(s), under exclusive licence to European Society of Radiology.)

Background: The advancements in artificial intelligence and computational power have made deep learning an attractive tool for radiotherapy treatment planning. Deep learning has the potential to significantly simplify the trial-and-error process involved in inverse planning required by modern treatment techniques such as volumetric modulated arc therapy (VMAT). In this study, we explore the ability of deep learning to predict organ-at-risk (OAR) dose-volume histograms (DVHs) of left-sided breast cancer patients undergoing VMAT treatment based solely on their anatomical characteristics. The predicted DVHs could be used to derive patient-specific dose constraints and dose objectives, streamlining the treatment planning process, standardizing the quality of the plans, and personalizing the treatment planning., Purpose: This study aimed to develop a deep learning-based framework for the prediction of organ-specific dose-volume histograms (DVH) based on structures delineated for left-sided breast cancer treatment., Methods: We used a dataset of 249 left-sided breast cancer patients treated with tangential VMAT fields. We extracted delineated structures and dose distributions for each patient and derived slice-by-slice DVHs for planning target volume (PTV) and organs-at-risk. The patients were divided into training (70%, n = 174), validation (10%, n = 24), and test (20%, n = 51) sets. Collected data were used to train a deep learning model for the prediction of the DVHs based on the delineated structures. The developed deep learning model comprised a modified DenseNet architecture followed by a recurrent neural network., Results: In the independent test set (n = 51), the point-wise differences in the slice-by-slice DVHs between the clinical and predicted DVHs were small; the mean squared errors were 3.53, 1.58, 2.28, 3.37, and 1.44 [×10 -4 ] for PTV, heart, ipsilateral lung, contralateral lung, and contralateral breast, respectively. With the derived cumulative DVHs, the mean absolute difference ± standard deviation of mean doses between the clinical and the predicted DVH were 0.08 ± 0.04 Gy, 0.24 ± 0.22 Gy, 0.73 ± 0.46 Gy, 0.07 ± 0.06 Gy, and 0.14 ± 0.14 Gy for PTV, heart, ipsilateral lung, contralateral lung, and contralateral breast, respectively., Conclusions: The deep learning-based approach enabled automatic and reliable prediction of the DVH based on delineated structures. The predicted DVHs could potentially serve as patient-specific clinical goals used to aid treatment planning and avoid suboptimal plans or to derive optimization objectives and constraints for automated treatment planning., (© 2024 The Author(s). Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Kidney diseases pose a significant global health challenge, requiring precise diagnostic tools to improve patient outcomes. This study addresses this need by investigating three main categories of renal diseases: kidney stones, cysts, and tumors. Utilizing a comprehensive dataset of 12,446 CT whole abdomen and urogram images, this study developed an advanced AI-driven diagnostic system specifically tailored for kidney disease classification. The innovative approach of this study combines the strengths of traditional convolutional neural network architecture (AlexNet) with modern advancements in ConvNeXt architectures. By integrating AlexNet's robust feature extraction capabilities with ConvNeXt's advanced attention mechanisms, the paper achieved an exceptional classification accuracy of 99.85%. A key advancement in this study's methodology lies in the strategic amalgamation of features from both networks. This paper concatenated hierarchical spatial information and incorporated self-attention mechanisms to enhance classification performance. Furthermore, the study introduced a custom optimization technique inspired by the Adam optimizer, which dynamically adjusts the step size based on gradient norms. This tailored optimizer facilitated faster convergence and more effective weight updates, imporving model performance. The model of this study demonstrated outstanding performance across various metrics, with an average precision of 99.89%, recall of 99.95%, and specificity of 99.83%. These results highlight the efficacy of the hybrid architecture and optimization strategy in accurately diagnosing kidney diseases. Additionally, the methodology of this paper emphasizes interpretability and explainability, which are crucial for the clinical deployment of deep learning models., (© 2024. The Author(s).)

Objective . The clinical diagnosis of Parkinson's disease (PD) relying on medical history, clinical symptoms, and signs is subjective and lacks sensitivity. Resting-state fMRI (rs-fMRI) has been demonstrated to be an effective biomarker for diagnosing PD. Approach. This study proposes a deep learning approach for the automatic diagnosis of PD using rs-fMRI, named PD-ARnet. Specifically, PD-ARnet utilizes Amplitude of Low Frequency Fluctuations and Regional Homogeneity extracted from rs-fMRI as inputs. The inputs are then processed through a developed dual-branch 3D feature extractor to perform advanced feature extraction. During this process, a Correlation-Driven weighting module is applied to capture complementary information from both features. Subsequently, the Attention-Enhanced fusion module is developed to effectively merge two types of features, and the fused features are input into a fully connected layer for automatic diagnosis classification. Main results. Using 145 samples from the PPMI dataset to evaluate the detection performance of PD-ARnet, the results indicated an average classification accuracy of 91.6% (95% confidence interval [CI]: 90.9%, 92.4%), precision of 94.7% (95% CI: 94.2%, 95.1%), recall of 86.2% (95% CI: 84.9%, 87.4%), F1 score of 90.2% (95% CI: 89.3%, 91.1%), and AUC of 92.8% (95% CI: 91.1%, 95.0%). Significance. The proposed method has the potential to become a clinical auxiliary diagnostic tool for PD, reducing subjectivity in the diagnostic process, and enhancing diagnostic efficiency and consistency., (© 2024 IOP Publishing Ltd. All rights, including for text and data mining, AI training, and similar technologies, are reserved.)

Super-resolution fluorescence imaging has offered unprecedented insights and revolutionized our understanding of biology. In particular, localized plasmonic structured illumination microscopy (LPSIM) achieves video-rate super-resolution imaging with ∼50 nm spatial resolution by leveraging subdiffraction-limited nearfield patterns generated by plasmonic nanoantenna arrays. However, the conventional trial-and-error design process for LPSIM arrays is time-consuming and computationally intensive, limiting the exploration of optimal designs. Here, we propose a hybrid inverse design framework combining deep learning and genetic algorithms to refine LPSIM arrays. A population of designs is evaluated using a trained convolutional neural network, and a multiobjective optimization method optimizes them through iteration and evolution. Simulations demonstrate that the optimized LPSIM substrate surpasses traditional substrates, exhibiting higher reconstruction accuracy, robustness against noise, and increased tolerance for fewer measurements. This framework not only proves the efficacy of inverse design for tailoring LPSIM substrates but also opens avenues for exploring new plasmonic nanostructures in imaging applications.

Rationale and Objectives: To develop and validate a deep learning model for automated pathological grading and prognostic assessment of lung cancer using CT imaging, thereby providing surgeons with a non-invasive tool to guide surgical planning., Material and Methods: This study utilized 572 cases from the National Lung Screening Trial cohort, dividing them randomly into training (461 cases) and internal validation (111 cases) sets in an 8:2 ratio. Additionally, 224 cases from four cohorts obtained from the Cancer Imaging Archive, all diagnosed with non-small cell lung cancer, were included for external validation. The deep learning model, built on the MobileNetV3 architecture, was assessed in both internal and external validation sets using metrics such as accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC). The model's prognostic value was further analyzed using Cox proportional hazards models., Results: The model achieved high accuracy, sensitivity, specificity, and AUC in the internal validation set (accuracy: 0.888, macro AUC: 0.968, macro sensitivity: 0.798, macro specificity: 0.956). External validation demonstrated comparable performance (accuracy: 0.807, macro AUC: 0.920, macro sensitivity: 0.799, macro specificity: 0.896). The model's predicted signatures correlated significantly with patient mortality and provided valuable insights for prognostic assessment (adjusted HR 2.016 [95% CI: 1.010, 4.022])., Conclusions: This study successfully developed and validated a deep learning model for the preoperative grading of lung cancer pathology. The model's accurate predictions could serve as a useful adjunct in treatment planning for lung cancer patients, enabling more effective and customized interventions to improve patient outcomes., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Xiuhua Guo reports financial support was provided by National Natural Science Foundation of China. Xiuhua Guo reports financial support was provided by Beijing Municipal Health Commission. If there are other authors, they 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 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Breast cancer is a common disease that causes great health concerns to women worldwide. During the diagnosis and treatment of breast cancer, medical imaging plays an essential role, but its interpretation relies on radiologists or clinical doctors. Radiomics can extract high-throughput quantitative imaging features from images of various modalities via traditional machine learning or deep learning methods following a series of standard processes. Hopefully, radiomic models may aid various processes in clinical practice. In this review, we summarize the current utilization of radiomics for predicting clinicopathological indices and clinical outcomes. We also focus on radio-multi-omics studies that bridge the gap between phenotypic and microscopic scale information. Acknowledging the deficiencies that currently hinder the clinical adoption of radiomic models, we discuss the underlying causes of this situation and propose future directions for advancing radiomics in breast cancer research., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 The Author(s). Published by Elsevier Inc. All rights reserved.)

Chronic Obstructive Pulmonary Disease (COPD) represents a global public health issue that significantly impairs patients' quality of life and overall health. As one of the primary causes of chronic respiratory diseases and global mortality, effective diagnosis and classification of COPD are crucial for clinical management. Pulmonary function tests (PFTs) are standard for diagnosing COPD, yet their accuracy is influenced by patient compliance and other factors, and they struggle to detect early disease pathologies. Furthermore, the complexity of COPD pathological changes poses additional challenges for clinical diagnosis, increasing the difficulty for physicians in practice. Recently, deep learning (DL) technologies have demonstrated significant potential in medical image analysis, particularly for the diagnosis and classification of COPD. By analyzing key radiological features such as airway alterations, emphysema, and vascular characteristics in Computed Tomography (CT) scan images, DL enhances diagnostic accuracy and efficiency, providing more precise treatment plans for COPD patients. This article reviews the latest research advancements in DL methods based on principal radiological features of COPD for its classification and discusses the advantages, challenges, and future research directions of DL in this field, aiming to provide new perspectives for the personalized management and treatment of COPD., Competing Interests: Declaration of Competing Interest None declared., (Copyright © 2024. Published by Elsevier Ltd.)