{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,8]],"date-time":"2026-10-08T20:50:25Z","timestamp":1791492625437,"version":"4.3.4"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"3","license":[{"start":{"date-parts":[[2024,1,8]],"date-time":"2024-01-08T00:00:00Z","timestamp":1704672000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"name":"Australian National Health and Medical Research Council"},{"name":"Emerging Leadership Investigator","award":["GNT1172873"],"award-info":[{"award-number":["GNT1172873"]}]},{"name":"Emerging Leadership Investigator","award":["GNT1172900"],"award-info":[{"award-number":["GNT1172900"]}]},{"name":"Emerging Leadership Investigator","award":["GNT1193955"],"award-info":[{"award-number":["GNT1193955"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,2,29]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec>\n                  <jats:title>Background<\/jats:title>\n                  <jats:p>We have previously developed an artificial intelligence\u2013based risk assessment tool to identify the individual risk of HIV and sexually transmitted infections (STIs) in a sexual health clinical setting. Based on this tool, this study aims to determine the optimal risk score thresholds to identify individuals at high risk for HIV\/STIs.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>Using 2008\u20132022 data from 216 252 HIV, 227 995 syphilis, 262 599 gonorrhea, and 320 355 chlamydia consultations at a sexual health center, we applied MySTIRisk machine learning models to estimate infection risk scores. Optimal cutoffs for determining high-risk individuals were determined using Youden's index.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>The HIV risk score cutoff for high risk was 0.56, with 86.0% sensitivity (95% CI, 82.9%\u201388.7%) and 65.6% specificity (95% CI, 65.4%\u201365.8%). Thirty-five percent of participants were classified as high risk, which accounted for 86% of HIV cases. The corresponding cutoffs were 0.49 for syphilis (sensitivity, 77.6%; 95% CI, 76.2%\u201378.9%; specificity, 78.1%; 95% CI, 77.9%\u201378.3%), 0.52 for gonorrhea (sensitivity, 78.3%; 95% CI, 77.6%\u201378.9%; specificity, 71.9%; 95% CI, 71.7%\u201372.0%), and 0.47 for chlamydia (sensitivity, 68.8%; 95% CI, 68.3%\u201369.4%; specificity, 63.7%; 95% CI, 63.5%\u201363.8%). High-risk groups identified using these thresholds accounted for 78% of syphilis, 78% of gonorrhea, and 69% of chlamydia cases. The odds of positivity were significantly higher in the high-risk group than otherwise across all infections: 11.4 (95% CI, 9.3\u201314.8) times for HIV, 12.3 (95% CI, 11.4\u201313.3) for syphilis, 9.2 (95% CI, 8.8\u20139.6) for gonorrhea, and 3.9 (95% CI, 3.8\u20134.0) for chlamydia.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec>\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>Risk scores generated by the AI-based risk assessment tool MySTIRisk, together with Youden's index, are effective in determining high-risk subgroups for HIV\/STIs. The thresholds can aid targeted HIV\/STI screening and prevention.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1093\/ofid\/ofae011","type":"journal-article","created":{"date-parts":[[2024,1,5]],"date-time":"2024-01-05T21:25:03Z","timestamp":1704489903000},"source":"Crossref","is-referenced-by-count":30,"title":["Identifying Individuals at High Risk for HIV and Sexually Transmitted Infections With an Artificial Intelligence\u2013Based Risk Assessment Tool"],"prefix":"10.1093","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5880-5731","authenticated-orcid":false,"given":"Phyu M","family":"Latt","sequence":"first","affiliation":[{"name":"Artificial Intelligence and Modelling in Epidemiology Program, Melbourne Sexual Health Centre, Alfred Health , Melbourne , Australia"},{"name":"Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University , Melbourne , 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China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5784-7403","authenticated-orcid":false,"given":"Jason J","family":"Ong","sequence":"additional","affiliation":[{"name":"Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University , Melbourne , Australia"},{"name":"Melbourne Sexual Health Centre, Alfred Health , Melbourne , Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Eric P F","family":"Chow","sequence":"additional","affiliation":[{"name":"Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University , Melbourne , Australia"},{"name":"Melbourne Sexual Health Centre, Alfred Health , Melbourne , Australia"},{"name":"Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne , Melbourne , Australia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher 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