Accurate prediction of stroke risk at an early stage is essential for timely intervention and prevention, especially given the serious health consequences and economic burden that strokes can cause. In this study, we proposed a class-balanced and data-augmented (CBDA-ResNet50) deep learning model to improve the prediction accuracy of the well-known ResNet50 architecture for stroke risk. Our approach uses advanced techniques such as class balancing and data augmentation to address common challenges in medical imaging datasets, such as class imbalance and limited training examples. In most cases, these problems lead to biased or less reliable predictions. To address these issues, the proposed model assures that the predictions are still accurate even when some stroke risk factors are absent in the data. The performance of CBDA-ResNet50 improves by using the Adam optimizer and the ReduceLROnPlateau scheduler to adjust the learning rate. The application of weighted cross entropy removes the imbalance between classes and significantly improves the results. It achieves an accuracy of 97.87% and a balanced accuracy of 98.27%, better than many of the previous best models. This shows that we can make more reliable predictions by combining modern deep-learning models with advanced data-processing techniques. CBDA-ResNet50 has the potential to be a model for early stroke prevention, aiming to improve patient outcomes and reduce healthcare costs.

The histological grade of hepatocellular carcinoma (HCC) is an important factor associated with early tumor recurrence and prognosis after surgery. Developing a valuable tool to assess this grade is essential for treatment. This study aimed to evaluate the feasibility and efficacy of a deep learning-based three-dimensional super-resolution (SR) magnetic resonance imaging radiomics model for predicting the pathological grade of HCC. A total of 197 HCC patients were included and divided into a training cohort (n = 157) and a testing cohort (n = 40). Three-dimensional SR technology based on deep learning was used to obtain SR hepatobiliary phase (HBP) images from normal-resolution (NR) HBP images. High-dimensional quantitative features were extracted from manually segmented volumes of interest in NRHBP and SRHBP images. The gradient boosting, light gradient boosting machine, and support vector machine were used to develop three-class (well-differentiated vs. moderately differentiated vs. poorly differentiated) and binary radiomics (well-differentiated vs. moderately and poorly differentiated) models, and the predictive performance of these models was evaluated using several measures. All the three-class models using SRHBP images had higher area under the curve (AUC) values than those using NRHBP images. The binary classification models developed with SRHBP images also outperformed those with NRHBP images in distinguishing moderately and poorly differentiated HCC from well-differentiated HCC (AUC = 0.849, sensitivity = 77.8%, specificity = 76.9%, accuracy = 77.5% vs. AUC = 0.603, sensitivity = 48.1%, specificity = 76.9%, accuracy = 57.5%; p = 0.039). Decision curve analysis revealed the clinical value of the models. Deep learning-based three-dimensional SR technology may improve the performance of radiomics models using HBP images for predicting the preoperative pathological grade of HCC.

Reduced muscle mass and function are associated with increased morbidity, and mortality. Ultrasound, despite being cost-effective and portable, is still underutilized in muscle trophism assessment due to its reliance on operator expertise and measurement variability. This proof-of-concept study aimed to overcome these limitations by developing a deep learning model that predicts muscle density, as assessed by CT, using Ultrasound data, exploring the feasibility of a novel Ultrasound-based parameter for muscle trophism.A sample of adult participants undergoing CT examination in our institution's emergency department between May 2022 and March 2023 was enrolled in this single-center study. Ultrasound examinations were performed with a L11-3 MHz probe. The rectus abdominis muscles, selected as target muscles, were scanned in the transverse plane, recording an Ultrasound image per side. For each participant, the same operator calculated the average target muscle density in Hounsfield Units from an axial CT slice closely matching the Ultrasound scanning plane.The final dataset included 1090 Ultrasound images from 551 participants (mean age 67 ± 17, 323 males). A deep learning model was developed to classify Ultrasound images into three muscle-density classes based on CT values. The model achieved promising performance, with a categorical accuracy of 70% and AUC values of 0.89, 0.79, and 0.90 across the three classes.This observational study introduces an innovative approach to automated muscle trophism assessment using Ultrasound imaging. Future efforts should focus on external validation in diverse populations and clinical settings, as well as expanding its application to other muscles.

