Deep learning predicts prevalent and incident Parkinson's disease from UK Biobank fundus imaging.
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
13 Feb 2024
13 Feb 2024
Historique:
received:
02
05
2023
accepted:
10
02
2024
medline:
14
2
2024
pubmed:
14
2
2024
entrez:
13
2
2024
Statut:
epublish
Résumé
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.
Identifiants
pubmed: 38351326
doi: 10.1038/s41598-024-54251-1
pii: 10.1038/s41598-024-54251-1
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
3637Subventions
Organisme : National Science Foundation
ID : NSF IIS 2123809
Informations de copyright
© 2024. The Author(s).
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