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For: Kulkarni S, Jha S. Artificial Intelligence, Radiology, and Tuberculosis: A Review. Acad Radiol 2020;27:71-5. [PMID: 31759796 DOI: 10.1016/j.acra.2019.10.003] [Cited by in Crossref: 15] [Cited by in F6Publishing: 18] [Article Influence: 5.0] [Reference Citation Analysis]
Number Citing Articles
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7 Battineni G, Hossain MA, Chintalapudi N, Amenta F. A Survey on the Role of Artificial Intelligence in Biobanking Studies: A Systematic Review. Diagnostics (Basel) 2022;12:1179. [PMID: 35626333 DOI: 10.3390/diagnostics12051179] [Cited by in Crossref: 3] [Cited by in F6Publishing: 2] [Article Influence: 3.0] [Reference Citation Analysis]
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9 Choudhury S, Chohan A, Dadhwal R, Vakil AP, Franco R, Taweesedt PT. Applications of artificial intelligence in common pulmonary diseases. Artif Intell Med Imaging 2022; 3(1): 1-7 [DOI: 10.35711/aimi.v3.i1.1] [Reference Citation Analysis]
10 Soffer S, Morgenthau AS, Shimon O, Barash Y, Konen E, Glicksberg BS, Klang E. Artificial Intelligence for Interstitial Lung Disease Analysis on Chest Computed Tomography: A Systematic Review. Acad Radiol 2022;29 Suppl 2:S226-35. [PMID: 34219012 DOI: 10.1016/j.acra.2021.05.014] [Cited by in Crossref: 3] [Cited by in F6Publishing: 3] [Article Influence: 3.0] [Reference Citation Analysis]
11 Sebhatu S, Pooja, Nand P. Intelligent System for Diagnosis of Pulmonary Tuberculosis Using XGBoosting Method. Smart Innovation, Systems and Technologies 2022. [DOI: 10.1007/978-981-19-2541-2_41] [Reference Citation Analysis]
12 Vorster M, Sathekge MM. Positron Emission Tomography (PET) Imaging in Tuberculosis. Nuclear Medicine and Molecular Imaging 2022. [DOI: 10.1016/b978-0-12-822960-6.00097-1] [Reference Citation Analysis]
13 Codlin AJ, Dao TP, Vo LNQ, Forse RJ, Van Truong V, Dang HM, Nguyen LH, Nguyen HB, Nguyen NV, Sidney-Annerstedt K, Squire B, Lönnroth K, Caws M. Independent evaluation of 12 artificial intelligence solutions for the detection of tuberculosis. Sci Rep 2021;11:23895. [PMID: 34903808 DOI: 10.1038/s41598-021-03265-0] [Cited by in Crossref: 5] [Cited by in F6Publishing: 5] [Article Influence: 2.5] [Reference Citation Analysis]
14 Herman B, Sirichokchatchawan W, Nantasenamat C, Pongpanich S. Artificial intelligence in overcoming rifampicin resistant-screening challenges in Indonesia: a qualitative study on the user experience of CUHAS-ROBUST. JHR 2021. [DOI: 10.1108/jhr-11-2020-0535] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
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16 Morozov SP, Kokina DY, Pavlov NA, Kirpichev YS, Gombolevskiy VA, Аndreychenko AE. Clinical aspects of using artificial intelligence for the interpretation of chest X-rays. Tuberk bolezni lëgk 2021;99:58-64. [DOI: 10.21292/2075-1230-2021-99-4-58-64] [Cited by in Crossref: 2] [Cited by in F6Publishing: 2] [Article Influence: 1.0] [Reference Citation Analysis]
17 Bigio J, Kohli M, Klinton JS, MacLean E, Gore G, Small PM, Ruhwald M, Weber SF, Jha S, Pai M. Diagnostic accuracy of point-of-care ultrasound for pulmonary tuberculosis: A systematic review. PLoS One 2021;16:e0251236. [PMID: 33961639 DOI: 10.1371/journal.pone.0251236] [Cited by in Crossref: 8] [Cited by in F6Publishing: 9] [Article Influence: 4.0] [Reference Citation Analysis]
18 Xiang Y, Huang C, He Y, Zhang Q. Cancer or Tuberculosis: A Comprehensive Review of the Clinical and Imaging Features in Diagnosis of the Confusing Mass. Front Oncol 2021;11:644150. [PMID: 33996560 DOI: 10.3389/fonc.2021.644150] [Cited by in Crossref: 3] [Cited by in F6Publishing: 5] [Article Influence: 1.5] [Reference Citation Analysis]
19 Hayashi H, Kimoto N, Asahara T, Asakawa T, Lee C, Katsumata A. Introduction to Physics in Medical X-ray Diagnosis. Photon Counting Detectors for X-ray Imaging 2021. [DOI: 10.1007/978-3-030-62680-8_2] [Reference Citation Analysis]
20 Hao X, Bai J, Ding Y, Wang J, Liu Y, Yao L, Pan W. Characterization of antibody response against 16kD and 38kD of M. tuberculosis in the assisted diagnosis of active pulmonary tuberculosis. Ann Transl Med 2020;8:945. [PMID: 32953745 DOI: 10.21037/atm-20-5476] [Cited by in Crossref: 1] [Cited by in F6Publishing: 1] [Article Influence: 0.3] [Reference Citation Analysis]
21 Jha S, Cook T. Artificial Intelligence in Radiology--The State of the Future. Acad Radiol 2020;27:1-2. [PMID: 31753720 DOI: 10.1016/j.acra.2019.11.003] [Cited by in Crossref: 6] [Cited by in F6Publishing: 7] [Article Influence: 2.0] [Reference Citation Analysis]
22 Munadi K, Muchtar K, Maulina N, Pradhan B. Image Enhancement for Tuberculosis Detection Using Deep Learning. IEEE Access 2020;8:217897-907. [DOI: 10.1109/access.2020.3041867] [Cited by in Crossref: 22] [Cited by in F6Publishing: 25] [Article Influence: 7.3] [Reference Citation Analysis]