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Craniospinal irradiation (CSI) is a vital therapeutic approach utilized for young patients suffering from central nervous system disorders such as medulloblastoma. The task of accurately outlining the treatment area is particularly time-consuming due to the presence of several sensitive organs at risk (OAR) that can be affected by radiation. This study aimed to assess two different methods for automating the segmentation process: an atlas technique and a deep learning neural network approach. Additionally, a novel method was devised to prospectively evaluate the accuracy of automated segmentation as a knowledge-based quality assurance (QA) tool. Involving a patient cohort of 100, ranging in ages from 2 to 25 years with a median age of 8, this study employed quantitative metrics centered around overlap and distance calculations to determine the most effective approach for practical clinical application. The contours generated by two distinct methods of atlas and neural network were compared to ground truth contours approved by a radiation oncologist, utilizing 13 distinct metrics. Furthermore, an innovative QA tool was conceptualized, designed for forthcoming cases based on the baseline dataset of 100 patient cases. The calculated metrics indicated that, in the majority of cases (60.58%), the neural network method demonstrated a notably higher alignment with the ground truth. Instances where no difference was observed accounted for 31.25%, while utilization of the atlas method represented 8.17%. The QA tool results showed that the two approaches achieved 100% agreement in 39.4% of instances for the atlas method and in 50.6% of instances for the neural network auto-segmentation. The results indicate that the neural network approach showcases superior performance, and its significantly closer physical alignment to ground truth contours in the majority of cases. The metrics derived from overlap and distance measurements have enabled clinicians to discern the optimal choice for practical clinical application. [ABSTRACT FROM AUTHOR]

Background: Posterior capsular opacification (PCO) is a common complication following cataract surgery that leads to visual disturbances and decreased quality of vision. The aim of our study was to employ a machine‐learning methodology to characterize and validate enhancements applied to the grey‐level co‐occurrence matrix (GLCM) while assessing its validity in comparison to clinical evaluations for evaluating PCO. Methods: One hundred patients diagnosed with age‐related cataracts who were scheduled for phacoemulsification surgery were included in the study. Following mydriasis, anterior segment photographs were captured using a high‐resolution photographic system. The GLCM was utilized as the feature extractor, and a supported vector machine as the regressor. Three variations, namely, GLCM, GLCM+C (+axial information), and GLCM+V (+regional voting), were analyzed. The reference value for regression was determined by averaging clinical scores obtained through subjective analysis. The relationships between the predicted PCO outcome scores and the ground truth were assessed using Pearson correlation analysis and a Bland–Altman plot, while agreement between them was assessed through the Bland–Altman plot. Results: Relative to the ground truth, the GLCM, GLCM+C, and GLCM+V methods exhibited correlation coefficients of 0.706, 0.768, and 0.829, respectively. The relationship between the PCO score predicted by the GLCM+V method and the ground truth was statistically significant (p < 0.001). Furthermore, the GLCM+V method demonstrated competitive performance comparable to that of two experienced clinicians (r = 0.825, 0.843) and superior to that of two junior clinicians (r = 0.786, 0.756). Notably, a high level of agreement was observed between predictions and the ground truth, without significant evidence of proportional bias (p > 0.05). Conclusions: Overall, our findings suggest that a machine‐learning approach incorporating the GLCM, specifically the GLCM+V method, holds promise as an objective and reliable tool for assessing PCO progression. Further studies in larger patient cohorts are warranted to validate these findings and explore their potential clinical applications. [ABSTRACT FROM AUTHOR]

