{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,17]],"date-time":"2026-04-17T03:17:59Z","timestamp":1776395879397,"version":"3.51.2"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,4,8]],"date-time":"2024-04-08T00:00:00Z","timestamp":1712534400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,4,8]],"date-time":"2024-04-08T00:00:00Z","timestamp":1712534400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100000060","name":"National Institute of Allergy and Infectious Diseases","doi-asserted-by":"publisher","award":["HHSN316201300006W\/75N93022F00001"],"award-info":[{"award-number":["HHSN316201300006W\/75N93022F00001"]}],"id":[{"id":"10.13039\/100000060","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Office of the Secretary Patient-Centered Outcomes Research Trust Fund","award":["750119PE080057"],"award-info":[{"award-number":["750119PE080057"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"DOI":"10.1007\/s10278-024-01052-7","type":"journal-article","created":{"date-parts":[[2024,4,8]],"date-time":"2024-04-08T15:02:12Z","timestamp":1712588532000},"page":"2173-2185","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["Automated Pulmonary Tuberculosis Severity Assessment on Chest X-rays"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6423-1647","authenticated-orcid":false,"given":"Karthik","family":"Kantipudi","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2258-3131","authenticated-orcid":false,"given":"Jingwen","family":"Gu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5749-3756","authenticated-orcid":false,"given":"Vy","family":"Bui","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3426-3872","authenticated-orcid":false,"given":"Hang","family":"Yu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6877-4318","authenticated-orcid":false,"given":"Stefan","family":"Jaeger","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0315-7727","authenticated-orcid":false,"given":"Ziv","family":"Yaniv","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,4,8]]},"reference":[{"key":"1052_CR1","unstructured":"World Health Organization. Global Tuberculosis Report 2022. Geneva: World Health Organization; 2022. Licence: CC BY-NC-SA 3.0 IGO."},{"issue":"2","key":"1052_CR2","doi-asserted-by":"publisher","first-page":"64","DOI":"10.4046\/trd.2015.78.2.64","volume":"78","author":"YJ Ryu","year":"2015","unstructured":"Ryu YJ. Diagnosis of Pulmonary Tuberculosis: Recent Advances and Diagnostic Algorithms. Tuberc Respir Dis (Seoul). 2015;78(2):64\u201371.","journal-title":"Tuberc Respir Dis (Seoul)."},{"issue":"3","key":"1052_CR3","doi-asserted-by":"publisher","first-page":"134","DOI":"10.4046\/trd.2016.79.3.134","volume":"79","author":"HE Leylabadlo","year":"2016","unstructured":"Leylabadlo HE, Kafil HS, Yousefi M, Aghazadeh M, Asgharzadeh M. Pulmonary Tuberculosis Diagnosis: Where We Are? Tuberc Respir Dis (Seoul). 2016;79(3):134\u2013142.","journal-title":"Tuberc Respir Dis (Seoul)."},{"issue":"10","key":"1052_CR4","doi-asserted-by":"publisher","first-page":"e01582","DOI":"10.1128\/JCM.01582-19","volume":"58","author":"E MacLean","year":"2020","unstructured":"MacLean E, Kohli M, Weber SF, Suresh A, Schumacher SG, Denkinger CM, et\u00a0al. Advances in Molecular Diagnosis of Tuberculosis. J Clin Microbiol. 2020;58(10):e01582\u201319.","journal-title":"J Clin Microbiol."