{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,4]],"date-time":"2026-08-04T23:39:16Z","timestamp":1785886756272,"version":"3.56.0"},"reference-count":37,"publisher":"Public Library of Science (PLoS)","issue":"1","license":[{"start":{"date-parts":[[2020,1,24]],"date-time":"2020-01-24T00:00:00Z","timestamp":1579824000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/publicdomain\/zero\/1.0\/"}],"funder":[{"DOI":"10.13039\/100005447","name":"Office of Information and Resource Management","doi-asserted-by":"crossref","award":["HHSN316201300006W\/HHSN27200002"],"award-info":[{"award-number":["HHSN316201300006W\/HHSN27200002"]}],"id":[{"id":"10.13039\/100005447","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Civilian Research and Development Foundation","award":["BOB1-31120-MK-13"],"award-info":[{"award-number":["BOB1-31120-MK-13"]}]}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS ONE"],"DOI":"10.1371\/journal.pone.0224445","type":"journal-article","created":{"date-parts":[[2020,1,24]],"date-time":"2020-01-24T13:57:45Z","timestamp":1579874265000},"page":"e0224445","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":36,"title":["Performance of Qure.ai automatic classifiers against a large annotated database of patients with diverse forms of tuberculosis"],"prefix":"10.1371","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2136-8069","authenticated-orcid":true,"given":"Eric","family":"Engle","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Andrei","family":"Gabrielian","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alyssa","family":"Long","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9829-8567","authenticated-orcid":true,"given":"Darrell E.","family":"Hurt","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alex","family":"Rosenthal","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2020,1,24]]},"reference":[{"key":"pone.0224445.ref001","unstructured":"WHO Global tuberculosis report 2018, World Health Organization. http:\/\/www.who.int\/tb\/publications\/global_report\/en\/"},{"key":"pone.0224445.ref002","unstructured":"WHO World Health Organization. (\u200e2016)\u200e. Chest radiography in tuberculosis detection: summary of current WHO recommendations and guidance on programmatic approaches. World Health Organization. http:\/\/www.who.int\/iris\/handle\/10665\/252424"},{"key":"pone.0224445.ref003","unstructured":"WHO World Health Organization. (\u200e2013)\u200e. Systematic screening for active tuberculosis: principles and recommendations. World Health Organization. http:\/\/www.who.int\/iris\/handle\/10665\/84971"},{"key":"pone.0224445.ref004","doi-asserted-by":"crossref","first-page":"570","DOI":"10.1016\/S1473-3099(06)70578-3","article-title":"Fluorescence versus conventional sputum smear microscopy for tuberculosis: a systematic review","volume":"6","author":"KR Steingart","year":"2006","journal-title":"Lancet Infect Dis"},{"key":"pone.0224445.ref005","article-title":"Xpert MTB\/RIF Ultra for detection of Mycobacterium tuberculosis and rifampicin resistance: a prospective multicentre diagnostic accuracy study","author":"E Dorman S","year":"2017","journal-title":"Lancet Infect Dis"},{"key":"pone.0224445.ref006","volume-title":"Chest Imaging: An Algorithmic Approach to Learning","author":"Springer","year":"2012"},{"key":"pone.0224445.ref007","article-title":"Can tuberculosis patients in resource-constrained settings afford chest radiography?","author":"D Pedrazzoli","year":"2016","journal-title":"European Respiratory Journal"},{"key":"pone.0224445.ref008","doi-asserted-by":"crossref","first-page":"481","DOI":"10.1007\/s13244-016-0493-6","article-title":"\u201cEuropean Society of Radiology (ESR) and American College of Radiology (ACR) Report of the 2015 Global Summit on Radiological Quality and Safety.","volume":"7.4","author":"ESR European Society