This study is from Florida suggesting an easy way to screen PD.

Sci Rep

. 2024 Feb 13;14(1):3637.

doi: 10.1038/s41598-024-54251-1.

Deep learning predicts prevalent and incident Parkinson's disease from UK Biobank fundus imaging

Charlie Tran ¹, Kai Shen ¹, Kang Liu ², Akshay Ashok ³, Adolfo Ramirez-Zamora ⁴, Jinghua Chen ⁵, Yulin Li ⁶, Ruogu Fang ^{7 8 9}

Affiliations expand

• PMID: 38351326

DOI: 10.1038/s41598-024-54251-1

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 suggest Parkinson's disease individuals can be differentiated from age and gender-matched healthy subjects with 68% accuracy. 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.

Professor Finkelstein and Dr. Nquyen have been very active in studying the retina and the cells of the retina of PD patients, even in evaluating the effect of possible new PD drugs on the function of retina cells. I expect them to publish some results of the primate study as well as screening studies of different potential PD drugs in their model.