An ensemble machine learning classifier for Parkinson's disease diagnosis using optical coherence tomography angiography.

ABSTRACT: Parkinson's disease (PD) is the fastest-growing neurodegenerative disorder worldwide, yet its early diagnosis remains a major challenge due to the absence of reliable biomarkers. Emerging evidence indicates that retinal microvascular alterations, detectable through Optical Coherence Tomography Angiography (OCTA), may serve as promising non-invasive biomarkers for PD. However, the lack of definitive diagnostic tests for early-stage PD underscores an urgent need for objective, non-invasive tools to facilitate timely detection and intervention. In this retrospective study, OCTA images were obtained from 53 PD patients and 39 healthy controls. Both the superficial vascular complex (SVC) and deep vascular complex (DVC) were segmented to extract 22 quantitative features, including foveal avascul

SUBMITTER: Hasanshahi M 

PROVIDER: S-EPMC12923507 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature