<i>"Just Accepted" papers have undergone full peer review and have been accepted for publication in <i>Radiology: Artificial Intelligence</i>. This article will undergo copyediting, layout, and proof review before it is published in its final version. Please note that during production of the final copyedited article, errors may be discovered which could affect the content</i>. Purpose To introduce an open-source deep learning brain segmentation model for fully automated brain MRI segmentation, enabling rapid segmentation and facilitating large-scale neuroimaging research. Materials and Methods In this retrospective study, a deep learning model was developed using a diverse training dataset of 1900 MRI scans (ages 24-93 with a mean of 65 years (SD: 11.5 years) and 1007 females and 893 males) with reference labels generated using a multiatlas segmentation method with human supervision. The final model was validated using 71391 scans from 14 studies. Segmentation quality was assessed using Dice similarity and Pearson correlation coefficients with reference segmentations. Downstream predictive performance for brain age and Alzheimer's disease was evaluated by fitting machine learning models. Statistical significance was assessed using Mann-Whittney U and McNemar's tests. Results The DLMUSE model achieved high correlation (r = 0.93-0.95) and agreement (median Dice scores = 0.84-0.89) with reference segmentations across the testing dataset. Prediction of brain age using DLMUSE features achieved a mean absolute error of 5.08 years, similar to that of the reference method (5.15 years, <i>P</i> = .56). Classification of Alzheimer's disease using DLMUSE features achieved an accuracy of 89% and F1-score of 0.80, which were comparable to values achieved by the reference method (89% and 0.79, respectively). DLMUSE segmentation speed was over 10000 times faster than that of the reference method (3.5 seconds vs 14 hours). Conclusion DLMUSE enabled rapid brain MRI segmentation, with performance comparable to that of state-of-theart methods across diverse datasets. The resulting open-source tools and user-friendly web interface can facilitate large-scale neuroimaging research and wide utilization of advanced segmentation methods. ©RSNA, 2025.

Effective and interpretable classification of medical images is a challenge in computer-aided diagnosis, especially in resource-limited clinical settings. This study introduces spline-based Kolmogorov-Arnold Networks (KANs) for accurate medical image classification with limited, diverse datasets. The models include SBTAYLOR-KAN, integrating B-splines with Taylor series; SBRBF-KAN, combining B-splines with Radial Basis Functions; and SBWAVELET-KAN, embedding B-splines in Morlet wavelet transforms. These approaches leverage spline-based function approximation to capture both local and global nonlinearities. The models were evaluated on brain MRI, chest X-rays, tuberculosis X-rays, and skin lesion images without preprocessing, demonstrating the ability to learn directly from raw data. Extensive experiments, including cross-dataset validation and data reduction analysis, showed strong generalization and stability. SBTAYLOR-KAN achieved up to 98.93% accuracy, with a balanced F1-score, maintaining over 86% accuracy using only 30% of the training data across three datasets. Despite class imbalance in the skin cancer dataset, experiments on both imbalanced and balanced versions showed SBTAYLOR-KAN outperforming other models, achieving 68.22% accuracy. Unlike traditional CNNs, which require millions of parameters (e.g., ResNet50 with 24.18M), SBTAYLOR-KAN achieves comparable performance with just 2,872 trainable parameters, making it more suitable for constrained medical environments. Gradient-weighted Class Activation Mapping (Grad-CAM) was used for interpretability, highlighting relevant regions in medical images. This framework provides a lightweight, interpretable, and generalizable solution for medical image classification, addressing the challenges of limited datasets and data-scarce scenarios in clinical AI applications.