BACKGROUND: In clinical medicine, low-dose radiographic image noise reduces the quality of the detected image features and may have a negative impact on disease diagnosis. OBJECTIVE: In this study, Adaptive Projection Network (APNet) is proposed to reduce noise from low-dose medical images. METHODS: APNet is developed based on an architecture of the U-shaped network to capture multi-scale data and achieve end-to-end image denoising. To adaptively calibrate important features during information transmission, a residual block of the dual attention method throughout the encoding and decoding phases is integrated. A non-local attention module to separate the noise and texture of the image details by using image adaptive projection during the feature fusion. RESULTS: To verify the effectiveness of APNet, experiments on lung CT images with synthetic noise are performed, and the results demonstrate that the proposed approach outperforms recent methods in both quantitative index and visual quality. In addition, the denoising experiment on the dental CT image is also carried out and it verifies that the network has a certain generalization. CONCLUSIONS: The proposed APNet is an effective method that can reduce image noise and preserve the required image details in low-dose radiographic images. [ABSTRACT FROM AUTHOR]

Background: Antiangiogenic therapies for glioblastoma (GBM) such as bevacizumab (BVZ), have been unable to extend survival in large patient cohorts. However, a subset of patients having angiogenesis-dependent tumors might benefit from these therapies. Currently, there are no biomarkers allowing to discriminate responders from non-responders before the start of the therapy. Methods: 40 patients from the randomized GENOM009 study complied the inclusion criteria (quality of images, clinical data available). Of those, 23 patients received first line temozolomide (TMZ) for eight weeks and then concomitant radiotherapy and TMZ. 17 patients received BVZ+TMZ for seven weeks and then added radiotherapy to the treatment. Clinical variables were collected, tumors segmented and several geometrical measures computed including: Contrast enhancing (CE), necrotic, and total volumes; equivalent spherical CE width; several geometric measures of the CE ‘rim’ geometry and a set of image texture measures. The significance of the results was studied using Kaplan-Meier and Cox proportional hazards analysis. Correlations were assessed using Spearman correlation coefficients. Results: Kaplan-Meier and Cox proportional hazards analysis showed that total, CE and inner volume (p = 0.019, HR = 4.258) and geometric heterogeneity of the CE areas (p = 0.011, HR = 3.931) were significant parameters identifying response to BVZ. The group of patients with either regular CE areas (small geometric heterogeneity, median difference survival 15.88 months, p = 0.011) or those with small necrotic volume (median survival difference 14.50 months, p = 0.047) benefited substantially from BVZ. Conclusion: Imaging biomarkers related to the irregularity of contrast enhancing areas and the necrotic volume were able to discriminate GBM patients with a substantial survival benefit from BVZ. A prospective study is needed to validate our results. [ABSTRACT FROM AUTHOR]

The subject of this work is the problem of separation of noise from informative texture elements taking into account special features of the ultrasonic image. A mathematical model is developed that describes statistical and spectral properties of different elements of image texture. A noise-suppression procedure based on the developed mathematical model is implemented. The method passed clinical trials that proved its efficiency. [ABSTRACT FROM AUTHOR]

In medical research and clinical applications, the utilization of MRI datasets from multiple centers has become increasingly prevalent. However, inherent variability between these centers presents challenges due to domain shift, which can impact the quality and reliability of the analysis. Regrettably, the absence of adequate tools for domain shift analysis hinders the development and validation of domain adaptation and harmonization techniques. To address this issue, this paper presents a novel Domain Shift analyzer for MRI (DSMRI) framework designed explicitly for domain shift analysis in multi-center MRI datasets. The proposed model assesses the degree of domain shift within an MRI dataset by leveraging various MRI-quality-related metrics derived from the spatial domain. DSMRI also incorporates features from the frequency domain to capture low- and high-frequency information about the image. It further includes the wavelet domain features by effectively measuring the sparsity and energy present in the wavelet coefficients. Furthermore, DSMRI introduces several texture features, thereby enhancing the robustness of the domain shift analysis process. The proposed framework includes visualization techniques such as t-SNE and UMAP to demonstrate that similar data are grouped closely while dissimilar data are in separate clusters. Additionally, quantitative analysis is used to measure the domain shift distance, domain classification accuracy, and the ranking of significant features. The effectiveness of the proposed approach is demonstrated using experimental evaluations on seven large-scale multi-site neuroimaging datasets. [ABSTRACT FROM AUTHOR]

