Détail du document
Identifiant
oai:arXiv.org:2302.06727
Sujet
Computer Science - Machine Learnin... Computer Science - Computer Vision... Electrical Engineering and Systems...Auteur
Tran, Charlie Shen, Kai Liu, Kang Ashok, Akshay Ramirez-Zamora, Adolfo Chen, Jinghua Li, Yulin Fang, RuoguCatégorie
Computer Science
Année
2023
Date de référencement
21/02/2024
Mots clés
parkinson science fundus imaging computer diseaseRésumé
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.
;Comment: 17 pages, 4 figures, 2 tables, 4 supplementary tables
Tran, Charlie,Shen, Kai,Liu, Kang,Ashok, Akshay,Ramirez-Zamora, Adolfo,Chen, Jinghua,Li, Yulin,Fang, Ruogu, 2023, Deep Learning Predicts Prevalent and Incident Parkinson's Disease From UK Biobank Fundus Imaging
