Diagnostic Accuracy of the Artificial Intelligence Methods in Medical Imaging for Pulmonary Tuberculosis: A Systematic Review and Meta-Analysis
Tuberculosis (TB) remains one of the leading causes of death among infectious diseases worldwide. Early screening and diagnosis of pulmonary tuberculosis (PTB) is crucial in TB control, and tend to benefit from artificial intelligence. Here, we aimed to evaluate the diagnostic efficacy of a variety...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-12-01
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| Series: | Journal of Clinical Medicine |
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| Online Access: | https://www.mdpi.com/2077-0383/12/1/303 |
| _version_ | 1797625480661172224 |
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| author | Yuejuan Zhan Yuqi Wang Wendi Zhang Binwu Ying Chengdi Wang |
| author_facet | Yuejuan Zhan Yuqi Wang Wendi Zhang Binwu Ying Chengdi Wang |
| author_sort | Yuejuan Zhan |
| collection | DOAJ |
| description | Tuberculosis (TB) remains one of the leading causes of death among infectious diseases worldwide. Early screening and diagnosis of pulmonary tuberculosis (PTB) is crucial in TB control, and tend to benefit from artificial intelligence. Here, we aimed to evaluate the diagnostic efficacy of a variety of artificial intelligence methods in medical imaging for PTB. We searched MEDLINE and Embase with the OVID platform to identify trials published update to November 2022 that evaluated the effectiveness of artificial-intelligence-based software in medical imaging of patients with PTB. After data extraction, the quality of studies was assessed using quality assessment of diagnostic accuracy studies 2 (QUADAS-2). Pooled sensitivity and specificity were estimated using a bivariate random-effects model. In total, 3987 references were initially identified and 61 studies were finally included, covering a wide range of 124,959 individuals. The pooled sensitivity and the specificity were 91% (95% confidence interval (CI), 89–93%) and 65% (54–75%), respectively, in clinical trials, and 94% (89–96%) and 95% (91–97%), respectively, in model-development studies. These findings have demonstrated that artificial-intelligence-based software could serve as an accurate tool to diagnose PTB in medical imaging. However, standardized reporting guidance regarding AI-specific trials and multicenter clinical trials is urgently needed to truly transform this cutting-edge technology into clinical practice. |
| first_indexed | 2024-03-11T09:57:06Z |
| format | Article |
| id | doaj.art-9b45150bcf584549b985a93f8e137bf9 |
| institution | Directory Open Access Journal |
| issn | 2077-0383 |
| language | English |
| last_indexed | 2024-03-11T09:57:06Z |
| publishDate | 2022-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Journal of Clinical Medicine |
| spelling | doaj.art-9b45150bcf584549b985a93f8e137bf92023-11-16T15:44:16ZengMDPI AGJournal of Clinical Medicine2077-03832022-12-0112130310.3390/jcm12010303Diagnostic Accuracy of the Artificial Intelligence Methods in Medical Imaging for Pulmonary Tuberculosis: A Systematic Review and Meta-AnalysisYuejuan Zhan0Yuqi Wang1Wendi Zhang2Binwu Ying3Chengdi Wang4Department of Respiratory and Critical Care Medicine, West China Medical School/West China Hospital, Sichuan University, Chengdu 610041, ChinaDepartment of Respiratory and Critical Care Medicine, West China Medical School/West China Hospital, Sichuan University, Chengdu 610041, ChinaDepartment of Respiratory and Critical Care Medicine, West China Medical School/West China Hospital, Sichuan University, Chengdu 610041, ChinaDepartment of Laboratory Medicine, West China Medical School/West China Hospital, Sichuan University, Chengdu 610041, ChinaDepartment of Respiratory and Critical Care Medicine, West China Medical School/West China Hospital, Sichuan University, Chengdu 610041, ChinaTuberculosis (TB) remains one of the leading causes of death among infectious diseases worldwide. Early screening and diagnosis of pulmonary tuberculosis (PTB) is crucial in TB control, and tend to benefit from artificial intelligence. Here, we aimed to evaluate the diagnostic efficacy of a variety of artificial intelligence methods in medical imaging for PTB. We searched MEDLINE and Embase with the OVID platform to identify trials published update to November 2022 that evaluated the effectiveness of artificial-intelligence-based software in medical imaging of patients with PTB. After data extraction, the quality of studies was assessed using quality assessment of diagnostic accuracy studies 2 (QUADAS-2). Pooled sensitivity and specificity were estimated using a bivariate random-effects model. In total, 3987 references were initially identified and 61 studies were finally included, covering a wide range of 124,959 individuals. The pooled sensitivity and the specificity were 91% (95% confidence interval (CI), 89–93%) and 65% (54–75%), respectively, in clinical trials, and 94% (89–96%) and 95% (91–97%), respectively, in model-development studies. These findings have demonstrated that artificial-intelligence-based software could serve as an accurate tool to diagnose PTB in medical imaging. However, standardized reporting guidance regarding AI-specific trials and multicenter clinical trials is urgently needed to truly transform this cutting-edge technology into clinical practice.https://www.mdpi.com/2077-0383/12/1/303pulmonary tuberculosisartificial intelligencemedical imagingdiagnostic accuracysensitivityspecificity |
| spellingShingle | Yuejuan Zhan Yuqi Wang Wendi Zhang Binwu Ying Chengdi Wang Diagnostic Accuracy of the Artificial Intelligence Methods in Medical Imaging for Pulmonary Tuberculosis: A Systematic Review and Meta-Analysis Journal of Clinical Medicine pulmonary tuberculosis artificial intelligence medical imaging diagnostic accuracy sensitivity specificity |
| title | Diagnostic Accuracy of the Artificial Intelligence Methods in Medical Imaging for Pulmonary Tuberculosis: A Systematic Review and Meta-Analysis |
| title_full | Diagnostic Accuracy of the Artificial Intelligence Methods in Medical Imaging for Pulmonary Tuberculosis: A Systematic Review and Meta-Analysis |
| title_fullStr | Diagnostic Accuracy of the Artificial Intelligence Methods in Medical Imaging for Pulmonary Tuberculosis: A Systematic Review and Meta-Analysis |
| title_full_unstemmed | Diagnostic Accuracy of the Artificial Intelligence Methods in Medical Imaging for Pulmonary Tuberculosis: A Systematic Review and Meta-Analysis |
| title_short | Diagnostic Accuracy of the Artificial Intelligence Methods in Medical Imaging for Pulmonary Tuberculosis: A Systematic Review and Meta-Analysis |
| title_sort | diagnostic accuracy of the artificial intelligence methods in medical imaging for pulmonary tuberculosis a systematic review and meta analysis |
| topic | pulmonary tuberculosis artificial intelligence medical imaging diagnostic accuracy sensitivity specificity |
| url | https://www.mdpi.com/2077-0383/12/1/303 |
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