{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T18:27:59Z","timestamp":1792693679525,"version":"4.5.0"},"reference-count":13,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Public Health Research"],"published-print":{"date-parts":[[2025,10]]},"abstract":"<jats:p>Artificial intelligence (AI)-enhanced ultraportable X-ray machines have been recently introduced in our region as tools for community-based pulmonary tuberculosis (TB) screening. Compared to the previously used method of relying solely on \u201cany symptoms of tuberculosis,\u201d chest X-ray screening is a more efficient and practical approach. Subclinical TB cases, who often lack overt clinical symptoms, may still be infectious and can present with radiographic abnormalities detectable on chest X-rays. Therefore, a parallel screening strategy combining chest X-rays and symptom assessment, followed by confirmatory sputum testing, is effective in detecting both clinical and subclinical TB cases (with X-ray abnormalities) within the community. The integration of this algorithm, along with AI-assisted portable X-ray devices, represents a significant advancement in community TB screening, although some limitations remain.<\/jats:p>","DOI":"10.1177\/22799036251399279","type":"journal-article","created":{"date-parts":[[2025,11,24]],"date-time":"2025-11-24T11:18:37Z","timestamp":1763983117000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["AI X-ray for tuberculosis screening in remote Nepal: Benefits and challenges from a doctor\u2019s perspective"],"prefix":"10.1177","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-6458-2160","authenticated-orcid":false,"given":"Ravi","family":"Shukla","sequence":"first","affiliation":[{"name":"Bhim Hospital, Siddharthanagar, Lumbini Province, Nepal"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,11,24]]},"reference":[{"key":"e_1_3_2_2_2","volume-title":"Chest radiography in tuberculosis detection: summary of current WHO recommendations and guidance on programmatic approaches","author":"World Health Organization","year":"2016","unstructured":"World Health Organization. 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Licence: CC BY-NC-SA 3.0 IGO."},{"issue":"3","key":"e_1_3_2_12_2","article-title":"Development and validation of a deep learning\u2013based automated detection algorithm for major thoracic diseases on chest radiographs","volume":"2","author":"Hwang EJ","year":"2019","unstructured":"Hwang EJ, Park S, Jin K-N, et al. Development and validation of a deep learning\u2013based automated detection algorithm for major thoracic diseases on chest radiographs. Lancet Digit Health 2019; 2(3): e271\u2013e279.","journal-title":"Lancet Digit Health"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pgph.0003986"},{"issue":"4","key":"e_1_3_2_14_2","article-title":"Development and evaluation of an artificial intelligence-assisted chest X-ray diagnostic system for detecting, diagnosing, and monitoring tuberculosis","volume":"5","author":"Wong A","year":"2023","unstructured":"Wong A, Lin Y, Sanchez M, et al. Development and evaluation of an artificial intelligence-assisted chest X-ray diagnostic system for detecting, diagnosing, and monitoring tuberculosis. 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