{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T17:09:57Z","timestamp":1786122597031,"version":"build-2736575974"},"reference-count":20,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T00:00:00Z","timestamp":1781568000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>To develop and validate a machine learning-based model for diagnosing the presence of tuberculosis (TB) in patients with pneumoconiosis, utilizing complex clinical data to support early identification and clinical decision-making.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This retrospective case-control study analyzed the risk of TB among patients with pneumoconiosis using data extracted from electronic medical records. A total of 325 patients with occupational pneumoconiosis who were admitted to Beijing Chest Hospital, Shilong Hospital, and Fuxing Hospital between January 2019 and June 2024 were included. Participants were classified into a case group (pneumoconiosis with TB) and a control group (pneumoconiosis without TB). Diagnostic variables included demographic characteristics, occupational exposure history, medical history, laboratory parameters, etiological indicators, and imaging features. The study outcome was the occurrence of TB. Multivariable regression analysis was performed to identify independent risk factors for concomitant pulmonary TB. Based on the selected variables, eight machine learning models were developed to construct diagnostic algorithms. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis. The best-performing model was further interpreted using the Shapley Additive Explanations (SHAP) framework.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Regression analysis identified ten key diagnostic factors associated with TB in pneumoconiosis patients: etiological type, serum albumin, PaO\n                      <jats:sub>2<\/jats:sub>\n                      \/FiO\n                      <jats:sub>2<\/jats:sub>\n                      ratio, HbA1c, calcification, bilateral lung lesions, cavitation, prior antibiotic use, pleural effusion, and duration of dust exposure cessation. These diagnostic factors were further classified into three categories: occupational factors (etiological type, duration of dust exposure cessation), nutritional and metabolic indicators (serum albumin, PaO\n                      <jats:sub>2<\/jats:sub>\n                      \/FiO\n                      <jats:sub>2<\/jats:sub>\n                      ratio, HbA1c), and radiological features (calcification, bilateral lung lesions, cavitation, pleural effusion). Eight machine learning models were developed using these variables. After comprehensive evaluation of discrimination, calibration, clinical utility, and model stability, the random forest (RF) model demonstrated the best overall performance. The RF model achieved an AUC of 0.997 (95% CI: 0.994\u20131.000) in the training set and 0.905 (95% CI: 0.833\u20130.977) in the validation set, which outperformed other models.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Patients with pneumoconiosis are at a high risk of developing TB. Machine learning models can effectively aid in identifying TB in this population, with the RF model showing superior diagnostic performance. This approach may facilitate early auxiliary diagnosis and timely clinical intervention.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1817334","type":"journal-article","created":{"date-parts":[[2026,6,16]],"date-time":"2026-06-16T12:31:25Z","timestamp":1781613085000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Construction and application of a diagnostic model for tuberculosis in patients with pneumoconiosis"],"prefix":"10.3389","volume":"13","author":[{"given":"Shuang","family":"Qu","sequence":"first","affiliation":[{"name":"Department of Respiratory and Critical Care Medicine, Beijing Chest Hospital, Capital Medical University","place":["Beijing, China"]},{"name":"Department of Respiratory and Critical Care Medicine, Fuxing Hospital, Capital Medical University","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shiping","family":"Hu","sequence":"additional","affiliation":[{"name":"Department of Occupational Diseases, Shilong Hospital, National Center for Occupational Safety and Health, National Health Commission","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Zhu","sequence":"additional","affiliation":[{"name":"Department of Respiratory and Critical Care Medicine, Beijing Chest Hospital, Capital Medical University","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Huan","family":"Ye","sequence":"additional","affiliation":[{"name":"Department of Respiratory and Critical Care Medicine, Beijing Chest Hospital, Capital Medical University","place":["Beijing, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,6,16]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"407","DOI":"10.1136\/oemed-2019-106321","article-title":"Trends in global, regional and national incidence of pneumoconiosis caused by different aetiologies: an analysis from the Global Burden of Disease Study 2017.","volume":"77","author":"Shi","year":"2020","journal-title":"Occup Environ Med."},{"key":"B2","doi-asserted-by":"publisher","first-page":"898","DOI":"10.1097\/CM9.0000000000001461","article-title":"Pneumoconiosis: current status and future prospects.","volume":"134","author":"Qi","year":"2021","journal-title":"Chin Med J."