Published on in Vol 26 (2024)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/58413, first published .
Development and Validation of Deep Learning–Based Infectivity Prediction in Pulmonary Tuberculosis Through Chest Radiography: Retrospective Study

Development and Validation of Deep Learning–Based Infectivity Prediction in Pulmonary Tuberculosis Through Chest Radiography: Retrospective Study

Development and Validation of Deep Learning–Based Infectivity Prediction in Pulmonary Tuberculosis Through Chest Radiography: Retrospective Study

Journals

  1. Zhang F, Han H, Li M, Tian T, Zhang G, Yang Z, Guo F, Li M, Wang Y, Wang J, Liu Y. Revolutionizing diagnosis of pulmonary Mycobacterium tuberculosis based on CT: a systematic review of imaging analysis through deep learning. Frontiers in Microbiology 2025;15 View
  2. Rudolph J, Huemmer C, Preuhs A, Buizza G, Dinkel J, Koliogiannis V, Fink N, Goller S, Schwarze V, Heimer M, Hoppe B, Liebig T, Ricke J, Sabel B, Rueckel J. Threshold optimization in AI chest radiography analysis: integrating real-world data and clinical subgroups. European Radiology Experimental 2025;9(1) View

Conference Proceedings

  1. Teja M, Singh D. 2025 12th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions) (ICRITO). TBNet: A Deep Learning Framework for Automated Tuberculosis Detection from Chest X-Ray Images View