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.

With increasing interest in studying biological systems across spatial scales--from centimetres down to nanometres--histology continues to be the gold standard for tissue imaging at cellular resolution, providing an essential bridge between macroscopic and nanoscopic analysis. However, its inherently destructive and two-dimensional nature limits its ability to capture the full three-dimensional complexity of tissue architecture. Here we show that phase-contrast X-ray microscopy can enable three-dimensional virtual histology with subcellular resolution. This technique provides direct quantification of electron density without restrictive assumptions, allowing for direct characterisation of cellular nuclei in a standard laboratory setting. By combining high spatial resolution and soft tissue contrast, with automated segmentation of cell nuclei, we demonstrated virtual H&E staining using machine learning-based style transfer, yielding volumetric datasets compatible with existing histopathological analysis tools. Furthermore, by integrating electron density and the sensitivity to nanometric features of the dark field contrast channel, we achieve stain-free, high-content imaging capable of distinguishing nuclei and extracellular matrix.

Despite continued advances in oncology, cancer remains a leading cause of global mortality, highlighting the need for diagnostic and prognostic tools that are both accurate and interpretable. Unimodal approaches often fail to capture the biological and clinical complexity of tumors. In this study, we present a suite of task-specific AI models that leverage CT imaging, multi-omics profiles, and structured clinical data to address distinct challenges in segmentation, classification, and prognosis. We developed three independent models across large public datasets. Task 1 applied a 3D U-Net to segment pancreatic tumors from CT scans, achieving a Dice Similarity Coefficient (DSC) of 0.7062. Task 2 employed a hierarchical ensemble of omics-based classifiers to distinguish tumor from normal tissue and classify six major cancer types with 98.67% accuracy. Task 3 benchmarked classical machine learning models on clinical data for prognosis prediction across three cancers (LIHC, KIRC, STAD), achieving strong performance (e.g., C-index of 0.820 in KIRC, AUC of 0.978 in LIHC). Across all tasks, explainable AI methods such as SHAP and attention-based visualization enabled transparent interpretation of model outputs. These results demonstrate the value of tailored, modality-aware models and underscore the clinical potential of applying such tailored AI systems for precision oncology. Technical FoundationsO_LISegmentation (Task 1): A custom 3D U-Net was trained using the Task07_Pancreas dataset from the Medical Segmentation Decathlon (MSD). CT images were preprocessed with MONAI-based pipelines, resampled to (64, 96, 96) voxels, and intensity-windowed to HU ranges of -100 to 240. C_LIO_LIClassification (Task 2): Multi-omics data from TCGA--including gene expression, methylation, miRNA, CNV, and mutation profiles--were log-transformed and normalized. Five modality-specific LightGBM classifiers generated meta-features for a late-fusion ensemble. Stratified 5-fold cross-validation was used for evaluation. C_LIO_LIPrognosis (Task 3): Clinical variables from TCGA were curated and imputed (median/mode), with high-missing-rate columns removed. Survival models (e.g., Cox-PH, Random Forest, XGBoost) were trained with early stopping. No omics or imaging data were used in this task. C_LIO_LIInterpretability: SHAP values were computed for all tree-based models, and attention-based overlays were used in imaging tasks to visualize salient regions. C_LI

PSMA PET/CT imaging has been increasingly utilized in the management of patients with metastatic prostate cancer (mPCa). Imaging biomarkers derived from PSMA PET may provide improved prognostication and prediction of treatment response for mPCa patients. This study investigates a novel deep learning-derived imaging biomarker framework for outcome prediction using multi-modal PSMA PET/CT and clinical features. A single institution cohort of 99 mPCa patients with 396 lesions was evaluated. Imaging features were extracted from cropped lesion areas and combined with clinical variables including body mass index, ECOG performance status, prostate specific antigen (PSA) level, Gleason score, and treatments received. The PSA progression-free survival (PFS) model was trained using a ResNet architecture with a Cox proportional hazards loss function using five-fold cross-validation. Performance was assessed using concordance index (C-index) and Kaplan-Meier survival analysis. Among evaluated model architectures, the ResNet-18 backbone offered the best performance. The multi-modal deep learning framework achieved a 5-fold cross-validation C-index ranging from 0.75 to 0.94, outperforming models incorporating imaging only (0.70-0.89) and clinical features only (0.53-0.65). Kaplan-Meir survival analysis performed on the deep learning-derived predictions demonstrated clear risk stratification, with a median PSA progression free survival (PFS) of 19.7 months in the high-risk group and 26 months in the low-risk group (P < 0.001). Deep learning-derived imaging biomarker based on PSMA PET/CT can effectively predict PSA PFS for mPCa patients. Further clinical validation in prospective cohorts is warranted.

Breast cancer remains the most prevalent cause of cancer-related mortality among women worldwide, with an estimated incidence exceeding 500,000 new cases annually. Timely diagnosis is vital for enhancing therapeutic outcomes and increasing survival probabilities. Although conventional diagnostic tools such as mammography are widely used and generally effective, they are often invasive, costly, and exhibit reduced efficacy in patients with dense breast tissue. Infrared thermography, by contrast, offers a non-invasive and economical alternative; however, its clinical adoption has been limited, largely due to difficulties in accurate thermal image interpretation and the suboptimal tuning of machine learning algorithms. To overcome these limitations, this study proposes an automated classification framework that employs convolutional neural networks (CNNs) for distinguishing between malignant and benign thermographic breast images. An Enhanced Particle Swarm Optimization (EPSO) algorithm is integrated to automatically fine-tune CNN hyperparameters, thereby minimizing manual effort and enhancing computational efficiency. The methodology also incorporates advanced image preprocessing techniques-including Mamdani fuzzy logic-based edge detection, Contrast-Limited Adaptive Histogram Equalization (CLAHE) for contrast enhancement, and median filtering for noise suppression-to bolster classification performance. The proposed model achieves a superior classification accuracy of 98.8%, significantly outperforming conventional CNN implementations in terms of both computational speed and predictive accuracy. These findings suggest that the developed system holds substantial potential for early, reliable, and cost-effective breast cancer screening in real-world clinical environments.

