{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["9"],"submitter":["Shi D"],"pubmed_abstract":["<h4>Motivation</h4>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.<h4>Results</h4>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. A"],"journal":["Frontiers in cardiovascular medicine"],"pagination":["823436"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC8980780"],"repository":["biostudies-literature"],"pubmed_title":["A Deep Learning System for Fully Automated Retinal Vessel Measurement in High Throughput Image Analysis."],"pmcid":["PMC8980780"],"pubmed_authors":["Tan Z","Zhang X","He M","Ge Z","Shi D","Wang W","Shang X","Lin Z","Meng W"],"additional_accession":[]},"is_claimable":false,"name":"A Deep Learning System for Fully Automated Retinal Vessel Measurement in High Throughput Image Analysis.","description":"<h4>Motivation</h4>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.<h4>Results</h4>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. A","dates":{"release":"2022-01-01T00:00:00Z","publication":"2022","modification":"2025-04-05T16:00:13.313Z","creation":"2025-04-05T16:00:13.313Z"},"accession":"S-EPMC8980780","cross_references":{"pubmed":["35391847"],"doi":["10.3389/fcvm.2022.823436"]}}