},{"issue":"1","key":"1052_CR5","doi-asserted-by":"publisher","first-page":"15000","DOI":"10.1038\/s41598-019-51503-3","volume":"18","author":"ZZ Qin","year":"2019","unstructured":"Qin ZZ, Sander MS, Rai B, Titahong CN, Sudrungrot S, Laah SN, et\u00a0al. Using artificial intelligence to read chest radiographs for tuberculosis detection: A multi-site evaluation of the diagnostic accuracy of three deep learning systems. Sci Rep. 2019;18(1):15000.","journal-title":"Sci Rep."},{"issue":"11","key":"1052_CR6","doi-asserted-by":"publisher","first-page":"e573","DOI":"10.1016\/S2589-7500(20)30221-1","volume":"2","author":"FA Khan","year":"2020","unstructured":"Khan FA, Majidulla A, Tavaziva G, Nazish A, Abidi SK, Benedetti A, et\u00a0al. 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. Lancet Digit Health. 2020;2(11):e573\u2013e581.","journal-title":"Lancet Digit Health."},{"issue":"9","key":"1052_CR7","doi-asserted-by":"publisher","first-page":"e543","DOI":"10.1016\/S2589-7500(21)00116-3","volume":"3","author":"ZZ Qin","year":"2021","unstructured":"Qin ZZ, Ahmed S, Sarker MS, Paul K, Adel ASS, Naheyan T, et\u00a0al. Tuberculosis detection from chest x-rays for triaging in a high tuberculosis-burden setting: an evaluation of five artificial intelligence algorithms. Lancet Digit Health. 2021;3(9):e543\u2013e554.","journal-title":"Lancet Digit Health."},{"key":"1052_CR8","unstructured":"Monitoring treatment response. In: Companion handbook to the WHO guidelines for the programmatic management of drug-resistant tuberculosis; 2014. p. 139\u2013144."},{"issue":"10","key":"1052_CR9","doi-asserted-by":"publisher","first-page":"863","DOI":"10.1136\/thx.2010.136242","volume":"65","author":"AP Ralph","year":"2010","unstructured":"Ralph AP, Ardian M, Wiguna A, Maguire GP, Becker NG, Drogumuller G, et\u00a0al. A simple, valid, numerical score for grading chest x-ray severity in adult smear-positive pulmonary tuberculosis. Thorax. 2010;65(10):863\u2013869.","journal-title":"Thorax."},{"issue":"11","key":"1052_CR10","doi-asserted-by":"publisher","first-page":"1354","DOI":"10.5588\/ijtld.15.0098","volume":"19","author":"M Kriel","year":"2015","unstructured":"Kriel M, Lotz JW, Kidd M, Walzl G. Evaluation of a radiological severity score to predict treatment outcome in adults with pulmonary tuberculosis. Int J Tuberc Lung Dis. 2015;19(11):1354\u20131360.","journal-title":"Int J Tuberc Lung Dis."},{"issue":"10","key":"1052_CR11","doi-asserted-by":"publisher","first-page":"1358","DOI":"10.5588\/ijtld.16.0186","volume":"20","author":"B Thiel","year":"2016","unstructured":"Thiel B, Bark C, Nakibali J, Van Der\u00a0Kuyp F, Johnson J. Reader variability and validation of the Timika X-ray score during treatment of pulmonary tuberculosis. The International Journal of Tuberculosis and Lung Disease. 2016;20(10):1358\u20131363.","journal-title":"The International Journal of Tuberculosis and Lung Disease."},{"issue":"5","key":"1052_CR12","doi-asserted-by":"publisher","first-page":"205","DOI":"10.5603\/ARM.2018.0032","volume":"86","author":"A Chakraborthy","year":"2018","unstructured":"Chakraborthy A, Shivananjaiah AJ, Ramaswamy S, Chikkavenkatappa N. Chest X ray score (Timika score): an useful adjunct to predict treatment outcome in tuberculosis. Advances in respiratory medicine. 2018;86(5):205\u2013210.","journal-title":"Advances in respiratory medicine."},{"issue":"9","key":"1052_CR13","doi-asserted-by":"publisher","first-page":"e392","DOI":"10.1093\/cid\/ciaa054","volume":"71","author":"H Kornfeld","year":"2020","unstructured":"Kornfeld H, Sahukar SB, Procter-Gray E, Kumar NP, West K, Kane K, et\u00a0al. Impact of Diabetes and Low Body Mass Index on Tuberculosis Treatment Outcomes. Clin Infect Dis. 2020;71(9):e392\u2013e398.","journal-title":"Clin Infect Dis."