Artificial Intelligence Medical Compendium

BACKGROUND: Non-attendance after referral from screening is one of the major challenges for diabetic retinopathy screening programmes in Africa. Artificial intelligence (AI) systems for the identification of diabetic retinopathy can provide point-of-... read more 

Periodic discharges (PDs) and rhythmic delta activity (RDA) are common EEG patterns in critically ill patients that require characterization of lateralization, spatial localization, and frequency. Manual characterization is subjective and time-consum... read more 

Osteoarthritis (OA) is a degenerative joint disease; however, the role of ferroptosis in synovial macrophages remains poorly defined. Here, we integrated single-cell RNA sequencing data from human synovial tissues to systematically characterize cellu... read more 

Artificial intelligence (AI) is increasingly applied to toxicological assessment and health-based exposure limit (HBEL) derivation, offering potential gains in literature retrieval efficiency, methodological consistency, but introducing novel risks s... read more 

AIM: Detecting endoleaks on post-EVAR CT scans, especially in the presence of artifacts caused by embolic agents like Onyx, is a challenging but critical task. In this study, we propose a deep learning-based method for endoleak detection using a sing... read more 

Understanding how network structure shapes neural population dynamics and representation of external inputs is a key issue in neuroscience. This study proposes a modeling framework for recurrent spiking neural networks (RSNN) and elucidates dynamical... read more 

Cyanobacteria, as photosynthetic microorganisms, are emerging as a significant source of bioactive peptides with tremendous therapeutic potential against cancer, infections, and inflammatory diseases. Recent studies have highlighted that cyanobacteri... read more 

Artificial intelligence (AI) has become a major area of ophthalmic innovation, with a rapidly expanding literature, yet routine clinical adoption remains limited. Diagnostic accuracy alone is an insufficient marker of clinical value because even tech... read more 

BACKGROUND: Deep learning-based aneurysm segmentation using 3D time-of-flight magnetic resonance angiography (TOF-MRA) offers promising non-invasive screening, but its accuracy and generalizability remain limited. PURPOSE: To develop a deep learning ... read more 

OBJECTIVE: Drug-drug interactions (DDIs) are a major cause of adverse drug reactions (ADRs), particularly in patients receiving multiple medications. Existing computational methods predominantly focus on pairwise interactions, limiting their ability ... read more