Deep learning has significantly advanced automatic medical diagnostics, releasing human resources from clinical pressure, yet the persistent challenge of data scarcity in this area hampers its further improvements and applications. To address this gap, we introduce a novel ensemble framework called 'Efficient Transfer and Self-supervised Learning based Ensemble Framework' (ETSEF). ETSEF leverages features from multiple pre-trained deep learning models to efficiently learn powerful representations from a limited number of data samples. To the best of our knowledge, ETSEF is the first strategy that combines two pre-training methodologies (Transfer Learning and Self-supervised Learning) with ensemble learning approaches. Various data enhancement techniques, including data augmentation, feature fusion, feature selection, and decision fusion, have also been deployed to maximise the efficiency and robustness of the ETSEF model. Five independent medical imaging tasks, including endoscopy, breast cancer detection, monkeypox detection, brain tumour detection, and glaucoma detection, were tested to demonstrate ETSEF's effectiveness and robustness. Facing limited sample numbers and challenging medical tasks, ETSEF has demonstrated its effectiveness by improving diagnostic accuracy by up to 13.3% compared to strong ensemble baseline models and up to 14.4% compared with recent state-of-the-art methods. Moreover, we emphasise the robustness and trustworthiness of the ETSEF method through various vision-explainable artificial intelligence techniques, including Grad-CAM, SHAP, and t-SNE. Compared to large-scale deep learning models, ETSEF can be flexibly deployed and maintain superior performance for challenging medical imaging tasks, demonstrating potential for application in areas lacking training data. The code is available at Github ETSEF.

Brain extraction is a common preprocessing step when working with intracranial medical imaging data. While several tools exist to automate the preprocessing of magnetic resonance imaging (MRI) of the human brain, none are available for canine MRIs. We present a pipeline mapping separate 2D scans to a 3D image, and a neural network for canine brain extraction. The training dataset consisted of T1-weighted and contrast-enhanced images from 68 dogs of different breeds, all cranial conformations (mesaticephalic, dolichocephalic, brachycephalic), with several pathological conditions, taken at three institutions. Testing was performed on a similarly diverse group of 10 dogs with images from a 4th institution. The model achieved excellent results in terms of Dice ([Formula: see text]) and Jaccard ([Formula: see text]) metrics and generalised well across different MRI scanners, the three aforementioned skull types, and variations in head size and breed. The pipeline was effective for a combination of one to three acquisition planes (i.e., transversal, dorsal, and sagittal). Aside from the T1 weighted imaging training datasets, the model also performed well on other MRI sequences with Jaccardian indices and median Dice scores ranging from 0.86 to 0.89 and 0.92 to 0.94, respectively. Our approach was robust for automated brain extraction. Variations in canine anatomy and performance degradation in multi-scanner data can largely be mitigated through normalisation and augmentation techniques. Brain extraction, as a preprocessing step, can improve the accuracy of an algorithm for abnormality classification in MRI image slices.

The rapid advancement of artificial intelligence (AI) in healthcare imaging has revolutionized diagnostic medicine and clinical decision-making processes. This work presents an intelligent multimodal framework for medical image analysis that leverages Vision-Language Models (VLMs) in healthcare diagnostics. The framework integrates Google Gemini 2.5 Flash for automated tumor detection and clinical report generation across multiple imaging modalities including CT, MRI, X-ray, and Ultrasound. The system combines visual feature extraction with natural language processing to enable contextual image interpretation, incorporating coordinate verification mechanisms and probabilistic Gaussian modeling for anomaly distribution. Multi-layered visualization techniques generate detailed medical illustrations, overlay comparisons, and statistical representations to enhance clinical confidence, with location measurement achieving 80 pixels average deviation. Result processing utilizes precise prompt engineering and textual analysis to extract structured clinical information while maintaining interpretability. Experimental evaluations demonstrated high performance in anomaly detection across multiple modalities. The system features a user-friendly Gradio interface for clinical workflow integration and demonstrates zero-shot learning capabilities to reduce dependence on large datasets. This framework represents a significant advancement in automated diagnostic support and radiological workflow efficiency, though clinical validation and multi-center evaluation are necessary prior to widespread adoption.