},{"issue":"4","key":"1052_CR14","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1016\/j.ijtb.2021.08.004","volume":"69","author":"Y Krishnamoorthy","year":"2022","unstructured":"Krishnamoorthy Y, Knudsen S, Rajaa S, Lakshminarayanan S, Senbagavalli P, Ellner J, et\u00a0al. Accuracy of Timika X-ray scoring system to predict the treatment outcomes among tuberculosis patients in India. Indian Journal of Tuberculosis. 2022;69(4):476\u2013481.","journal-title":"Indian Journal of Tuberculosis."},{"key":"1052_CR15","doi-asserted-by":"crossref","unstructured":"Rosenfeld G, Gabrielian A, Hurt D, Rosenthal A. Predictive capabilities of baseline radiological findings for early and late disease outcomes within sensitive and multi-drug resistant tuberculosis cases. Eur J Radiol Open. 2023;11(100518).","DOI":"10.1016\/j.ejro.2023.100518"},{"key":"1052_CR16","unstructured":"Edwardsson S, Rizzoli A.: COVID-19 xray dataset. Last accessed July 2023. Available from: https:\/\/github.com\/v7labs\/covid-19-xray-dataset."},{"key":"1052_CR17","doi-asserted-by":"crossref","unstructured":"Liu Y, Wu YH, Ban Y, Wang H, Cheng MM. Rethinking computer-aided tuberculosis diagnosis. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition; 2020. p. 2646\u20132655.","DOI":"10.1109\/CVPR42600.2020.00272"},{"issue":"11","key":"1052_CR18","doi-asserted-by":"publisher","first-page":"3267","DOI":"10.1128\/JCM.01013-17","volume":"55","author":"A Rosenthal","year":"2017","unstructured":"Rosenthal A, Gabrielian A, Engle E, Hurt DE, Alexandru S, Crudu V, et\u00a0al. The TB portals: an open-access, web-based platform for global drug-resistant-tuberculosis data sharing and analysis. Journal of Clinical Microbiology. 2017;55(11):3267\u20133282.","journal-title":"Journal of Clinical Microbiology."},{"key":"1052_CR19","unstructured":"Paszke A, Gross S, Massa F, Lerer A, Bradbury J, Chanan G, et\u00a0al. Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems. 2019;32."},{"key":"1052_CR20","unstructured":"Cardoso MJ, Li W, Brown R, Ma N, Kerfoot E, Wang Y, et\u00a0al. MONAI: An open-source framework for deep learning in healthcare. arXiv preprint arXiv:2211.02701. 2022;."},{"key":"1052_CR21","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T. U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-Assisted Intervention (MICCAI). Springer; 2015. p. 234\u2013241.","DOI":"10.1007\/978-3-319-24574-4_28"},{"key":"1052_CR22","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition; 2016. p. 770\u2013778.","DOI":"10.1109\/CVPR.2016.90"},{"key":"1052_CR23","unstructured":"Jocher G, Chaurasia A, Stoken A, et\u00a0al.: YOLOv5 by Ultralytics."},{"key":"1052_CR24","doi-asserted-by":"crossref","unstructured":"Huang G, Liu Z, Weinberger KQ. Densely Connected Convolutional Networks. CoRR. 2016;abs\/1608.06993.","DOI":"10.1109\/CVPR.2017.243"},{"key":"1052_CR25","doi-asserted-by":"crossref","unstructured":"Karki M, Kantipudi K, Yu H, Yang F, Kassim YM, Yaniv Z, et\u00a0al. Identifying drug-resistant tuberculosis in chest radiographs: Evaluation of CNN architectures and training strategies. In: 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE; 2021. p. 2964\u20132967.","DOI":"10.1109\/EMBC46164.2021.9630189"},{"issue":"1","key":"1052_CR26","doi-asserted-by":"publisher","first-page":"188","DOI":"10.3390\/diagnostics12010188","volume":"12","author":"M Karki","year":"2022","unstructured":"Karki M, Kantipudi K, Yang F, Yu H, Wang YXJ, Yaniv Z, et\u00a0al. Generalization Challenges in Drug-Resistant Tuberculosis Detection from Chest X-rays. Diagnostics. 2022;12(1):188.","journal-title":"Diagnostics."