{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T14:11:22Z","timestamp":1790345482262,"version":"4.1.0"},"reference-count":99,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"2","license":[{"start":{"date-parts":[[2022,1,11]],"date-time":"2022-01-11T00:00:00Z","timestamp":1641859200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.ahajournals.org"],"crossmark-restriction":true},"short-container-title":["Circulation"],"published-print":{"date-parts":[[2022,1,11]]},"abstract":"<jats:sec>\n            <jats:title>Background:<\/jats:title>\n            <jats:p>The microvasculature, the smallest blood vessels in the body, has key roles in maintenance of organ health and tumorigenesis. The retinal fundus is a window for human in vivo noninvasive assessment of the microvasculature. Large-scale complementary machine learning-based assessment of the retinal vasculature with phenome-wide and genome-wide analyses may yield new insights into human health and disease.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods:<\/jats:title>\n            <jats:p>\n              We used 97\u2009895 retinal fundus images from 54\u2009813 UK Biobank participants. Using convolutional neural networks to segment the retinal microvasculature, we calculated vascular density and fractal dimension as a measure of vascular branching complexity. We associated these indices with 1866 incident\n              <jats:italic>International Classification of Diseases<\/jats:italic>\n              \u2013based conditions (median 10-year follow-up) and 88 quantitative traits, adjusting for age, sex, smoking status, and ethnicity.\n            <\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results:<\/jats:title>\n            <jats:p>Low retinal vascular fractal dimension and density were significantly associated with higher risks for incident mortality, hypertension, congestive heart failure, renal failure, type 2 diabetes, sleep apnea, anemia, and multiple ocular conditions, as well as corresponding quantitative traits. Genome-wide association of vascular fractal dimension and density identified 7 and 13 novel loci, respectively, that were enriched for pathways linked to angiogenesis (eg, vascular endothelial growth factor, platelet-derived growth factor receptor, angiopoietin, and WNT signaling pathways) and inflammation (eg, interleukin, cytokine signaling).<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions:<\/jats:title>\n            <jats:p>Our results indicate that the retinal vasculature may serve as a biomarker for future cardiometabolic and ocular disease and provide insights into genes and biological pathways influencing microvascular indices. Moreover, such a framework highlights how deep learning of images can quantify an interpretable phenotype for integration with electronic health record, biomarker, and genetic data to inform risk prediction and risk modification.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1161\/circulationaha.121.057709","type":"journal-article","created":{"date-parts":[[2021,11,8]],"date-time":"2021-11-08T10:00:28Z","timestamp":1636365628000},"page":"134-150","update-policy":"https:\/\/doi.org\/10.1161\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":176,"title":["Deep Learning of the Retina Enables Phenome- and Genome-Wide Analyses of the Microvasculature"],"prefix":"10.1161","volume":"145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4026-8944","authenticated-orcid":true,"given":"Seyedeh Maryam","family":"Zekavat","sequence":"first","affiliation":[{"name":"Department of Ophthalmology and Visual Science, Yale School of Medicine, New Haven, CT (S.M.Z., J.M., R.A.A., L.D.P., J.C.W.)."},{"name":"Computational Biology &amp; Bioinformatics Program (S.M.Z., Y.Y., H.Z.), Yale University, New Haven, CT."},{"name":"Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (S.M.Z., V.K.R., M.T., S.K., M.C.H., Z.Y., A.P., S.U., P.T.E., P.N.)."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3524-3945","authenticated-orcid":true,"given":"Vineet K.","family":"Raghu","sequence":"additional","affiliation":[{"name":"Program in Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard, Cambridge, MA (S.M.Z., V.K.R., M.T., S.K., M.C.H., Z.Y., A.P., S.U., P.T.E., P.N.)."},{"name":"Cardiovascular Research Center (S.M.Z., V.K.R., M.C.H., S.U., S.H., P.T.E., P.N.), Massachusetts General Hospital, Harvard Medical School, Boston."},{"name":"Cardiovascular Imaging Research Center (V.K.R.), Massachusetts General 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