Robot-assisted radical prostatectomy (RARP) is the standard surgical procedure for the treatment of prostate cancer. RARP requires a trade-off between performing a wider resection in order to reduce the risk of positive surgical margins (PSMs) and performing minimal resection of the nerve bundles that determine functional outcomes, such as incontinence and potency, which affect patients' quality of life. In order to achieve favourable outcomes, a precise understanding of the three-dimensional (3D) anatomy of the prostate, nerve bundles and tumour lesion is needed. This is the protocol for a single-centre feasibility study including a prospective two-arm interventional group (a 3D virtual and a 3D printed prostate model), and a prospective control group. The primary endpoint will be PSM status and the secondary endpoint will be functional outcomes, including incontinence and sexual function. The study will consist of a total of 270 patients: 54 patients will be included in each of the interventional groups (3D virtual, 3D printed models), 54 in the retrospective control group and 108 in the prospective control group. Automated segmentation of prostate gland and lesions will be conducted on multiparametric magnetic resonance imaging (mpMRI) using 'AutoProstate' and 'AutoLesion' deep learning approaches, while manual annotation of the neurovascular bundles, urethra and external sphincter will be conducted on mpMRI by a radiologist. This will result in masks that will be post-processed to generate 3D printed/virtual models. Patients will be allocated to either interventional arm and the surgeon will be given either a 3D printed or a 3D virtual model at the start of the RARP procedure. At the 6-week follow-up, the surgeon will meet with the patient to present PSM status and capture functional outcomes from the patient via questionnaires. We will capture these measures as endpoints for analysis. These questionnaires will be re-administered at 3, 6 and 12 months postoperatively.

Accurate segmentation of lung regions from CT scan images is critical for diagnosing and monitoring respiratory diseases. This study introduces a novel hybrid architecture Adaptive Attention U-NetAA, which combines the strengths of U-Net3 + and Transformer based attention mechanisms models for high-precision lung segmentation. The U-Net3 + module effectively segments the lung region by leveraging its deep convolutional network with nested skip connections, ensuring rich multi-scale feature extraction. A key innovation is introducing an adaptive attention mechanism within the Transformer module, which dynamically adjusts the focus on critical regions in the image based on local and global contextual relationships. This model's adaptive attention mechanism addresses variations in lung morphology, image artifacts, and low-contrast regions, leading to improved segmentation accuracy. The combined convolutional and attention-based architecture enhances robustness and precision. Experimental results on benchmark CT datasets demonstrate that the proposed model achieves an IoU of 0.984, a Dice coefficient of 0.989, a MIoU of 0.972, and an HD95 of 1.22 mm, surpassing state-of-the-art methods. These results establish U-NetAA as a superior tool for clinical lung segmentation, with enhanced accuracy, sensitivity, and generalization capability.

Diabetic retinopathy (DR) is a leading cause of vision loss in working-age adults. While screening reduces the risk of blindness, traditional imaging is often costly and inaccessible. Artificial intelligence (AI) algorithms present a scalable diagnostic solution, but concerns regarding fairness and generalization persist. This work evaluates the fairness and performance of image-trained models in DR prediction, as well as the impact of disentanglement as a bias mitigation technique, using the diverse mBRSET fundus dataset. Three models, ConvNeXt V2, DINOv2, and Swin V2, were trained on macula images to predict DR and sensitive attributes (SAs) (e.g., age and gender/sex). Fairness was assessed between subgroups of SAs, and disentanglement was applied to reduce bias. All models achieved high DR prediction performance in diagnosing (up to 94% AUROC) and could reasonably predict age and gender/sex (91% and 77% AUROC, respectively). Fairness assessment suggests disparities, such as a 10% AUROC gap between age groups in DINOv2. Disentangling SAs from DR prediction had varying results, depending on the model selected. Disentanglement improved DINOv2 performance (2% AUROC gain), but led to performance drops in ConvNeXt V2 and Swin V2 (7% and 3%, respectively). These findings highlight the complexity of disentangling fine-grained features in fundus imaging and emphasize the importance of fairness in medical imaging AI to ensure equitable and reliable healthcare solutions.

Pneumonia is a leading cause of mortality in children under five, requiring accurate chest X-ray diagnosis. This study presents a machine learning-based Pediatric Chest Pneumonia Classification System to assist healthcare professionals in diagnosing pneumonia from chest X-ray images. The CNN-based model was trained on 5,863 labeled chest X-ray images from children aged 0-5 years from the Guangzhou Women and Children's Medical Center. To address limited data, we applied augmentation techniques (rotation, zooming, shear, horizontal flipping) and employed GANs to generate synthetic images, addressing class imbalance. The system achieved optimal performance using combined original, augmented, and GAN-generated data, evaluated through accuracy and F1 score metrics. The final model was deployed via a Flask web application, enabling real-time classification with probability estimates. Results demonstrate the potential of deep learning and GANs in improving diagnostic accuracy and efficiency for pediatric pneumonia classification, particularly valuable in resource-limited clinical settings https://github.com/AbdulManaf12/Pediatric-Chest-Pneumonia-Classification

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.