{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,14]],"date-time":"2026-10-14T21:43:48Z","timestamp":1792014228083,"version":"4.4.0"},"reference-count":29,"publisher":"Elsevier BV","issue":"12","license":[{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,12,1]],"date-time":"2025-12-01T00:00:00Z","timestamp":1764547200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,9,25]],"date-time":"2025-09-25T00:00:00Z","timestamp":1758758400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01HL138728"],"award-info":[{"award-number":["R01HL138728"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["T32 AI007291"],"award-info":[{"award-number":["T32 AI007291"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["K23AI185268"],"award-info":[{"award-number":["K23AI185268"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","thelancet.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["The Lancet Digital Health"],"published-print":{"date-parts":[[2025,12]]},"DOI":"10.1016\/j.landig.2025.100934","type":"journal-article","created":{"date-parts":[[2025,11,26]],"date-time":"2025-11-26T19:53:57Z","timestamp":1764186837000},"page":"100934","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":1,"title":["Performance of universal and stratified computer-aided detection thresholds for chest x-ray-based tuberculosis screening: a cross-sectional, diagnostic accuracy study"],"prefix":"10.1016","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4726-9651","authenticated-orcid":false,"given":"Joowhan","family":"Sung","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peter James","family":"Kitonsa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Annet","family":"Nalutaaya","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Isooba","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Susan","family":"Birabwa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Keneth","family":"Ndyabayunga","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rogers","family":"Okura","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan","family":"Magezi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Deborah","family":"Nantale","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ivan","family":"Mugabi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Violet","family":"Nakiiza","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David W","family":"Dowdy","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Achilles","family":"Katamba","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Emily A","family":"Kendall","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.landig.2025.100934_bib1","series-title":"Global Tuberculosis Report 2023","year":"2023"},{"key":"10.1016\/j.landig.2025.100934_bib2","doi-asserted-by":"crossref","first-page":"e283","DOI":"10.1016\/S2468-2667(21)00033-5","article-title":"Community-based active case-finding interventions for tuberculosis: a systematic review","volume":"6","author":"Burke","year":"2021","journal-title":"Lancet Public Health"},{"key":"10.1016\/j.landig.2025.100934_bib3","series-title":"WHO consolidated guidelines on tuberculosis: Module 2: screening\u2013systematic screening for tuberculosis disease","year":"2021"},{"key":"10.1016\/j.landig.2025.100934_bib4","series-title":"Determining the local calibration of computer-assisted detection (CAD) thresholds and other parameters: a toolkit to support the effective use of CAD for TB screening","year":"2021"},{"key":"10.1016\/j.landig.2025.100934_bib5","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-019-51503-3","article-title":"Using artificial intelligence to read chest radiographs for tuberculosis detection: a multi-site evaluation of the diagnostic accuracy of three deep learning systems","volume":"9","author":"Qin","year":"2019","journal-title":"Sci Rep"},{"key":"10.1016\/j.landig.2025.100934_bib6","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0106381","article-title":"Diagnostic accuracy of computer-aided detection of pulmonary tuberculosis in chest radiographs: a validation study from sub-Saharan Africa","volume":"9","author":"Breuninger","year":"2014","journal-title":"PLoS One"},{"key":"10.1016\/j.landig.2025.100934_bib7","doi-asserted-by":"crossref","first-page":"e573","DOI":"10.1016\/S2589-7500(20)30221-1","article-title":"Chest x-ray