{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,28]],"date-time":"2026-09-28T22:39:00Z","timestamp":1790635140296,"version":"4.1.0"},"reference-count":29,"publisher":"BMJ","funder":[{"name":"Fundamental Research Funds of the State key Laboratory of 0phthalmology","award":["3030901010058"],"award-info":[{"award-number":["3030901010058"]}]},{"DOI":"10.13039\/501100021171","name":"Guangdong Basic and Applied Basic Research Foundation","doi-asserted-by":"crossref","award":["2019B1515120011"],"award-info":[{"award-number":["2019B1515120011"]}],"id":[{"id":"10.13039\/501100021171","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100003453","name":"Natural Science Foundation of Guangdong Province","doi-asserted-by":"crossref","award":["2023A1515011475"],"award-info":[{"award-number":["2023A1515011475"]}],"id":[{"id":"10.13039\/501100003453","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["bmj.com"],"crossmark-restriction":true},"short-container-title":["Br J Ophthalmol"],"abstract":"<jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>To quantify the profiles of choroidal vascularity index (CVI) using fully artificial intelligence (AI)-based algorithm applied to swept-source optical coherence tomography (SS-OCT) images and evaluate the determinants of CVI in a population-based study.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This cross-sectional study included adults aged \u226535 years residing in the Yuexiu District of Guangzhou, China, a follow-up population-based study. All participants (n=646) underwent comprehensive ophthalmic examinations, including SS-OCT for quantifying choroidal parameters. The CVI and subfoveal choroidal thickness (SFCT) were measured by a novel AI-based system.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>A total of 556 participants were included, with a mean age of 56.4\u00b19.9 years and 44.96% women. The average CVI and SFCT of the overall population were 69.7% (95% CI 69.2 to 70.3) and 263.0\u2009\u00b5m (95%\u2009CI 257.2 to 268.8), respectively. After adjusting for other factors, older age and longer AL were significantly associated with a lower CVI. The CVI decreased by \u20130.13% (\u20130.19 to \u20130.06, p&lt;0.001) with each 1-year increase in age, \u20132.10% (\u20133.29 to \u20130.92, p=0.001) with each 1\u2009mm increase in AL. Furthermore, significantly positive correlation between CVI and SFCT has been observed, with coefficient of 0.059 (0.052 to 0.065, p&lt;0.001).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Using new AI-based choroidal segmentation software, we provided a fast, reliable and objective CVI profile for large-scale samples. Older age and longer AL were independent correlates of choroidal thinning and CVI decline. These factors should be considered when interpreting SS-OCT-based choroidal measurements.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1136\/bjo-2023-323224","type":"journal-article","created":{"date-parts":[[2023,3,31]],"date-time":"2023-03-31T13:20:45Z","timestamp":1680268845000},"page":"bjo-2023-323224","update-policy":"https:\/\/doi.org\/10.1136\/crossmarkpolicy","source":"Crossref","is-referenced-by-count":9,"title":["Distribution and determinants of choroidal vascularity index in healthy eyes from deep-learning choroidal analysis: a population-based SS-OCT study"],"prefix":"10.1136","author":[{"given":"Meng","family":"Xuan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cong","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangbin","family":"Kong","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5273-3332","authenticated-orcid":false,"given":"Wei","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mingguang","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"239","published-online":{"date-parts":[[2023,3,31]]},"reference":[{"key":"2023033110200728000_bjo-2023-323224v1.1","doi-asserted-by":"publisher","DOI":"10.1016\/j.preteyeres.2021.100994"},{"key":"2023033110200728000_bjo-2023-323224v1.2","doi-asserted-by":"publisher","DOI":"10.1016\/j.preteyeres.2021.100951"},{"key":"2023033110200728000_bjo-2023-323224v1.3","doi-asserted-by":"publisher","DOI":"10.1016\/j.preteyeres.2020.100829"},{"key":"2023033110200728000_bjo-2023-323224v1.4","doi-asserted-by":"publisher","DOI":"10.1167\/iovs.14-14447"},{"key":"2023033110200728000_bjo-2023-323224v1.5","doi-asserted-by":"publisher","DOI":"10.1038\/srep21090"},{"key":"2023033110200728000_bjo-2023-323224v1.6","doi-asserted-by":"publisher","DOI":"10.1167\/iovs.18-25764"},{"key":"2023033110200728000_bjo-2023-323224v1.7","doi-asserted-by":"crossref","unstructured":"Nivison-Smith L , Khandelwal N , Tong J , et al . 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