Deep learning for early Parkinson's detection: A review of fundus imaging approaches

Abstract

Parkinson's disease (PD), a type of neurodegenerative disease, is on the rise globally as the population ages. Today's costly diagnostic techniques for Parkinson's disease often detect the illness after significant brain damage has already occurred. Early detection is essential for improving patient outcomes and potentially slowing the disease's progression. One of the newest advances in artificial intelligence, deep learning (DL), presents new opportunities for the early, non-invasive diagnosis of Parkinson's disease. Fundus imaging, which captures fine-grained images of the retina, is a promising technique for detecting the disease's early symptoms. Changes in the retinal blood vessels and anomalies of the optic disc (OD) have been linked to neurodegeneration. DL models can identify subtle patterns in these fundus images, such as vascular alterations and changes in the optic disc, which have been connected to Parkinson's disease. This approach replaces current diagnostic methods with a scalable and cost-effective solution, increasing access to early detection. This review explores the current state of the art in using DL models with fundus images to detect PD early on, with a focus on significant public datasets, methodologies, and related research. It highlights how DL models could transform PD screening and provides an overview of the advancements and challenges in this emerging field