PurposeThis study aims to explore whether ChatGPT can serve as an assistive tool for neuroradiologists in establishing a reasonable differential diagnosis in central nervous system tumors based on MRI images characteristics. MethodsThis retrospective study included 50 patients aged 18-90 who underwent imaging and surgery at the Western Galilee Medical Center. ChatGPT was provided with demographic and radiological information of the patients to generate differential diagnoses. We compared ChatGPTs performance to an experienced neuroradiologist, using pathological reports as the gold standard. Quantitative data were described using means and standard deviations, median and range. Qualitative data were described using frequencies and percentages. The level of agreement between examiners (neuroradiologist versus ChatGPT) was assessed using Fleiss kappa coefficient. A significance value below 5% was considered statistically significant. Statistical analysis was performed using IBM SPSS Statistics, version 27. ResultsThe results showed that while ChatGPT demonstrated good performance, particularly in identifying common tumors such as glioblastoma and meningioma, its overall accuracy (48%) was lower than that of the neuroradiologist (70%). The AI tool showed moderate agreement with the neuroradiologist (kappa = 0.445) and with pathology results (kappa = 0.419). ChatGPTs performance varied across tumor types, performing better with common tumors but struggling with rarer ones. ConclusionThis study suggests that ChatGPT has the potential to serve as an assistive tool in neuroradiology for establishing a reasonable differential diagnosis in central nervous system tumors based on MRI images characteristics. However, its limitations and potential risks must be considered, and it should therefore be used with caution.

Alzheimer's Disease (AD) and related dementias are significant global health issues characterized by progressive cognitive decline and memory loss. Computer-aided systems can help physicians in the early and accurate detection of AD, enabling timely intervention and effective management. This study presents a combination of two parallel Convolutional Neural Networks (CNNs) and an ensemble learning method for classifying AD stages using Magnetic Resonance Imaging (MRI) data. Initially, these images were resized and augmented before being input into Network 1 and Network 2, which have different structures and layers to extract important features. These features were then fused and fed into an ensemble learning classifier containing Support Vector Machine, Random Forest, and K-Nearest Neighbors, with hyperparameters optimized by the Grid Search Cross-Validation technique. Considering distinct Network 1 and Network 2 along with ensemble learning, four classes were identified with accuracies of 95.16% and 97.97%, respectively. However, using the derived features from both networks resulted in an acceptable classification accuracy of 99.06%. These findings imply the potential of the proposed hybrid approach in the classification of AD stages. As the evaluation was conducted at the slice-level using a Kaggle dataset, additional subject-level validation and clinical testing are required to determine its real-world applicability.

Accurately distinguishing the different molecular subtypes of 2021 World Health Organization (WHO) grade 4 Central Nervous System (CNS) gliomas is highly relevant for prognostic stratification and personalized treatment. To develop and validate a machine learning (ML) model using multiparametric MRI for the preoperative differentiation of astrocytoma, CNS WHO grade 4, and glioblastoma (GBM), isocitrate dehydrogenase-wild-type (IDH-wt) (WHO 2021) (Task 1:grade 4 vs. GBM); and to stratify astrocytoma, CNS WHO grade 4, by distinguish astrocytoma, IDH-mutant (IDH-mut), CNS WHO grade 4 from astrocytoma, IDH-wild-type (IDH-wt), CNS WHO grade 4 (Task 2:IDH-mut ^{grade 4} vs. IDH-wt ^{grade 4}). Additionally, to evaluate the model's prognostic value. We retrospectively analyzed 320 glioma patients from three hospitals (training/testing, 7:3 ratio) and 99 patients from ‌The Cancer Genome Atlas (TCGA) database for external validation‌. Radiomic features were extracted from tumor and edema on contrast-enhanced T1-weighted imaging (CE-T1WI) and T2 fluid-attenuated inversion recovery (T2-FLAIR). Extreme gradient boosting (XGBoost) was utilized for constructing the ML, clinical, and combined models. Model performance was evaluated with receiver operating characteristic (ROC) curves, decision curves, and calibration curves. Stability was evaluated using six additional classifiers. Kaplan-Meier (KM) survival analysis and the log-rank test assessed the model's prognostic value. In Task 1 and Task 2, the combined model (AUC = 0.907, 0.852 and 0.830 for Task 1; AUC = 0.899, 0.895 and 0.792 for Task 2) and the optimal ML model (AUC = 0.902, 0.854 and 0.832 for Task 1; AUC = 0.904, 0.899 and 0.783 for Task 2) significantly outperformed the clinical model (AUC = 0.671, 0.656, and 0.543 for Task 1; AUC = 0.619, 0.605 and 0.400 for Task 2) in both the training, testing and validation sets. Survival analysis showed the combined model performed similarly to molecular subtype in both tasks (p = 0.964 and p = 0.746). The multiparametric MRI ML model effectively distinguished astrocytoma, CNS WHO grade 4 from GBM, IDH-wt (WHO 2021) and differentiated astrocytoma, IDH-mut from astrocytoma, IDH-wt, CNS WHO grade 4. Additionally, the model provided reliable survival stratification for glioma patients across different molecular subtypes.