analysis with deep learning-based software as a triage test for pulmonary tuberculosis: a prospective study of diagnostic accuracy for culture-confirmed disease","volume":"2","author":"Khan","year":"2020","journal-title":"Lancet Digit Health"},{"key":"10.1016\/j.landig.2025.100934_bib8","doi-asserted-by":"crossref","DOI":"10.1038\/srep25265","article-title":"An automated tuberculosis screening strategy combining x-ray-based computer-aided detection and clinical information","volume":"6","author":"Melendez","year":"2016","journal-title":"Sci Rep"},{"key":"10.1016\/j.landig.2025.100934_bib9","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0093757","article-title":"The sensitivity and specificity of using a computer aided diagnosis program for automatically scoring chest x-rays of presumptive TB patients compared with Xpert MTB\/RIF in Lusaka Zambia","volume":"9","author":"Muyoyeta","year":"2014","journal-title":"PLoS One"},{"key":"10.1016\/j.landig.2025.100934_bib10","doi-asserted-by":"crossref","first-page":"e543","DOI":"10.1016\/S2589-7500(21)00116-3","article-title":"Tuberculosis detection from chest x-rays for triaging in a high tuberculosis-burden setting: an evaluation of five artificial intelligence algorithms","volume":"3","author":"Qin","year":"2021","journal-title":"Lancet Digit Health"},{"key":"10.1016\/j.landig.2025.100934_bib11","doi-asserted-by":"crossref","first-page":"157","DOI":"10.5588\/ijtld.22.0437","article-title":"CAD4TB software updates: different triaging thresholds require caution by users and regulation by authorities","volume":"27","author":"Fehr","year":"2023","journal-title":"Int J Tuberc Lung Dis"},{"key":"10.1016\/j.landig.2025.100934_bib12","article-title":"Diagnostic accuracy of chest x-ray interpretation for tuberculosis by three artificial intelligence-based software in a screening use-case: an individual patient meta-analysis of global data","author":"Kik","year":"2022","journal-title":"medRxiv"},{"key":"10.1016\/j.landig.2025.100934_bib13","doi-asserted-by":"crossref","DOI":"10.1038\/s41598-021-03265-0","article-title":"Independent evaluation of 12 artificial intelligence solutions for the detection of tuberculosis","volume":"11","author":"Codlin","year":"2021","journal-title":"Sci Rep"},{"key":"10.1016\/j.landig.2025.100934_bib14","doi-asserted-by":"crossref","DOI":"10.1093\/ofid\/ofae020","article-title":"Diagnostic accuracy of computer-aided detection during active case finding for pulmonary tuberculosis in Africa: a systematic review and meta-analysis","volume":"11","author":"Scott","year":"2024","journal-title":"Open Forum Infect Dis"},{"key":"10.1016\/j.landig.2025.100934_bib15","doi-asserted-by":"crossref","first-page":"e605","DOI":"10.1016\/S2589-7500(24)00118-3","article-title":"Computer-aided detection of tuberculosis from chest radiographs in a tuberculosis prevalence survey in South Africa: external validation and modelled impacts of commercially available artificial intelligence software","volume":"6","author":"Qin","year":"2024","journal-title":"Lancet Digit Health"},{"key":"10.1016\/j.landig.2025.100934_bib16","doi-asserted-by":"crossref","first-page":"2000","DOI":"10.1093\/cid\/ciad027","article-title":"Disease transmission by patients with subclinical tuberculosis","volume":"76","author":"Nguyen","year":"2023","journal-title":"Clin Infect Dis"},{"key":"10.1016\/j.landig.2025.100934_bib17","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pmed.1003752","article-title":"Computer-aided x-ray screening for tuberculosis and HIV testing among adults with cough in Malawi (the PROSPECT study): a randomised trial and cost-effectiveness analysis","volume":"18","author":"MacPherson","year":"2021","journal-title":"PLoS Med"},{"key":"10.1016\/j.landig.2025.100934_bib18","doi-asserted-by":"crossref","first-page":"e894","DOI":"10.1093\/cid\/ciac679","article-title":"The performance of computer-aided detection digital chest x-ray reading technologies for triage of active tuberculosis among persons with a history of previous tuberculosis","volume":"76","author":"Kagujje","year":"2023","journal-title":"Clin Infect Dis"},{"key":"10.1016\/j.landig.2025.100934_bib19","doi-asserted-by":"crossref","first-page":"106","DOI":"10.1038\/s41746-021-00471-y","article-title":"Computer-aided interpretation of chest radiography