Mancuso G, Midiri A, De Gaetano S, Ponzo E, Biondo C. Tackling drug-resistant tuberculosis: new challenges from the old pathogen Mycobacterium tuberculosis. Volume 11. Microorganisms; 2023. p. 2277.

Reid M, Agbassi YJP, Arinaminpathy N, Bercasio A, Bhargava A, Bhargava M, Bloom A, Cattamanchi A, Chaisson R, Chin D. Scientific advances and the end of tuberculosis: a report from the lancet commission on tuberculosis. Lancet. 2023;402:1473–98.

Article  PubMed  Google Scholar 

Meintjes G, Maartens G. HIV-Associated tuberculosis. N Engl J Med. 2024;391:343–55.

Article  CAS  PubMed  Google Scholar 

Carabalí-Isajar ML, Rodríguez-Bejarano OH, Amado T, Patarroyo MA, Izquierdo MA, Lutz JR, Ocampo M. Clinical manifestations and immune response to tuberculosis. World J Microbiol Biotechnol. 2023;39:206. https://doi.org/10.1007/s11274-023-03636-x.

Article  PubMed  PubMed Central  Google Scholar 

Li L-S, Yang L, Zhuang L, Ye Z-Y, Zhao W-G, Gong W-P. From immunology to artificial intelligence: revolutionizing latent tuberculosis infection diagnosis with machine learning. Military Med Res. 2023;10:58. https://doi.org/10.1186/s40779-023-00490-8.

Article  CAS  Google Scholar 

Koth LL, Solberg OD, Peng JC, Bhakta NR, Nguyen CP, Woodruff PG. Sarcoidosis blood transcriptome reflects lung inflammation and overlaps with tuberculosis. Am J Respir Crit Care Med. 2011;184:1153–63. https://doi.org/10.1164/rccm.201106-1143OC.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Orgeur M, Sous C, Madacki J, Brosch R. Evolution and emergence of Mycobacterium tuberculosis. FEMS Microbiol Rev. 2024;48:fuae006.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Albadrani M. Exploring the impact of silicosis incidence on tuberculosis mortality and morbidity: A Multi-Country study. Med Sci. 2023;11:63.

Google Scholar 

Jimma BL. Artificial intelligence in healthcare: A bibliometric analysis. Telematics Inf Rep. 2023;9:100041.

Article  Google Scholar 

Zatt FP, Rocha AO, Anjos LMD, Caldas RA, Cardoso M, Rabelo GD. Artificial intelligence applications in dentistry: A bibliometric review with an emphasis on computational research trends within the field. Journal of the American Dental Association (1939) 2024. https://doi.org/10.1016/j.adaj.2024.05.013

Tran BX, Vu GT, Ha GH, Vuong Q-H, Ho M-T, Vuong T-T, La V-P, Ho M-T, Nghiem K-CP, Nguyen HLT, et al. Global evolution of research in artificial intelligence in health and medicine: A bibliometric study. J Clin Med. 2019;8:360.

Article  PubMed  PubMed Central  Google Scholar 

Alkhammash R. Bibliometric, network, and thematic mapping analyses of metaphor and discourse in COVID-19 publications from 2020 to 2022. Front Psychol. 2023;13:1062943.

Article  PubMed  PubMed Central  Google Scholar 

Aria M, Cuccurullo C. Bibliometrix: an R-tool for comprehensive science mapping analysis. J Informetrics. 2017;11:959–75.

Article  Google Scholar 

Van Eck N, Waltman L. Software survey: vosviewer, a computer program for bibliometric mapping. Scientometrics. 2010;84:523–38.

Article  PubMed  Google Scholar 

Hu R, Guo S, Liu M. Knowledge map of thrombopoietin receptor agonists: A bibliometric analysis. Heliyon. 2024;10:e24051. https://doi.org/10.1016/j.heliyon.2024.e24051.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Guo SB, Feng XZ, Huang WJ, Zhou ZZ, Tian XP. Global research hotspots, development trends and prospect discoveries of phase separation in cancer: a decade-long informatics investigation. Biomark Res. 2024;12:39. https://doi.org/10.1186/s40364-024-00587-9.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Memon S, Bibi S, He G. Integration of AI and ML in tuberculosis (TB) management: from diagnosis to drug discovery. Volume 13. Diseases; 2025. p. 184.

Cabanillas-Lazo M, Quispe-Vicuña C, Pascual-Guevara M, Barja-Ore J, Guerrero ME, Munive-Degregori A, Mayta-Tovalino F. Bibliometric analyses of applications of artificial intelligence on tuberculosis. Int J Mycobacteriology. 2022;11:389–93.

Article  Google Scholar 

Asnicar F, Thomas AM, Passerini A, Waldron L, Segata N. Machine learning for microbiologists. Nat Rev Microbiol. 2024;22:191–205. https://doi.org/10.1038/s41579-023-00984-1.

Article  CAS  PubMed  Google Scholar 

Abdelwahab SI, Taha MME, Alfaifi HA, Farasani A, Hassan W. Bibliometric analysis of machine learning applications in ischemia research. Curr Probl Cardiol 2024, 102754, https://doi.org/10.1016/j.cpcardiol.2024.102754

Luo C, Mao B, Wu Y, He Y. The research hotspots and theme trends of artificial intelligence in nurse education: A bibliometric analysis from 1994 to 2023. Nurse Educ Today. 2024;141:106321. https://doi.org/10.1016/j.nedt.2024.106321.

