{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T23:37:13Z","timestamp":1792798633139,"version":"4.5.0"},"reference-count":50,"publisher":"BMJ","issue":"12","license":[{"start":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T00:00:00Z","timestamp":1661299200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100000274","name":"British Heart Foundation","doi-asserted-by":"publisher","award":["PG\/15\/101\/31889"],"award-info":[{"award-number":["PG\/15\/101\/31889"]}],"id":[{"id":"10.13039\/501100000274","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Richard Desmond Charitable Trust","award":["N\/A"],"award-info":[{"award-number":["N\/A"]}]},{"name":"NIHR Biomedical Research Centre Moorfields Eye Hospital and the UCL Institute of Ophthalmology","award":["N\/A"],"award-info":[{"award-number":["N\/A"]}],"id":[{"id":"https:\/\/ror.org\/03tb37539","id-type":"ROR","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100000265","name":"Medical Research Council","doi-asserted-by":"publisher","award":["MR\/L02005X\/1"],"award-info":[{"award-number":["MR\/L02005X\/1"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000265","name":"Medical Research Council","doi-asserted-by":"publisher","award":["MR\/N003284\/1"],"award-info":[{"award-number":["MR\/N003284\/1"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000289","name":"Cancer Research UK","doi-asserted-by":"publisher","award":["C864\/A8257"],"award-info":[{"award-number":["C864\/A8257"]}],"id":[{"id":"10.13039\/501100000289","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000629","name":"Age UK","doi-asserted-by":"publisher","award":["262"],"award-info":[{"award-number":["262"]}],"id":[{"id":"10.13039\/501100000629","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["Br J Ophthalmol"],"published-print":{"date-parts":[[2022,12]]},"abstract":"<jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>We examine whether inclusion of artificial intelligence (AI)-enabled retinal vasculometry (RV) improves existing risk algorithms for incident stroke, myocardial infarction (MI) and circulatory mortality.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>\n                      AI-enabled retinal vessel image analysis processed images from 88\u2009052 UK Biobank (UKB) participants (aged 40\u201369 years at image capture) and 7411 European Prospective Investigation into Cancer (EPIC)-Norfolk participants (aged 48\u201392). Retinal arteriolar and venular width, tortuosity and area were extracted. Prediction models were developed in UKB using multivariable Cox proportional hazards regression for circulatory mortality, incident stroke and MI, and externally validated in EPIC-Norfolk. Model performance was assessed using optimism adjusted calibration, C-statistics and R\n                      <jats:sup>2<\/jats:sup>\n                      statistics. Performance of Framingham risk scores (FRS) for incident stroke and incident MI, with addition of RV to FRS, were compared with a simpler model based on RV, age, smoking status and medical history (antihypertensive\/cholesterol lowering medication, diabetes, prevalent stroke\/MI).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      UKB prognostic models were developed on 65\u2009144 participants (mean age 56.8; median follow-up 7.7 years) and validated in 5862 EPIC-Norfolk participants (67.6, 9.1 years, respectively). Prediction models for circulatory mortality in men and women had optimism adjusted C-statistics and R\n                      <jats:sup>2<\/jats:sup>\n                      statistics between 0.75\u20130.77 and 0.33\u20130.44, respectively. For incident stroke and MI, addition of RV to FRS did not improve model performance in either cohort. However, the simpler RV model performed equally or better than FRS.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>RV offers an alternative predictive biomarker to traditional risk-scores for vascular health, without the need for blood sampling or blood pressure measurement. Further work is needed to examine RV in population screening to triage individuals at high-risk.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/bjo-2022-321842","type":"journal-article","created":{"date-parts":[[2022,10,4]],"date-time":"2022-10-04T20:00:39Z","timestamp":1664913639000},"page":"1722-1729","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":73,"title":["Artificial intelligence-enabled retinal vasculometry for prediction of circulatory mortality, myocardial infarction and stroke"],"prefix":"10.1136","volume":"106","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0369-8574","authenticated-orcid":false,"given":"Alicja 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