Temporal-Enhanced Interpretable Multi-Modal Prognosis and Risk Stratification Framework for Diabetic Retinopathy (TIMM-ProRS)

Diabetic retinopathy (DR), affecting millions globally with projections indicating a significant rise, poses a severe blindness risk and strains healthcare systems. Diagnostic complexity arises from visual symptom overlap with conditions like age-related macular degeneration and hypertensive retinopathy, exacerbated by high misdiagnosis rates in underserved regions. This study introduces TIMM-ProRS, a novel deep learning framework integrating Vision Transformer (ViT), Convolutional Neural Network (CNN), and Graph Neural Network (GNN) with multi-modal fusion. TIMM-ProRS uniquely leverages both retinal images and temporal biomarkers (HbA1c, retinal thickness) to capture multi-modal and temporal dynamics. Evaluated comprehensively across diverse datasets including APTOS 2019 (trained), Messidor-2, RFMiD, EyePACS, and Messidor-1 (validated), the model achieves 97.8\% accuracy and an F1-score of 0.96, demonstrating state-of-the-art performance and outperforming existing methods like RSG-Net and DeepDR. This approach enables early, precise, and interpretable diagnosis, supporting scalable telemedical management and enhancing global eye health sustainability.

A Synoptic Review of High-Frequency Oscillations as a Biomarker in Neurodegenerative Disease

High Frequency Oscillations (HFOs), rapid bursts of brain activity above 80 Hz, have emerged as a highly specific biomarker for epileptogenic tissue. Recent evidence suggests that HFOs are also present in Alzheimer's Disease (AD), reflecting underlying network hyperexcitability and offering a promising, noninvasive tool for early diagnosis and disease tracking. This synoptic review provides a comprehensive analysis of publicly available electroencephalography (EEG) datasets relevant to HFO research in neurodegenerative disorders. We conducted a bibliometric analysis of 1,222 articles, revealing a significant and growing research interest in HFOs, particularly within the last ten years. We then systematically profile and compare key public datasets, evaluating their participant cohorts, data acquisition parameters, and accessibility, with a specific focus on their technical suitability for HFO analysis. Our comparative synthesis highlights critical methodological heterogeneity across datasets, particularly in sampling frequency and recording paradigms, which poses challenges for cross-study validation, but also offers opportunities for robustness testing. By consolidating disparate information, clarifying nomenclature, and providing a detailed methodological framework, this review serves as a guide for researchers aiming to leverage public data to advance the role of HFOs as a cross-disease biomarker for AD and related conditions.