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Kartik K Goswami et al. Cureus 2023 15(7) e41583
An AI-Enhanced Electronic Health Record Could Boost Primary Care Productivity.
Jeffrey E Harris et al. JAMA 2023 8
Diagnostic accuracy of three computer-aided detection systems for detecting pulmonary tuberculosis on chest radiography when used for screening: Analysis of an international, multicenter migrants screening study.
Sifrash Meseret Gelaw et al. PLOS Glob Public Health 2023 3(7) e0000402
A machine learning approach to explore individual risk factors for tuberculosis treatment non-adherence in Mukono district.
Haron W Gichuhi et al. PLOS Glob Public Health 2023 3(7) e0001466
Machine and Deep Learning for Tuberculosis Detection on Chest X-Rays: Systematic Literature Review.
Seng Hansun et al. J Med Internet Res 2023 25e43154
ReporTree: a surveillance-oriented tool to strengthen the linkage between pathogen genetic clusters and epidemiological data.
Verónica Mixão et al. Genome Med 2023 15(1) 43
Chest Radiography of Tuberculosis: Determination of Activity using Deep Learning Algorithm.
Ye Ra Choi et al. Tuberc Respir Dis (Seoul) 2023
A prospective observational multicentric clinical trial to evaluate microscopic examination of acid-fast bacilli in sputum by artificial intelligence-based microscopy system.
Prashant Gupta et al. J Investig Med 2023 10815589231171402
Machine learning approaches in diagnosing tuberculosis through biomarkers - A systematic review.
Vimala Balakrishnan et al. Progress in biophysics and molecular biology 2023 17916-25
Machine Learning Prediction Model of Tuberculosis Incidence Based on Meteorological Factors and Air Pollutants.
Na Tang et al. International journal of environmental research and public health 2023 20(5)
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Disclaimer: Articles listed in the Public Health Knowledge Base are selected by the CDC Office of Public Health Genomics to provide current awareness of the literature and news. Inclusion in the update does not necessarily represent the views of the Centers for Disease Control and Prevention nor does it imply endorsement of the article's methods or findings. CDC and DHHS assume no responsibility for the factual accuracy of the items presented. The selection, omission, or content of items does not imply any endorsement or other position taken by CDC or DHHS. Opinion, findings and conclusions expressed by the original authors of items included in the update, or persons quoted therein, are strictly their own and are in no way meant to represent the opinion or views of CDC or DHHS. References to publications, news sources, and non-CDC Websites are provided solely for informational purposes and do not imply endorsement by CDC or DHHS.