Background: Age prediction powered by artificial intelligence (AI) can be used as an objective technique to assess the cosmetic effect of rejuvenation surgery. Existing age‐estimation models are trained on public datasets with the Caucasian race as the main reference, thus they are impractical for clinical application in Chinese patients. Methods: To develop and select an age‐estimation model appropriate for Chinese patients receiving rejuvenation treatment, we obtained a face database of 10 529 images from 1821 patients from the author's hospital and selected two representative age‐estimation algorithms for the model training. The prediction accuracies and the interpretability of calculation logic of these two facial age predictors were compared and analyzed. Results: The mean absolute error (MAE) of a traditional support vector machine‐learning model was 10.185 years; the proportion of absolute error ≤6 years was 35.90% and 68.50% ≤12 years. The MAE of a deep‐learning model based on the VGG‐16 framework was 3.011 years; the proportion of absolute error ≤6 years was 90.20% and 100% ≤12 years. Compared with deep learning, traditional machine‐learning models have clearer computational logic, which allows them to give clinicians more specific treatment recommendations. Conclusion: Experimental results show that deep‐learning exceeds traditional machine learning in the prediction of Chinese cosmetic patients' age. Although traditional machine learning model has better interpretability than deep‐learning model, deep‐learning is more accurate for clinical quantitative evaluation. Knowing the decision‐making logic behind the accurate prediction of deep‐learning is crucial for deeper clinical application, and requires further exploration. [ABSTRACT FROM AUTHOR]

Contrast media are widely diffused in biomedical imaging, due to their relevance in the diagnosis of numerous disorders. However, the risk of adverse reactions, the concern of potential damage to sensitive organs, and the recently described brain deposition of gadolinium salts, limit the use of contrast media in clinical practice. In recent years, the application of artificial intelligence (AI) techniques to biomedical imaging has led to the development of 'virtual' and 'augmented' contrasts. The idea behind these applications is to generate synthetic post-contrast images through AI computational modeling starting from the information available on other images acquired during the same scan. In these AI models, non-contrast images (virtual contrast) or low-dose post-contrast images (augmented contrast) are used as input data to generate synthetic post-contrast images, which are often undistinguishable from the native ones. In this review, we discuss the most recent advances of AI applications to biomedical imaging relative to synthetic contrast media. [ABSTRACT FROM AUTHOR]

Objective. In order to better adapt to clinical applications, this paper proposes a cross-validation decision-making fusion method of Vision Transformer and DenseNet161. Methods. The dataset is the most critical acetic acid image for clinical diagnosis, and the SR areas are processed by a specific method. Then, the Vision Transformer and DenseNet161 models are trained by the fivefold cross-validation method, and the fivefold prediction results corresponding to the two models are fused by different weights. Finally, the five fused results are averaged to obtain the category with the highest probability. Results. The results show that the fusion method in this paper reaches an accuracy rate of 68% for the four classifications of cervical lesions. Conclusions. It is more suitable for clinical environments, effectively reducing the missed detection rate and ensuring the life and health of patients. [ABSTRACT FROM AUTHOR]

Single modal medical images provide limited information and cannot reflect all the details of the relevant tissues, which may lead to misdiagnosis in clinical medicine. Therefore, a medical image fusion algorithm based on non-down-sampling shear wave transform (NSST) is proposed. This algorithm fuses multi-modal medical images, enriches the information of fused images, improves the image quality, and provides a basis for clinical diagnosis. Firstly, the low-frequency sub-band and several high-frequency directional sub-bands are obtained by NSST transformation of the source image, and the structural similarity between sub-bands is evaluated. Then, according to the characteristics of low-frequency sub-band images, for sub-images with high similarity, the regional features are obtained by region energy and variance, and the fusion method is based on region feature weighting. For sub-images with low similarity, two images to be fused are input into the LSCN model respectively by fusing the connection items of the improved LSCN model. The improved L-term replaces the ignition frequency in the traditional PCNN as the output. According to the characteristics of high frequency sub-images, the fusion rule of combining visual sensitivity coefficient and regional energy is adopted for sub-images with high similarity. For sub-images with low similarity, an improved guided filter is used to fuse the sub-images in order to maintain the clear edges of the images. Finally, the image is reconstructed by inverse NSST transform. The experimental results show that the proposed algorithm can obtain better fusion effects in both objective and subjective evaluation. The obtained fusion image has rich information, excellent edge retention characteristics, subjectively clear texture and high contrast and good visual effect. [ABSTRACT FROM AUTHOR]

