ArticleFrontiers in cardiovascular medicine2022
A Deep Learning System for Fully Automated Retinal Vessel Measurement in High Throughput Image Analysis.
Article in Frontiers in cardiovascular medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep Learning and Machine Learning Algorithms for Retinal Image Analysis in Neurodegenerative Disease: Systematic Review of Datasets and Models.Translational vision science & technology · 2024Pooled it
- Retinal Vascular Features and Risk of Colorectal Cancer Incidence and Mortality: A Population-Based Cohort Study.Ophthalmology and therapy · 2026Article
- Retinomics as a Tool for Glaucoma Prediction.Ophthalmology science · 2026Article
- Alterations in Retinal Vasculometry After Acute Primary Angle Closure Episode: A Comparative Analysis.Translational vision science & technology · 2026Article
- Retinal vascular alteration following surgical intraocular pressure reduction in primary angle closure disease.BMJ open ophthalmology · 2026Article
- Automated measurement of optic nerve sheath diameter using ocular ultrasound video.Medical & biological engineering & computing · 2026Article
- Improving retinal vessel assessment precision by integrating deep learning with interactive editing and graphical modeling.Scientific reports · 2025Article
- Choroidal Vascular Fingerprints From Indocyanine Green Angiography Unveil Chorioretinal Disease State.Investigative ophthalmology & visual science · 2025Article
- The retina as a window into detecting subclinical cardiovascular disease in type 2 diabetes.Scientific reports · 2025Article
- Standardizing image acquisition and processing methods: a critical need for the accurate assessment of retinal blood vessel tortuosity.Biomedical optics express · 2025Article
- A multimodal visual-language foundation model for computational ophthalmology.NPJ digital medicine · 2025Article
- Comprehensive genome-wide analysis of retinal vessel caliber reveals microvascular-blood pressure pathways: advancing predictive, preventive, and personalized medicine.The EPMA journal · 2025Article
- Noninvasive early prediction of preeclampsia in pregnancy using retinal vascular features.NPJ digital medicine · 2025Article
- Genomic determinants of biological age estimated by deep learning applied to retinal images.GeroScience · 2025Article
- Retinal oculomics and risk of incident aortic aneurysm and aortic adverse events: a population-based cohort study.International journal of surgery (London, England) · 2025Article
- Evaluating the Impact of Retinal Vessel Segmentation Metrics on Retest Reliability in a Clinical Setting: A Comparative Analysis Using AutoMorph.Investigative ophthalmology & visual science · 2024Article
- GWAS-by-subtraction reveals an IOP-independent component of primary open angle glaucoma.Nature communications · 2024Article
- AI-based fully automatic analysis of retinal vascular morphology in pediatric high myopia.BMC ophthalmology · 2024Article
- A Retinal Vessel Segmentation Method Based on the Sharpness-Aware Minimization Model.Sensors (Basel, Switzerland) · 2024Article
- Oculomics: A Crusade Against the Four Horsemen of Chronic Disease.Ophthalmology and therapy · 2024Review
Corrections and comments
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9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Motivation: Retinal microvasculature is a unique window for predicting and monitoring major cardiovascular diseases, but high throughput tools based on deep learning for in-detail retinal vessel analysis are lacking. As such, we aim to develop and validate an artificial intelligence system (Retina-based Microvascular Health Assessment System, RMHAS) for fully automated vessel segmentation and quantification of the retinal microvasculature. Results: RMHAS achieved good segmentation accuracy across datasets with diverse eye conditions and image resolutions, having AUCs of 0.91, 0.88, 0.95, 0.93, 0.97, 0.95, 0.94 for artery segmentation and 0.92, 0.90, 0.96, 0.95, 0.97, 0.95, 0.96 for vein segmentation on the AV-WIDE, AVRDB, HRF, IOSTAR, LES-AV, RITE, and our internal datasets. Agreement and repeatability analysis supported the robustness of the algorithm. For vessel analysis in quantity, less than 2 s were needed to complete all required analysis.
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Registered trials
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