Hydatid cysts, caused by Echinococcus granulosus, form progressively enlarging fluid-filled cysts in organs like the liver and lungs, posing significant public health risks through severe complications or death. This study presents a novel deep feature generation framework utilizing vision transformer models (ViT-DFG) to enhance the classification accuracy of hydatid cyst types. The proposed framework consists of four phases: image preprocessing, feature extraction using vision transformer models, feature selection through iterative neighborhood component analysis, and classification, where the performance of the ViT-DFG model was evaluated and compared across different classifiers such as k-nearest neighbor and multi-layer perceptron (MLP). Both methods were evaluated independently to assess classification performance from different approaches. The dataset, comprising five cyst types, was analyzed for both five-class and three-class classification by grouping the cyst types into active, transition, and inactive categories. Experimental results showed that the proposed VIT-DFG method achieves higher accuracy than existing methods. Specifically, the ViT-DFG framework attained an overall classification accuracy of 98.10% for the three-class and 95.12% for the five-class classifications using 5-fold cross-validation. Statistical analysis through one-way analysis of variance (ANOVA), conducted to evaluate significant differences between models, confirmed significant differences between the proposed framework and individual vision transformer models ( <math xmlns="http://www.w3.org/1998/Math/MathML"><mrow><mi>p</mi> <mo><</mo> <mn>0.05</mn></mrow> </math> ). These results highlight the effectiveness of combining multiple vision transformer architectures with advanced feature selection techniques in improving classification performance. The findings underscore the ViT-DFG framework's potential to advance medical image analysis, particularly in hydatid cyst classification, while offering clinical promise through automated diagnostics and improved decision-making.

Brain tumor resection is a complex procedure with significant implications for patient survival and quality of life. Predictions of patient outcomes provide clinicians and patients the opportunity to select the most suitable onco-functional balance. In this study, global features derived from structural magnetic resonance imaging in a clinical dataset of 49 pre- and post-surgery patients identified potential biomarkers associated with survival outcomes. We propose a framework that integrates Explainable AI (XAI) with neuroimaging-based feature engineering for survival assessment, offering guidance for surgical decision-making. In this study, we introduce a global explanation optimizer that refines survival-related feature attribution in deep learning models, enhancing interpretability and reliability. Our findings suggest that survival is influenced by alterations in regions associated with cognitive and sensory functions, indicating the importance of preserving areas involved in decision-making and emotional regulation during surgery to improve outcomes. The global explanation optimizer improves both fidelity and comprehensibility of explanations compared to state-of-the-art XAI methods. It effectively identifies survival-related variability, underscoring its relevance in precision medicine for brain tumor treatment.

<b>Background:</b> Accurate upfront risk stratification in suspected clinically significant prostate cancer (csPCa) may reduce unnecessary prostate biopsies. Integrating clinical and Magnetic Resonance Imaging (MRI) variables using deep learning could improve prediction. <b>Methods:</b> We retrospectively analysed 538 men who underwent MRI and biopsy between April 2019-September 2024. A fully connected neural network was trained using 5-fold cross-validation. Model 1 included clinical features (age, prostate-specific antigen [PSA], PSA density, digital rectal examination, family history, prior negative biopsy, and ongoing therapy). Model 2 used MRI-derived Prostate Imaging Reporting and Data System (PI-RADS) categories. Model 3 used all previous variables as well as lesion size, location, and prostate volume as determined on MRI. <b>Results:</b> Model 3 achieved the highest area under the receiver operating characteristic curve (AUC = 0.822), followed by Model 2 (AUC = 0.778) and Model 1 (AUC = 0.716). Sensitivities for detecting clinically significant prostate cancer (csPCa) were 87.4%, 91.6%, and 86.8% for Models 1, 2, and 3, respectively. Although Model 3 had slightly lower sensitivity than Model 2, it showed higher specificity, reducing false positives and avoiding 43.4% and 21.2% more biopsies compared to Models 1 and 2. Decision curve analysis showed M2 had the highest net benefit at risk thresholds ≤ 20%, while M3 was superior above 20%. <b>Conclusions:</b> Model 3 improved csPCa risk stratification, particularly in biopsy-averse settings, while Model 2 was more effective in cancer-averse scenarios. These models support personalized, context-sensitive biopsy decisions.