},{"issue":"01","key":"1052_CR27","first-page":"2007","volume":"3","author":"H Abdi","year":"2007","unstructured":"Abdi H, et\u00a0al. Bonferroni and \u0160id\u00e1k corrections for multiple comparisons. Encyclopedia of measurement and statistics. 2007;3(01):2007.","journal-title":"Encyclopedia of measurement and statistics."},{"key":"1052_CR28","doi-asserted-by":"crossref","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D. Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. In: IEEE International Conference on Computer Vision, ICCV; 2017. p. 618\u2013626.","DOI":"10.1109\/ICCV.2017.74"},{"key":"1052_CR29","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2021.102125","volume":"72","author":"E \u00c7alli","year":"2021","unstructured":"\u00c7alli E, Sogancioglu E, van Ginneken B, van Leeuwen KG, Murphy K. Deep learning for chest X-ray analysis: A survey. Medical Image Anal. 2021;72:102125.","journal-title":"Medical Image Anal."},{"key":"1052_CR30","doi-asserted-by":"crossref","unstructured":"Samala RK, Hadjiiski L, Chan HP, Zhou C, Stojanovska J, Agarwal P, et\u00a0al. Severity assessment of COVID-19 using imaging descriptors: a deep-learning transfer learning approach from non-COVID-19 pneumonia. In: SPIE Medical Imaging: Computer-Aided Diagnosis; 2021. p. 115971T.","DOI":"10.1117\/12.2582115"},{"key":"1052_CR31","doi-asserted-by":"crossref","unstructured":"Li MD, Arun NT, Gidwani M, Chang K, Deng F, Little BP, et\u00a0al. Automated Assessment and Tracking of COVID-19 Pulmonary Disease Severity on Chest Radiographs Using Convolutional Siamese Neural Networks. Radiology: Artificial Intelligence. 2020;2(4):e200079.","DOI":"10.1148\/ryai.2020200079"},{"issue":"7","key":"1052_CR32","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0236621","volume":"15","author":"J Zhu","year":"2020","unstructured":"Zhu J, Shen B, Abbasi A, Hoshmand-Kochi M, Li H, Duong TQ. Deep transfer learning artificial intelligence accurately stages COVID-19 lung disease severity on portable chest radiographs. PLoS One. 2020;15(7):e0236621.","journal-title":"PLoS One."},{"key":"1052_CR33","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2022.106947","volume":"222","author":"TB Chandra","year":"2022","unstructured":"Chandra TB, Singh BK, Jain D. Disease Localization and Severity Assessment in Chest X-Ray Images using Multi-Stage Superpixels Classification. Computer Methods and Programs in Biomedicine. 2022;222:106947.","journal-title":"Computer Methods and Programs in Biomedicine."},{"key":"1052_CR34","doi-asserted-by":"publisher","DOI":"10.1016\/j.patcog.2022.108826","volume":"131","author":"A Sharma","year":"2022","unstructured":"Sharma A, Mishra PK. Covid-MANet: Multi-task attention network for explainable diagnosis and severity assessment of COVID-19 from CXR images. Pattern Recognition. 2022;131:108826.","journal-title":"Pattern Recognition."},{"key":"1052_CR35","doi-asserted-by":"crossref","unstructured":"Cavitary tuberculosis: the gateway of disease transmission. The Lancet Infectious Diseases. 2020;20(6):e117\u2013e128.","DOI":"10.1016\/S1473-3099(20)30148-1"},{"key":"1052_CR36","unstructured":"Ga\u00e1l G, Maga B, Luk\u00e1cs A. Attention U-Net Based Adversarial Architectures for Chest X-ray Lung Segmentation information. In: Proceedings of the Workshop on Applied Deep Generative Networks, 24th European Conference on Artificial Intelligence (ECAI; 2020. ."},{"key":"1052_CR37","doi-asserted-by":"publisher","first-page":"285","DOI":"10.1016\/j.cmpb.2019.06.005","volume":"177","author":"JC Souza","year":"2019","unstructured":"Souza JC, Diniz JOB, Ferreira JL, da\u00a0Silva GLF, Silva AC, de\u00a0Paiva AC. An automatic method for lung segmentation and reconstruction in chest X-ray using deep neural networks. Comput Methods Programs Biomed. 