Article  PubMed  Google Scholar 

Ekins S, Freundlich JS, Reynolds RC. Are bigger data sets better for machine learning? Fusing single-point and dual-event dose response data for Mycobacterium tuberculosis. J Chem Inf Model. 2014;54:2157–65. https://doi.org/10.1021/ci500264r.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Ekins S, Casey AC, Roberts D, Parish T, Bunin BA. Bayesian models for screening and TB mobile for target inference with Mycobacterium tuberculosis. Tuberculosis. 2014;94:162–9. https://doi.org/10.1016/j.tube.2013.12.001.

Article  CAS  PubMed  Google Scholar 

Ekins S, Freundlich JS, Hobrath JV, Lucile White E, Reynolds RC. Combining computational methods for hit to lead optimization in Mycobacterium tuberculosis drug discovery. Pharm Res. 2014;31:414–35. https://doi.org/10.1007/s11095-013-1172-7.

Article  CAS  PubMed  Google Scholar 

Lane T, Russo DP, Zorn KM, Clark AM, Korotcov A, Tkachenko V, Reynolds RC, Perryman AL, Freundlich JS, Ekins S. Comparing and validating machine learning models for Mycobacterium tuberculosis drug discovery. Mol Pharm. 2018;15:4346–60. https://doi.org/10.1021/acs.molpharmaceut.8b00083.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Korotcov A, Tkachenko V, Russo DP, Ekins S. Comparison of deep learning with multiple machine learning methods and metrics using diverse drug discovery data sets. Mol Pharm. 2017;14:4462–75. https://doi.org/10.1021/acs.molpharmaceut.7b00578.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Ekins S, Reynolds RC, Franzblau SG, Wan B, Freundlich JS, Bunin BA. Enhancing hit identification in Mycobacterium tuberculosis drug discovery using validated Dual-Event bayesian models. PLoS ONE. 2013;8. https://doi.org/10.1371/journal.pone.0063240.

Ekins S, Freundlich JS, Reynolds RC. Fusing dual-event data sets for Mycobacterium tuberculosis machine learning models and their evaluation. J Chem Inf Model. 2013;53:3054–63. https://doi.org/10.1021/ci400480s.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Ekins S, Pottorf R, Reynolds RC, Williams AJ, Clark AM, Freundlich JS. Looking back to the future: predicting in vivo efficacy of small molecules versus Mycobacterium tuberculosis. J Chem Inf Model. 2014;54:1070–82. https://doi.org/10.1021/ci500077v.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Zorn KM, Lane TR, Russo DP, Clark AM, Makarov V, Ekins S. Multiple machine learning comparisons of HIV Cell-based and reverse transcriptase data sets. Mol Pharm. 2019;16:1620–32. https://doi.org/10.1021/acs.molpharmaceut.8b01297.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Djaout K, Singh V, Boum Y, Katawera V, Becker HF, Bush NG, Hearnshaw SJ, Pritchard JE, Bourbon P, Madrid PB, et al. Predictive modeling targets thymidylate synthase ThyX in Mycobacterium tuberculosis. Sci Rep. 2016;6. https://doi.org/10.1038/srep27792.

Agranoff D, Fernandez-Reyes D, Papadopoulos MC, Rojas SA, Herbster M, Loosemore A, Tarelli E, Sheldon J, Schwenk A, Pollok R, et al. Identification of diagnostic markers for tuberculosis by proteomic fingerprinting of serum. Lancet. 2006;368:1012–21. https://doi.org/10.1016/S0140-6736(06)69342-2.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Abiyev RH, Ma’aitah MKS. Deep convolutional neural networks for chest diseases detection. J Healthc Eng. 2018;2018. https://doi.org/10.1155/2018/4168538.

Bharati S, Podder P, Mondal MRH. Hybrid deep learning for detecting lung diseases from X-ray images. Inf Med Unlocked. 2020;20. https://doi.org/10.1016/j.imu.2020.100391.

Harris M, Qi A, Jeagal L, Torabi N, Menzies D, Korobitsyn A, Pai M, Nathavitharana RR, Khan FA. A systematic review of the diagnostic accuracy of artificial intelligence-based computer programs to analyze chest x-rays for pulmonary tuberculosis. PLoS ONE. 2019;14. https://doi.org/10.1371/journal.pone.0221339.

Kavvas ES, Catoiu E, Mih N, Yurkovich JT, Seif Y, Dillon N, Heckmann D, Anand A, Yang L, Nizet V, et al. Machine learning and structural analysis of Mycobacterium tuberculosis pan-genome identifies genetic signatures of antibiotic resistance. Nat Commun. 2018;9. https://doi.org/10.1038/s41467-018-06634-y.

Melendez J, Van Ginneken B, Maduskar P, Philipsen RHHM, Reither K, Breuninger M, Adetifa IMO, Maane R, Ayles H, Sánchez CI. A novel multiple-instance learning-based approach to computer-aided detection of tuberculosis on chest X-rays. IEEE Trans Med Imaging. 2015;34:179–92.