{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T16:20:49Z","timestamp":1759335649903,"version":"3.40.4"},"reference-count":31,"publisher":"Public Library of Science (PLoS)","issue":"4","license":[{"start":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T00:00:00Z","timestamp":1744329600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"abstract":"\nBackground<\/jats:title>\nTuberculosis (TB) remains a significant public health challenge, particularly in rural areas of high-burden countries like China. Active case finding (ACF) and timely treatment have been proven effective in reducing TB prevalence, but the impact on the TB epidemic when employing new technologies in ACF is still unknown. This study aims to evaluate the effectiveness of a comprehensive ACF package utilizing mobile vans equipped with artificial intelligence (AI)-aided radiology and GeneXpert testing in reducing TB prevalence among high-risk populations in rural Guangxi, China.<\/jats:p>\n<\/jats:sec>\n\nMethods<\/jats:title>\nA pragmatic cluster randomized controlled trial will be conducted in two counties of Guangxi, China. The trial will randomize 23 townships to intervention or control groups at approximately 1:1 ratio. The intervention group will receive an ACF campaign in Year 1 among high-risk populations, incorporating visited by mobile vans equipped with AI-based digital X-ray screening, symptom assessment, and sputum collection for GeneXpert testing. Control group participants receive usual care. TB patients identified in Year 1 will complete their treatment in Year 2. The primary outcome is the prevalence rate of bacteriologically confirmed TB among high-risk populations in Year 3. Process evaluation will explore acceptability, feasibility and adaptation of the intervention. We will conduct incremental costing study to inform future scale-up of the intervention in other settings.<\/jats:p>\n<\/jats:sec>\n\nDiscussion<\/jats:title>\nThis study will provide valuable insights into the effectiveness and feasibility of utilizing AI-equipped mobile vans and GeneXpert for TB ACF to reduce TB prevalence in rural settings. If successful, this model will contribute to possible solutions to achieve the WHO End TB Strategy by 2035.<\/jats:p>\n<\/jats:sec>\n\nTrial registration<\/jats:title>\nClinicalTrials.gov NCT06702774<\/jats:ext-link><\/jats:p>\n<\/jats:sec>","DOI":"10.1371\/journal.pone.0316073","type":"journal-article","created":{"date-parts":[[2025,4,11]],"date-time":"2025-04-11T17:27:21Z","timestamp":1744392441000},"page":"e0316073","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":1,"title":["Active case finding using mobile vans with artificial intelligence aided radiology tests and sputum collection for rapid diagnostic tests to reduce tuberculosis prevalence among high-risk population in rural China: Protocol for a pragmatic 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