ArticleScientific reports2024
Deep learning predicts prevalent and incident Parkinson's disease from UK Biobank fundus imaging.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed, 33 citations in OpenAlex.
- Genome-wide association study for refractive error imputed from fundus images via deep learning: a proof-of-concept study.Japanese journal of ophthalmology · 2026Article
- AI-driven multimodal retinal imaging for early detection and risk stratification of vascular and neurodegenerative diseases.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Artificial intelligence and neurodegeneration: state of the art and explainability.International journal of retina and vitreous · 2026Review
- Artificial intelligence-integrated multimodal retinal imaging for early detection and risk stratification of systemic vascular and neurodegenerative diseases.Frontiers in neurology · 2026Article
- Article
- An Automated Diagnosis of Parkinson's Disease from MRI Scans Based on Enhanced Residual Dense Network with Attention Mechanism.Journal of imaging informatics in medicine · 2025Article
- Optic Disc Pallor in Parkinson's Disease: A UK Biobank Study.Movement disorders : official journal of the Movement Disorder Society · 2025Article
- A Narrative Review on Biochemical Markers and Emerging Treatments in Prodromal Synucleinopathies.Clinics and practice · 2025Review
- FundusNet: A Deep-Learning Approach for Fast Diagnosis of Neurodegenerative and Eye Diseases Using Fundus Images.Bioengineering (Basel, Switzerland) · 2025Article
- Advances in ocular aging: combining deep learning, imaging, and liquid biopsy biomarkers.Frontiers in medicine · 2025Review
- pTau pathology in the retina of TAU58 mice: association with ganglion cell degeneration and implications on seeding and propagation of pTau from human brain lysates.Acta neuropathologica communications · 2024Article
- Multimodal Retinal Imaging Classification for Parkinson's Disease Using a Convolutional Neural Network.Translational vision science & technology · 2024Article
- SLO-Net: Enhancing Multiple Sclerosis Diagnosis Beyond Optical Coherence Tomography Using Infrared Reflectance Scanning Laser Ophthalmoscopy Images.Translational vision science & technology · 2024Article
- Functional activity, functional connectivity and complex network biomarkers of progressive hyposmia Parkinson's disease with no cognitive impairment: evidences from resting-state fMRI study.Frontiers in aging neuroscience · 2024Article
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Authors and funding
8 authors at 1 institution in 1 country.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.