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

TL;DR

A systematic evaluation of conventional machine learning and deep learning techniques to classify Parkinson’s disease from UK Biobank fundus imaging suggests Parkinson’s disease individuals can be differentiated from age and gender-matched healthy subjects with 68% accuracy.

Abstract

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.

Figures (4)

• Figure 1: Data Collection pipeline from the UK Biobank. Instances in parentheses represent an equal balance of Parkinson’s disease and healthy controls subjects. Multiple quality selection phases were used as additional inclusion criteria into our dataset arising from AutoMorph and manual image grading. In total, we have the overall dataset of PD subjects matched with age and gender-matched healthy controls, and two subsets corresponding to prevalent and incident subjects.
• Figure 2: Box-plots and ROC curves of the Parkinson Disease Classification Models. The models are evaluated over five randomized repetitions of the five-fold stratified cross-validation protocol. The AUC scores are enlisted in the legend.
• Figure 3: Attribution correspondence of retinal features. In the first column, an artery-vein (red and blue, respectively) map is combined with the optic cup (teal) and optic disc (yellow) generated from the AutoMorph deep learning segmentation module. A white dashed line is shown as an estimate for the foveal region. In the third column, a predicted attribution map is generated using the guided backpropagation algorithm on top of the AlexNet model. The intersection of the salient features with the segmentation is shown in the last column. The images represent the left (top) and right (bottom) eyes from the same subject, demonstrating distinct feature distributions for prediction.
• Figure 4: Explanation of infidelity and sensitivity comparison among different models. The logarithm of the infidelity score was applied for visualization due to the large range of scores.