Deep Learning Predicts Prevalent and Incident Parkinson's Disease From UK Biobank Fundus Imaging
This addresses the need for a more accessible and early diagnostic screening method for Parkinson's disease patients, though it is incremental as it applies existing deep learning techniques to a new data modality.
The study tackled the problem of diagnosing Parkinson's disease by using deep learning on retinal fundus images from the UK Biobank, achieving an AUC of 0.77 for differentiating patients from healthy subjects, which held for both prevalent and incident cases.
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.