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Parkinson's disease is the world's fastest growing neurological disorder.Research to elucidate the mechanisms of Parkinson's disease and automatediagnostics would greatly improve the treatment of patients with Parkinson'sdisease. Current diagnostic methods are expensive with limited availability.Considering the long progression time of Parkinson's disease, a desirablescreening should be diagnostically accurate even before the onset of symptomsto allow medical intervention. We promote attention for retinal fundus imaging,often termed a window to the brain, as a diagnostic screening modality forParkinson's disease. We conduct a systematic evaluation of conventional machinelearning and deep learning techniques to classify Parkinson's disease from UKBiobank fundus imaging. Our results suggest Parkinson's disease individuals canbe differentiated from age and gender matched healthy subjects with 71%accuracy. This accuracy is maintained when predicting either prevalent orincident Parkinson's disease. Explainability and trustworthiness is enhanced byvisual attribution maps of localized biomarkers and quantified metrics of modelrobustness to data perturbations.

Deep Learning Predicts Prevalent and Incident Parkinson's Disease From UK Biobank Fundus Imaging