2019;177:285\u2013296.","journal-title":"Comput Methods Programs Biomed."},{"key":"1052_CR38","doi-asserted-by":"crossref","unstructured":"E L, Zhao B, Guo Y, Zheng C, Zhang M, Lin J, et\u00a0al. Using deep-learning techniques for pulmonary-thoracic segmentations and improvement of pneumonia diagnosis in pediatric chest radiographs. Pediatr Pulmonol. 2020;54(10):1617\u20131626.","DOI":"10.1002\/ppul.24431"},{"issue":"1","key":"1052_CR39","doi-asserted-by":"publisher","first-page":"210","DOI":"10.1038\/s41598-019-56589-3","volume":"10","author":"M Nash","year":"2020","unstructured":"Nash M, Kadavigere R, Andrade J, Sukumar CA, Chawla K, Shenoy VP, et\u00a0al. Deep learning, computer-aided radiography reading for tuberculosis: a diagnostic accuracy study from a tertiary hospital in India. Scientific reports. 2020;10(1):210.","journal-title":"Scientific reports."},{"issue":"3","key":"1052_CR40","doi-asserted-by":"publisher","first-page":"328","DOI":"10.5588\/ijtld.17.0520","volume":"22","author":"A Becker","year":"2018","unstructured":"Becker A, Bl\u00fcthgen C, Sekaggya-Wiltshire C, Castelnuovo B, Kambugu A, Fehr J, et\u00a0al. Detection of tuberculosis patterns in digital photographs of chest X-ray images using Deep Learning: feasibility study. The International Journal of Tuberculosis and Lung Disease. 2018;22(3):328\u2013335.","journal-title":"The International Journal of Tuberculosis and Lung Disease."},{"key":"1052_CR41","unstructured":"Bochkovskiy A, Wang CY, Liao HYM.: YOLOv4: Optimal Speed and Accuracy of Object Detection."},{"issue":"1","key":"1052_CR42","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1148\/rg.2017160032","volume":"37","author":"AC Nachiappan","year":"2017","unstructured":"Nachiappan AC, Rahbar K, Shi X, Guy ES, Mortani\u00a0Barbosa EJ, Shroff GS, et\u00a0al. Pulmonary Tuberculosis: Role of Radiology in Diagnosis and Management. RadioGraphics. 2017;37(1):52\u201372.","journal-title":"RadioGraphics."}],"container-title":["Journal of Imaging Informatics in Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-024-01052-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10278-024-01052-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10278-024-01052-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,10,29]],"date-time":"2024-10-29T08:30:21Z","timestamp":1730190621000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10278-024-01052-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,4,8]]},"references-count":42,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,10]]}},"alternative-id":["1052"],"URL":"https:\/\/doi.org\/10.1007\/s10278-024-01052-7","relation":{},"ISSN":["2948-2933"],"issn-type":[{"value":"2948-2933","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,4,8]]},"assertion":[{"value":"22 September 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 January 2024","order":2,"name":"revised","label":"Revised","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 February 2024","order":3,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"8 April 2024","order":4,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This study was exempt from local institutional review board review as it only used publicly available datasets (, , ).","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics Approval"}},{"value":"The authors declare no competing interests.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}]}}