Nationwide longitudinal evaluation of a machine learning approach for enhanced interpretation of Xpert MTB/RIF ultra rifampicin-resistance results in low bacterial load tuberculosis specimens

Background: The World Health Organization (WHO) has identified tuberculosis (TB) as the leading cause of death from a single infectious agent. False-positive rifampicin (RIF) resistance results from the Xpert MTB/RIF Ultra assay are common in TB patients with low bacterial loads, especially among HI...

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Автори:	Tai-Han Lin, Hsing-Yi Chung, Ming-Jr Jian, Chih-Kai Chang, Yun-Wen Lai, Cherng-Lih Perng, Feng-Yee Chang, Yuan-Hao Chen, Hung-Sheng Shang
Формат:	Artigo
Мова:	Inglês
Опубліковано:	Elsevier 2026-02-01
Серія:	Journal of Infection and Public Health
Предмети:	AI-CDSS Rifampicin resistance Machine learning Tuberculosis Xpert MTB/RIF Ultra
Онлайн доступ:	http://www.sciencedirect.com/science/article/pii/S1876034125004137
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