reveals the spectrum of tuberculosis in rural South Africa","volume":"4","author":"Fehr","year":"2021","journal-title":"NPJ Digit Med"},{"key":"10.1016\/j.landig.2025.100934_bib20","doi-asserted-by":"crossref","first-page":"1650","DOI":"10.1093\/cid\/ciab644","article-title":"Digital chest radiography enhances screening efficiency for pulmonary tuberculosis in primary health clinics in South Africa","volume":"74","author":"Moodley","year":"2022","journal-title":"Clin Infect Dis"},{"key":"10.1016\/j.landig.2025.100934_bib21","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pmed.1002119","article-title":"Sex differences in tuberculosis burden and notifications in low- and middle-income countries: a systematic review and meta-analysis","volume":"13","author":"Horton","year":"2016","journal-title":"PLoS Med"},{"key":"10.1016\/j.landig.2025.100934_bib22","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1016\/S1473-3099(21)00449-7","article-title":"Global, regional, and national sex differences in the global burden of tuberculosis by HIV status, 1990\u20132019: results from the Global Burden of Disease Study 2019","volume":"22","author":"Ledesma","year":"2022","journal-title":"Lancet Infect Dis"},{"key":"10.1016\/j.landig.2025.100934_bib23","doi-asserted-by":"crossref","first-page":"607","DOI":"10.1136\/thoraxjnl-2020-216123","article-title":"\u2018If not TB, what could it be?\u2019 Chest X-ray findings from the 2016 Kenya Tuberculosis Prevalence Survey","volume":"76","author":"Mungai","year":"2021","journal-title":"Thorax"},{"key":"10.1016\/j.landig.2025.100934_bib25","series-title":"The Uganda national tuberculosis prevalence survey, 2014\u20132015 survey report","year":"2016"},{"key":"10.1016\/j.landig.2025.100934_bib26","doi-asserted-by":"crossref","first-page":"543","DOI":"10.1007\/s11222-013-9448-7","article-title":"Shape constrained additive models","volume":"25","author":"Pya","year":"2015","journal-title":"Stat Comput"},{"key":"10.1016\/j.landig.2025.100934_bib27","article-title":"Long-term risk of tuberculosis among individuals with Xpert Ultra trace screening results in Uganda: a longitudinal follow-up study","author":"Sung","year":"2025","journal-title":"Lancet Infect Dis"},{"key":"10.1016\/j.landig.2025.100934_bib28","series-title":"Target product profiles for tuberculosis screening tests","year":"2025"},{"key":"10.1016\/j.landig.2025.100934_bib29","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0305126","article-title":"Simultaneous alleviation of verification and reference standard biases in a community-based tuberculosis screening study using Bayesian latent class analysis","volume":"19","author":"Keter","year":"2024","journal-title":"PLoS One"},{"key":"10.1016\/j.landig.2025.100934_bib30","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pgph.0001272","article-title":"Accuracy of computer-aided chest x-ray in community-based tuberculosis screening: lessons from the 2016 Kenya National Tuberculosis Prevalence Survey","volume":"2","author":"Mungai","year":"2022","journal-title":"PLOS Glob Public Health"}],"container-title":["The Lancet Digital Health"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2589750025001165?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S2589750025001165?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T20:29:27Z","timestamp":1780432167000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S2589750025001165"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,12]]},"references-count":29,"journal-issue":{"issue":"12","published-print":{"date-parts":[[2025,12]]}},"alternative-id":["S2589750025001165"],"URL":"https:\/\/doi.org\/10.1016\/j.landig.2025.100934","relation":{},"ISSN":["2589-7500"],"issn-type":[{"value":"2589-7500","type":"print"}],"subject":[],"published":{"date-parts":[[2025,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Performance of universal and stratified computer-aided detection thresholds for chest x-ray-based tuberculosis screening: a cross-sectional, diagnostic accuracy study","name":"articletitle","label":"Article Title"},{"value":"The Lancet Digital Health","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.landig.2025.100934","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Author(s). Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"100934"}}