Development of diagnostic algorithm using machine learning for distinguishing between active tuberculosis and latent tuberculosis infection

作者：Ying Luo, Ying Xue, Wei Liu, Huijuan Song, Yi Huang, Guoxing Tang, Feng Wang, Qi Wang, Yimin Cai, Ziyong Sun · 发表于：BMC Infectious Diseases · 年份：2022 · DOI：10.1186/s12879-022-07954-7 · 被引用次数：24 · 研究领域：Tuberculosis Research and Epidemiology、Image Processing Techniques and Applications、COVID-19 diagnosis using AI

BACKGROUND: The discrimination between active tuberculosis (ATB) and latent tuberculosis infection (LTBI) remains challenging. The present study aims to investigate the value of diagnostic models established by machine learning based on multiple laboratory data for distinguishing Mycobacterium tuberculosis (Mtb) infection status. METHODS: T-SPOT, lymphocyte characteristic detection, and routine laboratory tests were performed on participants. Diagnostic models were built according to various algorithms. RESULTS: A total of 892 participants (468 ATB and 424 LTBI) and another 263 participants (125 ATB and 138 LTBI), were respectively enrolled at Tongji Hospital (discovery cohort) and Sino-French New City Hospital (validation cohort). Receiver operating characteristic (ROC) curve analysis showed that the value of individual indicator for differentiating ATB from LTBI was limited (area under the ROC curve (AUC) < 0.8). A total of 28 models were successfully established using machine learning. Among them, the AUCs of 25 models were more than 0.9 in test set. It was found that conditional random forests (cforest) model, based on the implementation of the random forest and bagging ensemble algorithms utilizing conditional inference trees as base learners, presented best discriminative power in segregating ATB from LTBI. Specially, cforest model presented an AUC of 0.978, with the sensitivity of 93.39% and the specificity of 91.18%. Mtb-specific response represented by early secreted...