ArticleAnnals of clinical and translational neurology2019
Retinal image analytics detects white matter hyperintensities in healthy adults.
Article in Annals of clinical and translational neurology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers.
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Who cites it
23 citing papers in PubMed, 37 citations in OpenAlex.
- AI-Based Retinal Image Analysis for the Detection of Choroidal Neovascular Age-Related Macular Degeneration (AMD) and Its Association with Brain Health.Brain sciences · 2025Article
- Deep learning-based prediction of cerebral white matter hyperintensity burden using carotid magnetic resonance angiography.Frontiers in neurology · 2025Article
- Retinal Vascular Morphology Reflects and Predicts Cerebral Small Vessel Disease: Evidences from Eye-Brain Imaging Analysis.Research (Washington, D.C.) · 2025Article
- Two-Stage Recruitment Design to Reduce Magnetic Resonance Imaging Screening Cost for a Theoretical Clinical Trial of White Matter Hyperintensity Progression.Journal of the American Heart Association · 2024Article
- AI-integrated ocular imaging for predicting cardiovascular disease: advancements and future outlook.Eye (London, England) · 2024Review
- Automated Retinal Vessel Analysis Based on Fundus Photographs as a Predictor for Non-Ophthalmic Diseases-Evolution and Perspectives.Journal of personalized medicine · 2023Review
- Ocular images-based artificial intelligence on systemic diseases.Biomedical engineering online · 2023Review
- Lifestyle Changes Reduced Estimated White Matter Hyperintensities Based on Retinal Image Analysis.International journal of environmental research and public health · 2023Article
- Predicting the severity of white matter lesions among patients with cerebrovascular risk factors based on retinal images and clinical laboratory data: a deep learning study.Frontiers in neurology · 2023Article
- Digital solution for detection of undiagnosed diabetes using machine learning-based retinal image analysis.BMJ open diabetes research & care · 2022Article
- Automated Systems for Calculating Arteriovenous Ratio in Retinographies: A Scoping Review.Diagnostics (Basel, Switzerland) · 2022Article
- Giant cell arteritis: a cause of temporomandibular joint claudication.Journal of medicine and life · 2022Article
- Predicting Systemic Health Features from Retinal Fundus Images Using Transfer-Learning-Based Artificial Intelligence Models.Diagnostics (Basel, Switzerland) · 2022Article
- Prediction of White Matter Hyperintensity in Brain MRI Using Fundus Photographs via Deep Learning.Journal of clinical medicine · 2022Article
- Risk Assessment of CHD Using Retinal Images with Machine Learning Approaches for People with Cardiometabolic Disorders.Journal of clinical medicine · 2022Article
- Retinal microvascular changes in white matter hyperintensities investigated by swept source optical coherence tomography angiography.BMC ophthalmology · 2022Article
- Ischemic and haemorrhagic stroke risk estimation using a machine-learning-based retinal image analysis.Frontiers in neurology · 2022Article
- Machine-learning method for localization of cerebral white matter hyperintensities in healthy adults based on retinal images.Brain communications · 2021Article
- Relationship between Retinal Microvasculature, Cardiovascular Risk and Silent Brain Infarction in Hypertensive Patients.Diagnostics (Basel, Switzerland) · 2021Article
- Artificial intelligence for brain diseases: A systematic review.APL bioengineering · 2020Review
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: We investigated whether an automatic retinal image analysis (ARIA) incorporating machine learning approach can identify asymptomatic older adults harboring high burden of white matter hyperintensities (WMH) using MRI as gold standard. Methods: In this cross-sectional study, we evaluated 180 community-dwelling, stroke-, and dementia-free healthy subjects and performed ARIA by acquiring a nonmydriatic retinal fundus image. The primary outcome was the diagnostic performance of ARIA in detecting significant WMH on MRI brain, defined as age-related white matter changes (ARWMC) grade ≥2. We analyzed both clinical variables and retinal characteristics using logistic regression analysis. We developed a machine learning network model with ARIA to estimate WMH and its classification. Results: All 180 subjects completed MRI and ARIA. The mean age was 70.3 ± 4.5 years, 70 (39%) were male. Risk factor profiles were: 106 (59%) hypertension, 31 (17%) diabetes, and 47 (26%) hyperlipidemia. Severe WMH (global ARWMC grade ≥2) was found in 56 (31%) subjects. The performance for detecting severe WMH with sensitivity (SN) 0.929 (95% CI from 0.819 to 0.977) and specificity (SP) 0.984 (95% CI from 0.937 to 0.997) was excellent. There was a good correlation between WMH volume (log-transformed) obtained from MRI versus those estimated from retinal images using ARIA with a correlation coefficient of 0.897 (95% CI from 0.864 to 0.922). Interpretation: We developed a robust algorithm to automatically evaluate retinal fundus image that can identify subjects with high WMH burden. Further community-based prospective studies should be performed for early screening of population at risk of cerebral small vessel disease.
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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.