This study aims to provide a comprehensive comparison of the performance and reproducibility of two commercially available artificial intelligence (AI) software computer-aided triage and notification solutions, Vendor A (Aidoc) and Vendor B (Viz.ai), for the detection of intracranial hemorrhage (ICH) on non-contrast enhanced head CT (NCHCT) scans performed within a single academic institution. The retrospective analysis was conducted on a large patient cohort from multiple healthcare settings within a single academic institution, utilizing standardized scanning protocols. Sensitivity, specificity, false positive, and false negative rates were evaluated for both vendors. Outputs assessed included AI-generated case-level classification. Among 4,081 scans, 595 were positive for ICH. Vendor A demonstrated a sensitivity of 94.4% and specificity of 97.4%, PPV of 85.9%, and NPV of 99.1%. Vendor B showed a sensitivity of 59.5% and specificity of 99.0%, PPV of 90.0%, and NPV of 92.6%. Vendor A had 20 false negatives, which primarily involved subdural and intraparenchymal hemorrhages, and 97 false positives, which appear to be related to motion artifact. Vendor B had 145 false negatives, largely comprised of subdural and subarachnoid hemorrhages, and 36 false positives, which appeared to be related to motion artifact and calcified or dense lesions. Concordantly, 18 cases were false negatives and 11 cases were false positives for both AI solutions. The findings of this study provide valuable information for clinicians and healthcare institutions considering the implementation of AI software for computer aided-triage and notification in the detection of intracranial hemorrhage. The discussion encompasses the implications of the results, the importance of evaluating AI findings in context-especially in the absence of explainability tools, potential areas for improvement, and the relevance of standardized scanning protocols in ensuring the reliability of AI-based diagnostic tools in clinical practice. ICH = Intracranial Hemorrhage; NCHCT = Non-contrast Enhanced Head CT; AI = Artificial Intelligence; SDH = Subdural Hemorrhage; SAH = Subarachnoid Hemorrhage; IPH = Intraparenchymal Hemorrhage; IVH = Intraventricular Hemorrhage; PPV = Positive Predictive Value; NPV = Negative Predictive Value; CADt = Computer-Aided Triage; PACS = Picture Archiving and Communication System; FN = False Negative; FP = False Positive; CI = Confidence Interval.

Congenital heart diseases (CHD) are a significant cause of neonatal mortality and morbidity. Detecting these abnormalities during pregnancy increases survival rates, enhances prognosis, and improves pregnancy management and quality of life for the affected families. Foetal echocardiography can be considered an accurate method for detecting CHDs. However, the detection of CHDs can be limited by factors such as the sonographer's skill, expertise and patient specific variables. Using artificial intelligence (AI) has the potential to address these challenges, increasing antenatal CHD detection during prenatal care. A scoping review was conducted using Google Scholar, PubMed, and ScienceDirect databases, employing keywords, Boolean operators, and inclusion and exclusion criteria to identify peer-reviewed studies. Thematic mapping and synthesis of the found literature were conducted to review key concepts, research methods and findings. A total of n = 233 articles were identified, after exclusion criteria, the focus was narrowed to n = 7 that met the inclusion criteria. Themes in the literature identified the potential of AI to assist clinicians and trainees, alongside emerging new ethical limitations in ultrasound imaging. AI-based tools in ultrasound imaging offer great potential in assisting sonographers and doctors with decision-making in CHD diagnosis. However, due to the paucity of data and small sample sizes, further research and technological advancements are needed to improve reliability and integrate AI into routine clinical practice. This scoping review identified the reported accuracy and limitations of AI-based tools within foetal cardiac ultrasound imaging. AI has the potential to aid in reducing missed diagnoses, enhance training, and improve pregnancy management. There is a need to understand and address the ethical and legal considerations involved with this new paradigm in imaging.

Cardiovascular risk is underassessed in women. Many women undergo screening mammography in midlife when the risk of cardiovascular disease rises. Mammographic features such as breast arterial calcification and tissue density are associated with cardiovascular risk. We developed and tested a deep learning algorithm for cardiovascular risk prediction based on routine mammography images. Lifepool is a cohort of women with at least one screening mammogram linked to hospitalisation and death databases. A deep learning model based on DeepSurv architecture was developed to predict major cardiovascular events from mammography images. Model performance was compared against standard risk prediction models using the concordance index, comparative to the Harrells C-statistic. There were 49 196 women included, with a median follow-up of 8.8 years (IQR 7.7-10.6), among whom 3392 experienced a first major cardiovascular event. The DeepSurv model using mammography features and participant age had a concordance index of 0.72 (95% CI 0.71 to 0.73), with similar performance to modern models containing age and clinical variables including the New Zealand 'PREDICT' tool and the American Heart Association 'PREVENT' equations. A deep learning algorithm based on only mammographic features and age predicted cardiovascular risk with performance comparable to traditional cardiovascular risk equations. Risk assessments based on mammography may be a novel opportunity for improving cardiovascular risk screening in women.