<b>Background:</b> Chest X-rays are rapidly gaining prominence as a prevalent diagnostic tool, as recognized by the World Health Organization (WHO). However, interpreting chest X-rays can be demanding and time-consuming, even for experienced radiologists, leading to potential misinterpretations and delays in treatment. <b>Method:</b> The purpose of this research is the development of a RadAI model. The RadAI model can accurately detect four types of lung abnormalities in chest X-rays and generate a report on each identified abnormality. Moreover, deep learning algorithms, particularly convolutional neural networks (CNNs), have demonstrated remarkable potential in automating medical image analysis, including chest X-rays. This work addresses the challenge of chest X-ray interpretation by fine tuning the following three advanced deep learning models: Feature-selective and Spatial Receptive Fields Network (FSRFNet50), ResNext50, and ResNet50. These models are compared based on accuracy, precision, recall, and F1-score. <b>Results:</b> The outstanding performance of RadAI shows its potential to assist radiologists to interpret the detected chest abnormalities accurately. <b>Conclusions:</b> RadAI is beneficial in enhancing the accuracy and efficiency of chest X-ray interpretation, ultimately supporting the timely and reliable diagnosis of lung abnormalities.

Background and aimsAcute pancreatitis (AP) is a common gastrointestinal disease with rising global incidence. While most cases are mild, severe AP (SAP) carries high mortality. Early and accurate severity prediction is crucial for optimal management. However, existing severity prediction models, such as BISAP and mCTSI, have modest accuracy and often rely on data unavailable at admission. This study proposes a deep learning (DL) model to predict AP severity using abdominal contrast-enhanced CT (CECT) scans acquired within 24 hours of admission. MethodsWe collected 10,130 studies from 8,335 patients across a multi-site U.S. health system. The model was trained in two stages: (1) self-supervised pretraining on large-scale unlabeled CT studies and (2) fine-tuning on 550 labeled studies. Performance was evaluated against mCTSI and BISAP on a hold-out internal test set (n=100 patients) and externally validated on a Hungarian AP registry (n=518 patients). ResultsOn the internal test set, the model achieved AUROCs of 0.888 (95% CI: 0.800-0.960) for SAP and 0.888 (95% CI: 0.819-0.946) for mild AP (MAP), outperforming mCTSI (p = 0.002). External validation showed robust AUROCs of 0.887 (95% CI: 0.825-0.941) for SAP and 0.858 (95% CI: 0.826-0.888) for MAP, surpassing mCTSI (p = 0.024) and BISAP (p = 0.002). Retrospective simulation suggested the models potential to support admission triage and serve as a second reader during CECT interpretation. ConclusionsThe proposed DL model outperformed standard scoring systems for AP severity prediction, generalized well to external data, and shows promise for providing early clinical decision support and improving resource allocation.

Deep neural networks are increasingly applied for automated histopathology. Yet, whole-slide images (WSIs) are often acquired at gigapixel sizes, rendering it computationally infeasible to analyze them entirely at high resolution. Diagnostic labels are largely available only at the slide-level, because expert annotation of images at a finer (patch) level is both laborious and expensive. Moreover, regions with diagnostic information typically occupy only a small fraction of the WSI, making it inefficient to examine the entire slide at full resolution. Here, we propose SASHA -- {\it S}equential {\it A}ttention-based {\it S}ampling for {\it H}istopathological {\it A}nalysis -- a deep reinforcement learning approach for efficient analysis of histopathological images. First, SASHA learns informative features with a lightweight hierarchical, attention-based multiple instance learning (MIL) model. Second, SASHA samples intelligently and zooms selectively into a small fraction (10-20\%) of high-resolution patches, to achieve reliable diagnosis. We show that SASHA matches state-of-the-art methods that analyze the WSI fully at high-resolution, albeit at a fraction of their computational and memory costs. In addition, it significantly outperforms competing, sparse sampling methods. We propose SASHA as an intelligent sampling model for medical imaging challenges that involve automated diagnosis with exceptionally large images containing sparsely informative features.

The success of machine learning algorithms is inherently related to the extraction of meaningful features, as they play a pivotal role in the performance of these algorithms. Central to this challenge is the quality of data representation. However, the ability to generalize and extract these features effectively from unseen datasets is also crucial. In light of this, we introduce a novel concept: the polyrepresentation. Polyrepresentation integrates multiple representations of the same modality extracted from distinct sources, for example, vector embeddings from the Siamese Network, self-supervised models, and interpretable radiomic features. This approach yields better performance metrics compared to relying on a single representation. Additionally, in the context of X-ray images, we demonstrate the transferability of the created polyrepresentation to a smaller dataset, underscoring its potential as a pragmatic and resource-efficient approach in various image-related solutions. It is worth noting that the concept of polyprepresentation on the example of medical data can also be applied to other domains, showcasing its versatility and broad potential impact.