Brain tumor segmentation plays a critical role in clinical diagnosis and treatment planning, yet the variability in imaging quality across different MRI scanners presents significant challenges to model generalization. To address this, we propose the Edge Iterative MRI Lesion Localization System (EdgeIMLocSys), which integrates Continuous Learning from Human Feedback to adaptively fine-tune segmentation models based on clinician feedback, thereby enhancing robustness to scanner-specific imaging characteristics. Central to this system is the Graph-based Multi-Modal Interaction Lightweight Network for Brain Tumor Segmentation (GMLN-BTS), which employs a Modality-Aware Adaptive Encoder (M2AE) to extract multi-scale semantic features efficiently, and a Graph-based Multi-Modal Collaborative Interaction Module (G2MCIM) to model complementary cross-modal relationships via graph structures. Additionally, we introduce a novel Voxel Refinement UpSampling Module (VRUM) that synergistically combines linear interpolation and multi-scale transposed convolutions to suppress artifacts while preserving high-frequency details, improving segmentation boundary accuracy. Our proposed GMLN-BTS model achieves a Dice score of 85.1% on the BraTS2017 dataset with only 4.58 million parameters, representing a 98% reduction compared to mainstream 3D Transformer models, and significantly outperforms existing lightweight approaches. This work demonstrates a synergistic breakthrough in achieving high-accuracy, resource-efficient brain tumor segmentation suitable for deployment in resource-constrained clinical environments.

Objective: This study aims to support early diagnosis of Alzheimer's disease and detection of amyloid accumulation by leveraging the microstructural information available in multi-shell diffusion MRI (dMRI) data, using a vision transformer-based deep learning framework. Methods: We present a classification pipeline that employs the Swin Transformer, a hierarchical vision transformer model, on multi-shell dMRI data for the classification of Alzheimer's disease and amyloid presence. Key metrics from DTI and NODDI were extracted and projected onto 2D planes to enable transfer learning with ImageNet-pretrained models. To efficiently adapt the transformer to limited labeled neuroimaging data, we integrated Low-Rank Adaptation. We assessed the framework on diagnostic group prediction (cognitively normal, mild cognitive impairment, Alzheimer's disease dementia) and amyloid status classification. Results: The framework achieved competitive classification results within the scope of multi-shell dMRI-based features, with the best balanced accuracy of 95.2% for distinguishing cognitively normal individuals from those with Alzheimer's disease dementia using NODDI metrics. For amyloid detection, it reached 77.2% balanced accuracy in distinguishing amyloid-positive mild cognitive impairment/Alzheimer's disease dementia subjects from amyloid-negative cognitively normal subjects, and 67.9% for identifying amyloid-positive individuals among cognitively normal subjects. Grad-CAM-based explainability analysis identified clinically relevant brain regions, including the parahippocampal gyrus and hippocampus, as key contributors to model predictions. Conclusion: This study demonstrates the promise of diffusion MRI and transformer-based architectures for early detection of Alzheimer's disease and amyloid pathology, supporting biomarker-driven diagnostics in data-limited biomedical settings.