of Radiology (ESR), and American College of Radiology (ACR)","year":"2016","journal-title":"Insights into Imaging"},{"key":"pone.0224445.ref009","unstructured":"Diagnostic Imaging, Radiologist sightings drop around the world, Jul. 14, 2003, http:\/\/www.diagnosticimaging.com\/article\/radiologist-sightings-drop-around-world"},{"key":"pone.0224445.ref010","doi-asserted-by":"crossref","first-page":"136","DOI":"10.1186\/s12879-016-1460-z","article-title":"Chest Radiograph Reading and Recording System: Evaluation in Frontline Clinicians in Zambia","volume":"16","author":"German Henostroza","year":"2016","journal-title":"BMC Infectious Diseases"},{"key":"pone.0224445.ref011","unstructured":"DeVries G. \u201cOutreach programme of tuberculosis screening and strengthening care integration in Romania\u201d In: 49th Union World Conference Abstract Book, October 2018, The Hague."},{"key":"pone.0224445.ref012","unstructured":"Zaidi S., Adam A., Azeemi KS., Habib SS., Madhani F., Safdar N., et al. \u201cIdentification of potential TB hot-spots through a mobile X-ray supported community-based mass screening program in Karachi, Pakistan\u201d In: 49th Union World Conference Abstract Book, October 2018, The Hague."},{"key":"pone.0224445.ref013","unstructured":"Lovelace Jr. B. GE\u2019s health unit wins first FDA clearance for A.I.-powered X-ray system. https:\/\/www.cnbc.com\/2019\/09\/12\/ges-health-unit-wins-first-fda-clearance-for-ai-powered-x-ray-system.html"},{"key":"pone.0224445.ref014","doi-asserted-by":"crossref","first-page":"233","DOI":"10.1109\/TMI.2013.2284099","article-title":"Automatic Tuberculosis Screening Using Chest Radiographs","volume":"33","author":"S Jaeger","year":"2014","journal-title":"IEEE Transactions on Medical Imaging"},{"key":"pone.0224445.ref015","article-title":"Deep Learning Algorithms with Demographic Information Help to Detect Tuberculosis in Chest Radiographs in Annual Workers' Health Examination Data","author":"SJ Heo","year":"2019","journal-title":"Int J Environ Res Public Health"},{"key":"pone.0224445.ref016","article-title":"Deep Learning at Chest Radiography: Automated Classification of Pulmonary Tuberculosis by Using Convolutional Neural Networks","author":"P Lakhani","year":"2017","journal-title":"Radiology"},{"key":"pone.0224445.ref017","article-title":"Development and Validation of a Deep Learning-Based Automatic Detection Algorithm for Active Pulmonary Tuberculosis on Chest Radiographs","author":"EJ Hwang","year":"2018","journal-title":"Clin Infect Dis"},{"key":"pone.0224445.ref018","article-title":"Comparing deep learning models for population screening using chest radiography.\u201d Medical Imaging 2018: Computer-Aided Diagnosis","volume":"10575","author":"R Sivaramakrishnan","year":"2018","journal-title":"International Society for Optics and Photonics"},{"issue":"2","key":"pone.0224445.ref019","doi-asserted-by":"crossref","first-page":"537","DOI":"10.1148\/radiol.2018181422","article-title":"Assessment of Convolutional Neural Networks for Automated Classification of Chest Radiographs","volume":"290","author":"JA Dunnmon","year":"2019","journal-title":"Radiology"},{"issue":"Suppl 1","key":"pone.0224445.ref020","first-page":"1","article-title":"Efficacy of deep learning for screening pulmonary tuberculosis\u201d","volume":"9","author":"P Putha","year":"2018","journal-title":"ESR 2018 Book of Abstracts, Insights Imaging"},{"key":"pone.0224445.ref021","article-title":"Can Artificial Intelligence Reliably Report Chest X-Rays?: Radiologist Validation of an Algorithm trained on 1.2 Million X-Rays","author":"P Putha","year":"2018","journal-title":"arXiv:1807.07455"},{"key":"pone.0224445.ref022","unstructured":"Ridley E., \u201cIs seeing believing in imaging artificial intelligence?\u201d, Available at: https:\/\/www.auntminnie.com\/index.aspx?sec=sup&sub=aic&pag=dis&ItemID=119347, Accessed January 18, 2019."