This publication examines how multimodal computational biomarkers can improve the early detection of neurodegenerative change before clear clinical symptoms appear. The work synthesizes findings from neuroimaging, cognitive assessment, and molecular profiling to describe how machine-learning models identify subtle patterns associated with disease progression. Emphasis is placed on reproducibility, transparent feature selection, and the clinical limits of predictive algorithms in longitudinal patient cohorts.

This publication explores the use of artificial intelligence for high-precision segmentation in biomedical imaging. It reviews convolutional and transformer-based architectures applied to magnetic resonance, computed tomography, and microscopy datasets, with attention to annotation quality, model validation, and domain shift. The article argues that reliable clinical adoption requires interpretable outputs, standardized benchmarks, and prospective evaluation across diverse patient populations.