Precise segmentation of brain tumors from magnetic resonance imaging (MRI) is essential for neuro-oncology diagnosis and treatment planning. Despite advances in deep learning methods, automatic segmentation remains challenging due to tumor morphological heterogeneity and complex three-dimensional spatial relationships. Current techniques primarily rely on visual features extracted from MRI sequences while underutilizing semantic knowledge embedded in medical reports. This research presents a multi-level fusion architecture that integrates pixel-level, feature-level, and semantic-level information, facilitating comprehensive processing from low-level data to high-level concepts. The semantic-level fusion pathway combines the semantic understanding capabilities of Contrastive Language-Image Pre-training (CLIP) models with the spatial feature extraction advantages of 3D U-Net through three mechanisms: 3D-2D semantic bridging, cross-modal semantic guidance, and semantic-based attention mechanisms. Experimental validation on the BraTS 2020 dataset demonstrates that the proposed model achieves an overall Dice coefficient of 0.8567, representing a 4.8% improvement compared to traditional 3D U-Net, with a 7.3% Dice coefficient increase in the clinically important enhancing tumor (ET) region.

Reliable biomarkers are essential for tracking disease progression and advancing treatments for multiple system atrophy (MSA). In this study, we propose the MSA Atrophy Index (MSA-AI), a novel composite volumetric measure to distinguish MSA from related disorders and monitor disease progression. Seventeen participants with an initial diagnosis of probable MSA were enrolled in the longitudinal bioMUSE study and underwent 3T MRI, biofluid analysis, and clinical assessments at baseline, 6, and 12 months. Final diagnoses were determined after 12 months using clinical progression, imaging, and fluid biomarkers. Ten participants retained an MSA diagnosis, while five were reclassified as either Parkinson disease (PD, n = 4) or dementia with Lewy bodies (DLB, n = 1). Cross-sectional comparisons included additional MSA cases (n = 26), healthy controls (n = 23), pure autonomic failure (n = 23), PD (n = 56), and DLB (n = 8). Lentiform nucleus, cerebellum, and brainstem volumes were extracted using deep learning-based segmentation. Z-scores were computed using a normative dataset (n = 469) and integrated into the MSA-AI. Group differences were tested with linear regression; longitudinal changes and clinical correlations were assessed using mixed-effects models and Spearman correlations. MSA patients exhibited significantly lower MSA-AI scores compared to all other diagnostic groups (p < 0.001). The MSA-AI effectively distinguished MSA from related synucleinopathies, correlated with baseline clinical severity (ρ = -0.57, p < 0.001), and predicted disease progression (ρ = -0.55, p = 0.03). Longitudinal reductions in MSA-AI were associated with worsening clinical scores over 12 months (ρ = -0.61, p = 0.01). The MSA-AI is a promising imaging biomarker for diagnosis and monitoring disease progression in MSA. These findings require validation in larger, independent cohorts.

