  Parkinson's disease is the world's fastest-growing neurological disorder.
Research to elucidate the mechanisms of Parkinson's disease and automate
diagnostics would greatly improve the treatment of patients with Parkinson's
disease. Current diagnostic methods are expensive and have limited
availability. Considering the insidious and preclinical onset and progression
of the disease, a desirable screening should be diagnostically accurate even
before the onset of symptoms to allow medical interventions. We highlight
retinal fundus imaging, often termed a window to the brain, as a diagnostic
screening modality for Parkinson's disease. We conducted a systematic
evaluation of conventional machine learning and deep learning techniques to
classify Parkinson's disease from UK Biobank fundus imaging. Our results show
that Parkinson's disease individuals can be differentiated from age and
gender-matched healthy subjects with an Area Under the Curve (AUC) of 0.77.
This accuracy is maintained when predicting either prevalent or incident
Parkinson's disease. Explainability and trustworthiness are enhanced by visual
attribution maps of localized biomarkers and quantified metrics of model
robustness to data perturbations.
