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 with limited availability. Considering the long progression time of Parkinson's disease, a desirable screening should be diagnostically accurate even before the onset of symptoms to allow medical intervention. We promote attention for retinal fundus imaging, often termed a window to the brain, as a diagnostic screening modality for Parkinson's disease. We conduct a systematic evaluation of conventional machine learning and deep learning techniques to classify Parkinson's disease from UK Biobank fundus imaging. Our results suggest Parkinson's disease individuals can be differentiated from age and gender matched healthy subjects with 71% accuracy. This accuracy is maintained when predicting either prevalent or incident Parkinson's disease. Explainability and trustworthiness is enhanced by visual attribution maps of localized biomarkers and quantified metrics of model robustness to data perturbations.

翻译：帕金森病是全球增长最快的神经系统疾病。阐明帕金森病发病机制并实现诊断自动化将显著改善患者治疗。当前诊断方法成本高昂且可及性有限。鉴于帕金森病病程进展缓慢，理想的筛查应在症状出现前即具备诊断准确性，以便实施医疗干预。我们提倡将视网膜眼底影像（常被称为"大脑之窗"）作为帕金森病的诊断筛查工具。本研究系统评估了传统机器学习与深度学习技术在UK Biobank眼底影像中对帕金森病的分类能力。结果表明，帕金森病患者与年龄性别匹配的健康受试者可通过该方法以71%的准确率区分，且该准确率在预测现患与新发帕金森病时均保持稳定。通过局部生物标志物的可视化归因图及模型对数据扰动的量化鲁棒性指标，增强了方法的可解释性与可信度。