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Evaluation of efficiency of artificial intelligence for chest radiograph interpretation for pulmonary tuberculosis screening in mobile x-ray vehicle |
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| รหัสดีโอไอ | |
| Creator | Khemipa Sanklaa |
| Title | Evaluation of efficiency of artificial intelligence for chest radiograph interpretation for pulmonary tuberculosis screening in mobile x-ray vehicle |
| Publisher | Faculty of Associated Sciences, Chiang Mai University |
| Publication Year | 2564 |
| Journal Title | Journal of Associated Medical Sciences |
| Journal Vol. | 54 |
| Journal No. | 3 |
| Page no. | 43-47 |
| Keyword | Pulmonary tuberculosis, artificial intelligence, chest radiograph, quality control, portable x-ray |
| URL Website | https://he01.tci-thaijo.org/index.php/bulletinAMS/index |
| Website title | Journal of Associated Medical Sciences |
| ISSN | 25396066 |
| Abstract | Background: Tuberculosis (TB) is one of the major global health threats. The chest radiograph (CXR) is one of the primary tools for detecting TB, especially pulmonary TB. Artificial Intelligence (AI) is increasingly used with radiological technology by developing AI software for health screening by CXR.Objectives: To compare the pulmonary TB screening results between the radiologist and AI software from the mobile x-ray screening vehicle of the Faculty of Allied Health Sciences, Thammasat University.Materials and methods: 449 patients (408 normal, 41 abnormal) were exposed for chest radiograph at the mobile x-ray screening vehicle of Faculty of Allied Health Sciences, Thammasat University. The retrospective data was randomly collected between 2016 and 2018. The methods were divided into three steps: quality control for the x-ray machine, transferring the radiograph from digital radiography to PACS and AI, and displaying the results on the monitor with the StatPlus program.Results: The mobile x-ray machine has passed the quality control test. In addition, the TB interpretation by AI showed Area Under Curve of 0.859 and the study demonstrated high specificity of 0.995 but low sensitivity of 0.722. The positive predictive value (PPV) was 0.951, which was less than the Negative Predictive Value of 0.963.Conclusion: Artificial intelligence is becoming a healthcare supporter to help radiologists analyze and interpret chest radiographs and provide a fast diagnosis. |