Lung transplantation remains a critical treatment for end-stage lung diseases, yet it continues to have 1 of the lowest survival rates among solid organ transplants. Despite its life-saving potential, the field faces several challenges, including organ shortages, suboptimal donor matching, and post-transplant complications. The rapidly advancing field of artificial intelligence (AI) offers significant promise in addressing these challenges. Traditional statistical models, such as linear and logistic regression, have been used to predict post-transplant outcomes but struggle to adapt to new trends and evolving data. In contrast, machine learning algorithms can evolve with new data, offering dynamic and updated predictions. AI holds the potential to enhance lung transplantation at multiple stages. In the pre-transplant phase, AI can optimize waitlist management, refine donor selection, and improve donor-recipient matching, and enhance diagnostic imaging by harnessing vast datasets. Post-transplant, AI can help predict allograft rejection, improve immunosuppressive management, and better forecast long-term patient outcomes, including quality of life. However, the integration of AI in lung transplantation also presents challenges, including data privacy concerns, algorithmic bias, and the need for external clinical validation. This review explores the current state of AI in lung transplantation, summarizes key findings from recent studies, and discusses the potential benefits, challenges, and ethical considerations in this rapidly evolving field, highlighting future research directions.

Large language models (LLMs), such as ChatGPT and Gemini, are increasingly being used in medical domains, including dental diagnostics. Despite advancements in image-based deep learning systems, LLM diagnostic capabilities in oral and maxillofacial surgery (OMFS) for processing multi-modal imaging inputs remain underexplored. Radiolucent jaw lesions represent a particularly challenging diagnostic category due to their varied presentations and overlapping radiographic features. This study evaluated diagnostic performance of ChatGPT 4o and Gemini 2.5 Pro using real-world OMFS radiolucent jaw lesion cases, presented in multiple-choice (MCQ) and short-answer (SAQ) formats across 3 imaging conditions: panoramic radiography only, panoramic + CT, and panoramic + CT + pathology. Data from 100 anonymized patients at Wonkwang University Daejeon Dental Hospital were analyzed, including demographics, panoramic radiographs, CBCT images, histopathology slides, and confirmed diagnoses. Sample size was determined based on institutional case availability and statistical power requirements for comparative analysis. ChatGPT and Gemini diagnosed each case under 6 conditions using 3 imaging modalities (P, P+C, P+C+B) in MCQ and SAQ formats. Model accuracy was scored against expert-confirmed diagnoses by 2 independent evaluators. McNemar's and Cochran's Q tests evaluated statistical differences across models and imaging modalities. For MCQ tasks, ChatGPT achieved 66%, 73%, and 82% accuracies across the P, P+C, and P+C+B conditions, respectively, while Gemini achieved 57%, 62%, and 63%, respectively. In SAQ tasks, ChatGPT achieved 34%, 45%, and 48%; Gemini achieved 15%, 24%, and 28%, respectively. Accuracy improved significantly with additional imaging data for ChatGPT; ChatGPT consistently outperformed Gemini across all conditions (P < .001 for MCQ; P = .008 to < .001 for SAQ). MCQ format, which incorporates a human-in-the-loop (HITL) structure, showed higher overall performance than SAQ. ChatGPT demonstrated superior diagnostic performance compared to Gemini in OMFS diagnostic tasks when provided with richer multimodal inputs. Diagnostic accuracy increased with additional imaging data, especially in MCQ formats, suggesting LLMs can effectively synthesize radiographic and pathological data. LLMs have potential as diagnostic support tools for OMFS, especially in settings with limited specialist access. Presenting clinical cases in structured formats using curated imaging data enhances LLM accuracy and underscores HITL integration. Although current LLMs show promising results, further validation using larger datasets and hybrid AI systems are necessary for broader contextualised, clinical adoption.