},{"issue":"10","key":"pone.0224445.ref023","doi-asserted-by":"crossref","first-page":"e0204155","DOI":"10.1371\/journal.pone.0204155","article-title":"Deep learning in chest radiography: Detection of findings and presence of change","volume":"13","author":"R Singh","year":"2018","journal-title":"PLOS ONE"},{"key":"pone.0224445.ref024","first-page":"2019","article-title":"Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison","volume":"07031","author":"J Irvin","year":"1901","journal-title":"arXiv"},{"key":"pone.0224445.ref025","article-title":"ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases","author":"X Wang","year":"2017","journal-title":"IEEE CVPR"},{"key":"pone.0224445.ref026","article-title":"Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists","author":"P Rajpurkar","year":"2018","journal-title":"PLoS Med"},{"key":"pone.0224445.ref027","article-title":"The TB Portals: An open-access, web-based platform for global drug-resistant tuberculosis data sharing and analysis","author":"A Rosenthal","year":"2017","journal-title":"J Clin Microbiol"},{"key":"pone.0224445.ref028","article-title":"Tuberculosis and lung damage: from epidemiology to pathophysiology","author":"S Ravimohan","year":"2018","journal-title":"European Respiratory Review"},{"key":"pone.0224445.ref029","unstructured":"Liauchuk V, Kovalev V, Kalinovsky A, Tarasau A, Gabrielian A, Rosenthal A. Examining the ability of convolutional neural networks to detect lesions in lung CT images, Proceedings of International Congress on Computer Assisted Radiology and Surgery. 2017."},{"key":"pone.0224445.ref030","unstructured":"Roman, V. How to Develop a Machine Learning Model from Scratch, Towards Data Science, https:\/\/towardsdatascience.com\/machine-learning-general-process-8f1b510bd8af"},{"key":"pone.0224445.ref031","article-title":"Efficient Deep Network Architectures for Fast Chest X-Ray Tuberculosis Screening and Visualization","author":"F Pasa","year":"2019","journal-title":"Sci Rep"},{"key":"pone.0224445.ref032","article-title":"Artificial intelligence outperforms pulmonologists in the interpretation of pulmonary function tests","author":"M Topalovic","year":"2019","journal-title":"Eur Respir J"},{"key":"pone.0224445.ref033","unstructured":"Nielsen M., Neural Networks and Deep Learning, Chap 4, http:\/\/neuralnetworksanddeeplearning.com\/chap4.html"},{"key":"pone.0224445.ref034","unstructured":"The Economist, \u201cWhy Uber\u2019s self-driving car killed a pedestrian\u201d https:\/\/www.economist.com\/the-economist-explains\/2018\/05\/29\/why-ubers-self-driving-car-killed-a-pedestrian"},{"key":"pone.0224445.ref035","unstructured":"WHO Tuberculosis country profiles (2017), https:\/\/www.who.int\/tb\/country\/data\/profiles\/en\/"},{"key":"pone.0224445.ref036"},{"key":"pone.0224445.ref037","doi-asserted-by":"crossref","first-page":"160018","DOI":"10.1038\/sdata.2016.18","article-title":"The FAIR Guiding Principles for scientific data management and stewardship","volume":"3","author":"MD Wilkinson","year":"2016","journal-title":"Sci Data"}],"container-title":["PLOS ONE"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0224445","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2020,1,24]],"date-time":"2020-01-24T13:58:02Z","timestamp":1579874282000},"score":1,"resource":{"primary":{"URL":"https:\/\/dx.plos.org\/10.1371\/journal.pone.0224445"}},"subtitle":[],"editor":[{"given":"Julian C.","family":"Hong","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"editor"}]}],"short-title":[],"issued":{"date-parts":[[2020,1,24]]},"references-count":37,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2020,1,24]]}},"URL":"https:\/\/doi.org\/10.1371\/journal.pone.0224445","relation":{},"ISSN":["1932-6203"],"issn-type":[{"value":"1932-6203","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,24]]}}}