},{"key":"B3","volume-title":"Transcript of the press conference on April 25, 2022.","year":"2022"},{"key":"B4","doi-asserted-by":"publisher","first-page":"730","DOI":"10.3760\/cma.j.cn112147-20240208-00077","article-title":"[Chinese expert consensus on treatment and management of chronic obstructive pulmonary disease combined with pneumoconiosis].","volume":"47","year":"2024","journal-title":"Zhonghua Jie He He Hu Xi Za Zhi."},{"key":"B5","unstructured":"2022 Statistical Bulletin on the Development of China\u2019s Public Health.\n          \n          2023"},{"key":"B6","unstructured":"2023 Statistical Bulletin on the Development of China\u2019s Public Health.\n          \n          2024"},{"key":"B7","doi-asserted-by":"publisher","first-page":"3069","DOI":"10.3389\/fimmu.2018.03069","article-title":"Immunity to the dual threat of silica exposure and Mycobacterium tuberculosis.","volume":"9","author":"Kone\u010dn\u00fd","year":"2018","journal-title":"Front Immunol."},{"key":"B8","doi-asserted-by":"publisher","first-page":"1507","DOI":"10.1016\/s0891-5849(03)00149-7","article-title":"Reactive Oxygen Species (ROS) and Reactive Nitrogen Species (RNS) generation by silica in inflammation and fibrosis.","volume":"34","author":"Fubini","year":"2003","journal-title":"Free Radic Biol Med."},{"key":"B9","doi-asserted-by":"publisher","first-page":"2984","DOI":"10.1097\/CM9.0000000000002486","article-title":"Epidemiological characteristics of pulmonary tuberculosis in patients with pneumoconiosis based on its social determinants and risk factors in China: a cross-sectional study from 27 provinces.","volume":"135","author":"Wang","year":"2022","journal-title":"Chin Med J."},{"key":"B10","doi-asserted-by":"publisher","first-page":"E8","DOI":"10.21037\/jtd.2018.12.113","article-title":"Can treatment outcomes of latent TB infection and TB in silicosis be improved.","volume":"11","author":"Yew","year":"2019","journal-title":"J Thorac Dis."},{"key":"B11","doi-asserted-by":"publisher","first-page":"e343","DOI":"10.1016\/S2468-2667(25)00014-3","article-title":"Confronting the growing epidemic of silicosis and tuberculosis among small-scale miners.","volume":"10","author":"Howlett","year":"2025","journal-title":"Lancet Public Health."},{"key":"B12","volume-title":"Framework for Collaborative Action on Tuberculosis and Comorbidities.","year":"2022"},{"key":"B13","doi-asserted-by":"publisher","first-page":"e0002085","DOI":"10.1371\/journal.pgph.0002085","article-title":"Silicosis, tuberculosis and silica exposure among artisanal and small-scale miners: a systematic review and modelling paper.","volume":"3","author":"Howlett","year":"2023","journal-title":"PLoS Glob Public Health."},{"key":"B14","doi-asserted-by":"publisher","first-page":"519","DOI":"10.3760\/cma.j.cn121094-20200820-00479","article-title":"[Analysis of influencing factors of pneumoconiosis complicated with pulmonary tuberculosis].","volume":"39","author":"Zheng","year":"2021","journal-title":"Zhonghua Lao Dong Wei Sheng Zhi Ye Bing Za Zhi."},{"key":"B15","doi-asserted-by":"publisher","first-page":"2008","DOI":"10.1016\/S0140-6736(12)60235-9","article-title":"Silicosis.","volume":"379","author":"Leung","year":"2012","journal-title":"Lancet."},{"key":"B16","doi-asserted-by":"publisher","first-page":"40","DOI":"10.1038\/s41580-021-00407-0","article-title":"A guide to machine learning for biologists.","volume":"23","author":"Greener","year":"2022","journal-title":"Nat Rev Mol Cell Biol."},{"key":"B17","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1016\/j.chest.2022.07.005","article-title":"Machine-Learning model for mortality prediction in patients with community-acquired pneumonia: development and validation study.","volume":"163","author":"Cilloniz","year":"2023","journal-title":"Chest."},{"key":"B18","doi-asserted-by":"publisher","first-page":"94","DOI":"10.1186\/s12931-021-01690-3","article-title":"Development and performance assessment of novel machine learning models to predict pneumonia after liver transplantation.","volume":"22","author":"Chen","year":"2021","journal-title":"Respir Res."},{"key":"B19","doi-asserted-by":"publisher","first-page":"e69998","DOI":"10.2196\/69998","article-title":"Using machine learning methods to predict early treatment outcomes for multidrug-resistant or rifampicin-resistant tuberculosis to enhance patient cure rates: development and validation of multiple models.","volume":"27","author":"Zhang","year":"2025","journal-title":"J Med Internet Res."},{"key":"B20","doi-asserted-by":"publisher","first-page":"2416791","DOI":"10.1016\/j.pulmoe.2023.05.001","article-title":"Silicosis and tuberculosis: a systematic review and meta-analysis.","volume":"31","author":"Jamshidi","year":"2025","journal-title":"Pulmonology."}],"updated-by":[{"DOI":"10.3389\/fmed.2026.1942373","type":"correction","label":"Correction","source":"publisher","updated":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T00:00:00Z","timestamp":1786060800000}}],"container-title":["Frontiers in Medicine"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2026.1817334\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T10:54:41Z","timestamp":1786100081000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2026.1817334\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,16]]},"references-count":20,"alternative-id":["10.3389\/fmed.2026.1817334"],"URL":"https:\/\/doi.org\/10.3389\/fmed.2026.1817334","relation":{},"ISSN":["2296-858X"],"issn-type":[{"value":"2296-858X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,16]]},"article-number":"1817334"}}