| Autor: |
Chiqing Ying, Xukun Li, Shuangzhi Lv, Peng Du, Yunzhi Chen, Hongxin Fu, Weibo Du, Kaijin Xu, Ying Zhang, Wei Wu |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
International Journal of Infectious Diseases, Vol 125, Iss , Pp 42-50 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
1201-9712 |
| DOI: |
10.1016/j.ijid.2022.09.031 |
| Popis: |
Objectives: This study aimed to establish a diagnostic algorithm combining T-SPOT with computed tomography image analysis based on deep learning (DL) for early differential diagnosis of nontuberculous mycobacteria pulmonary disease (NTM-PD) and pulmonary tuberculosis (PTB). Methods: A total of 1049 cases were enrolled, including 467 NTM-PD and 582 PTB cases. A total of 320 cases (160 NTM-PD and 160 PTB) were randomized as the 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 separate the cases into four subgroups further. 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. Conclusion: Our study shows that the new diagnostic system combining T-SPOT with DL based computed tomography image analysis can greatly improve the classification precision of NTM-PD and PTB when the two methods of prediction are consistent. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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