Deep learning (DL) is a relatively newer subdomain of machine learning (ML) with incredible potential for certain applications in the medical field. Given recent advances in its use in neuro-oncology, its role in diagnosing, prognosticating, and managing the care of cancer patients has been the subject of many research studies. The gamut of studies has shown that the landscape of algorithmic methods is constantly improving with each iteration from its inception. With the increase in the availability of high-quality data, more training sets will allow for higher fidelity models. However, logistical and ethical concerns over a prospective trial comparing prognostic abilities of DL and physicians severely limit the ability of this technology to be widely adopted. One of the medical tenets is judgment, a facet of medical decision making in DL that is often missing because of its inherent nature as a "black box." A natural distrust for newer technology, combined with a lack of autonomy that is normally expected in our current medical practices, is just one of several important limitations in implementation. In our review, we will first define and outline the different types of artificial intelligence (AI) as well as the role of AI in the current advances of clinical medicine. We briefly highlight several of the salient studies using different methods of DL in the realm of neuroradiology and summarize the key findings and challenges faced when using this nascent technology, particularly ethical challenges that could be faced by users of DL. [ABSTRACT FROM AUTHOR]

Objective: To evaluate the use of ultrasound coupled with machine learning (ML) and deep learning (DL) techniques for automated or semi-automated classification of myositis. Methods: Eighty subjects comprised of 19 with inclusion body myositis (IBM), 14 with polymyositis (PM), 14 with dermatomyositis (DM), and 33 normal (N) subjects were included in this study, where 3214 muscle ultrasound images of 7 muscles (observed bilaterally) were acquired. We considered three problems of classification including (A) normal vs. affected (DM, PM, IBM); (B) normal vs. IBM patients; and (C) IBM vs. other types of myositis (DM or PM). We studied the use of an automated DL method using deep convolutional neural networks (DL-DCNNs) for diagnostic classification and compared it with a semi-automated conventional ML method based on random forests (ML-RF) and “engineered” features. We used the known clinical diagnosis as the gold standard for evaluating performance of muscle classification. Results: The performance of the DL-DCNN method resulted in accuracies ± standard deviation of 76.2% ± 3.1% for problem (A), 86.6% ± 2.4% for (B) and 74.8% ± 3.9% for (C), while the ML-RF method led to accuracies of 72.3% ± 3.3% for problem (A), 84.3% ± 2.3% for (B) and 68.9% ± 2.5% for (C). Conclusions: This study demonstrates the application of machine learning methods for automatically or semi-automatically classifying inflammatory muscle disease using muscle ultrasound. Compared to the conventional random forest machine learning method used here, which has the drawback of requiring manual delineation of muscle/fat boundaries, DCNN-based classification by and large improved the accuracies in all classification problems while providing a fully automated approach to classification. [ABSTRACT FROM AUTHOR]

