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In International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases

OBJECTIVES : This study aims to establish a diagnostic algorithm combining T-SPOT with CT image analysis based on deep learning (DL) for early differential diagnosis of non-tuberculous mycobacteria pulmonary disease (NTM-PD) and pulmonary tuberculosis (PTB).

METHODS : 1049 cases were enrolled including 467 NTM-PD and 582 PTB cases. 320 cases (160 NTM-PD and 160 PTB) were randomized as testing set, and were analyzed using T-SPOT combined with the DL model. The testing cases were first divided into T-SPOT positive and negative groups, and the DL model was then used to further separate the cases into four subgroups.

RESULTS : The precision was found to be 91.7% for the subgroup of T-SPOT negative and DL classified as NTM-PD and 89.8% for T-SPOT positive and DL classified as PTB, which covered 66.9% of the total cases, compared with the accuracy rate of 80.3% of T-SPOT alone. In the other two remaining groups where the T-SPOT prediction was inconsistent with the DL model, the accuracy was 73.0% and 52.2% separately.

CONCLUSIONS : Our study shows that the new diagnostic system combining T-SPOT with DL based CT image analysis can greatly improve the classification precision of NTM-PD and PTB, when the two method predictions were consistent.

Ying Chiqing, Li Xukun, Lv Shuangzhi, Du Peng, Chen Yunzhi, Fu Hongxin, Du Weibo, Xu Kaijin, Zhang Ying, Wu Wei


Artificial intelligence, Computed Tomography, Deep Learning, Non-tuberculous mycobacteria pulmonary disease, Pulmonary tuberculosis, T-SPOT