Stroke is one of the leading causes of death globally, making early and accurate diagnosis essential for improving patient outcomes, particularly in emergency settings where timely intervention is critical. CT scans are the key imaging modality because of their speed, accessibility, and cost-effectiveness. This study proposed an artificial intelligence framework for multiclass stroke classification (ischemic, hemorrhagic, and no stroke) using CT scan images from a dataset provided by the Republic of Turkey's Ministry of Health. The proposed method adopted MaxViT, a state-of-the-art Vision Transformer, as the primary deep learning model for image-based stroke classification, with additional transformer variants (vision transformer, transformer-in-transformer, and ConvNext). To enhance model generalization and address class imbalance, we applied data augmentation techniques, including synthetic image generation. The MaxViT model trained with augmentation achieved the best performance, reaching an accuracy and F1-score of 98.00%, outperforming all other evaluated models and the baseline methods. The primary goal of this study was to distinguish between stroke types with high accuracy while addressing crucial issues of transparency and trust in artificial intelligence models. To achieve this, Explainable Artificial Intelligence (XAI) was integrated into the framework, particularly Grad-CAM++. It provides visual explanations of the model's decisions by highlighting relevant stroke regions in the CT scans and establishing an accurate, interpretable, and clinically applicable solution for early stroke detection. This research contributed to the development of a trustworthy AI-assisted diagnostic tool for stroke, facilitating its integration into clinical practice and enhancing access to timely and optimal stroke diagnosis in emergency departments, thereby saving more lives.

Cognitive impairment is a key contributor to disability and poor outcomes in schizophrenia, yet it is not adequately addressed by currently available treatments. Thus, it is important to search for preventable or treatable risk factors for cognitive impairment. Here, we hypothesized that obesity-related neurostructural alterations will be associated with worse cognitive outcomes in people with first episode of psychosis (FEP). This observational study presents cross-sectional data from the Early-Stage Schizophrenia Outcome project. We acquired T1-weighted 3D MRI scans in 440 participants with FEP at the time of the first hospitalization and in 257 controls. Metabolic assessments included body mass index (BMI), waist-to-hip ratio (WHR), serum concentrations of triglycerides, cholesterol, glucose, insulin, and hs-CRP. We chose machine learning-derived brain age gap estimate (BrainAGE) as our measure of neurostructural changes and assessed attention, working memory and verbal learning using Digit Span and the Auditory Verbal Learning Test. Among obesity/metabolic markers, only WHR significantly predicted both, higher BrainAGE (t(281)=2.53, p=0.012) and worse verbal learning (t(290) = -2.51, P = .026). The association between FEP and verbal learning was partially mediated by BrainAGE (average causal mediated effects, ACME = -0.04 [-0.10, -0.01], P = .022) and the higher BrainAGE in FEP was partially mediated by higher WHR (ACME = 0.08 [0.02, 0.15], P = .006). Central obesity-related brain alterations were linked with worse cognitive performance already early in the course of psychosis. These structure-function links suggest that preventing or treating central obesity could target brain and cognitive impairments in FEP.

Foundation models like CLIP and SAM have transformed computer vision and medical imaging via low-shot transfer learning. However, deployment of these models hindered by two key challenges: \textit{distribution shift} between training and test data, and \textit{confidence misalignment} that leads to overconfident incorrect predictions. These issues manifest differently in vision-language classification and medical segmentation tasks, yet existing solutions remain domain-specific. We propose \textit{StaRFM}, a unified framework addressing both challenges. It introduces a Fisher information penalty (FIP), extended to 3D medical data via patch-wise regularization, to reduce covariate shift in CLIP and SAM embeddings. Additionally, a confidence misalignment penalty (CMP), reformulated for voxel-level predictions, calibrates uncertainty in segmentation tasks. We theoretically derive PAC-Bayes bounds showing FIP controls generalization via the Fisher-Rao norm, while CMP minimizes calibration error through Brier score optimization. StaRFM shows consistent performance like \texttt{+}3.5\% accuracy and 28\% lower ECE on 19 vision datasets (e.g., ImageNet, Office-Home), 84.7\% DSC and 4.8mm HD95 in medical segmentation (e.g., BraTS, ATLAS), and 40\% lower cross-domain performance gap compared to prior benchmarking methods. The framework is plug-and-play, requiring minimal architectural changes for seamless integration with foundation models. Code and models will be released at https://anonymous.4open.science/r/StaRFM-C0CD/README.md