Objective: The present work proposes a new clinical approach to TTFields therapy of glioblastoma. The approach combines targeted surgical skull removal (craniectomy) with TTFields therapy to enhance the induced electrical field in the underlying tumor tissue. Using computer simulations, we explore the potential of the intervention to improve the clinical efficacy of TTFields therapy of brain cancer. Methods: We used finite element analysis to calculate the electrical field distribution in realistic head models based on MRI data from two patients: One with left cortical/subcortical glioblastoma and one with deeply seated right thalamic anaplastic astrocytoma. Field strength was assessed in the tumor regions before and after virtual removal of bone areas of varying shape and size (10 to 100 mm) immediately above the tumor. Field strength was evaluated before and after tumor resection to assess realistic clinical scenarios. Results: For the superficial tumor, removal of a standard craniotomy bone flap increased the electrical field strength by 60–70% in the tumor. The percentage of tissue in expected growth arrest or regression was increased from negligible values to 30–50%. The observed effects were highly focal and targeted at the regions of pathology underlying the craniectomy. No significant changes were observed in surrounding healthy tissues. Median field strengths in tumor tissue increased with increasing craniectomy diameter up to 50–70 mm. Multiple smaller burr holes were more efficient than single craniectomies of equivalent area. Craniectomy caused no significant field enhancement in the deeply seated tumor, but rather a focal enhancement in the brain tissue underlying the skull defect. Conclusions: Our results provide theoretical evidence that small and clinically feasible craniectomies may provide significant enhancement of TTFields intensity in cerebral hemispheric tumors without severely compromising brain protection or causing unacceptable heating in healthy tissues. A clinical trial is being planned to validate safety and efficacy. [ABSTRACT FROM AUTHOR]

Purpose: Texture analysis (TA) has proved to be useful to distinguish different tissues and disease states using magnetic resonance imaging (MRI). TA has been successfully applied clinically to improve identification of abnormalities in the brain, liver, and bone and, more recently, has been used to enhance the specificity of breast MRI. This preclinical study used a custom-made phantom containing different grades of reticulated foam embedded in agarose gel to assess the capability of TA to distinguish between different texture objects, under different imaging conditions. The aim was to assess whether TA could be used reliably with clinical protocols that were not optimized for texture analysis and also to investigate the effect that changing imaging sequence parameters would have on the outcome of TA. Methods: Clinical fast gradient echo sequences and two different breast RF coils were used in order to reflect standard clinical practice. Three protocols were used: (1) a high spatial resolution protocol run on a 1.5 Tesla (T) MRI scanner, (2) a parameter matched sequence run on a 3.0 T magnet, and (3) a high temporal resolution protocol also run on a 3.0 T magnet.For each protocol, three sequence parameters (repetition time, bandwidth/echo time, and flip angle) were altered from the baseline values to assess the impact of changes in acquisition parameters on the outcome of TA. Results: TA was performed using MAZDA software and clearly differentiated four foam phantoms when using the wavelet transform method (WAV), also moderately so with the co-occurrence matrix method (COM). The outcome was generally improved for imaging protocols acquired on the 3.0 T scanner, particularly for the high spatial resolution protocol where changes to the acquisition parameters influenced the TA, especially changes to the bandwidth/echo time. For the other protocols, TA outcome was less affected by changes to the imaging parameters. Conclusions: This phantom study shows that acquisition parameters and protocols that are typically used for clinical breast imaging can result in good TA. Our findings suggest that changes to sequence parameters may not greatly influence the outcome of texture analysis, but rather that spatial resolution may be the most important factor to consider. [ABSTRACT FROM AUTHOR]

The article presents abstracts related to Clinical Practice which includes Patients' satisfaction with on-demand sedation for out-patient colonoscopies, Multi-disciplinary evaluation of an intensive care unit enteral feeding algorithm, and Australasian treatment guidelines for the management of pancreatic exocrine insufficiency.

There have been many attempts to further improve and automate cephalometric analysis in order to increase accuracy, reduce errors due to subjectivity, and to provide more efficient use of clinicians’ time. The aim of this research was to evaluate an automated system for landmarking of cephalograms based on the use of an active appearance model (AAM) that contains a statistical model of shape and grey-level appearance of an object of interest and represents both shape and texture variations of the region covered by the model. [ABSTRACT FROM PUBLISHER]