Development and evaluation of an artificial intelligence (AI) -assisted chest x-ray diagnostic system for detecting, diagnosing, and monitoring tuberculosis
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Issued Date
2025-01-01
Resource Type
eISSN
25897918
Scopus ID
2-s2.0-85219526292
Journal Title
Global Transitions
Volume
7
Start Page
87
End Page
93
Rights Holder(s)
SCOPUS
Bibliographic Citation
Global Transitions Vol.7 (2025) , 87-93
Suggested Citation
Kaewwilai L., Yoshioka H., Choppin A., Prueksaritanond T., Ayuthaya T.P.N., Brukesawan C., Matupumanon S., Kawabe S., Shimahara Y., Phosri A., Kaewboonchoo O. Development and evaluation of an artificial intelligence (AI) -assisted chest x-ray diagnostic system for detecting, diagnosing, and monitoring tuberculosis. Global Transitions Vol.7 (2025) , 87-93. 93. doi:10.1016/j.glt.2025.02.005 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/106649
Title
Development and evaluation of an artificial intelligence (AI) -assisted chest x-ray diagnostic system for detecting, diagnosing, and monitoring tuberculosis
Corresponding Author(s)
Other Contributor(s)
Abstract
Objectives: To develop an artificial intelligence (AI)-assisted chest x-ray diagnostic system for the detection, differential diagnosis, and follow-up of tuberculosis (TB), and prove its usefulness. Methods: This is a retrospective study. In-house developed AI-assisted chest x-ray diagnostic system was used to identify and diagnose lung abnormalities in participants' chest x-rays and to compare imaging findings from two x-rays. First, 100 chest radiographs were reviewed including TB cases (N = 43) with positive sputum test confirmation and non-TB cases (N = 57) for initial diagnosis and differential diagnosis. Next, 45 pairs of TB cases from the identical patients were reviewed for follow-up. The AI system diagnosed TB and graded the comparison images into three categories (improved, stable, or worsening). The performance was evaluated by four expert radiologists or pulmonary medicine specialists. Results: The AI system demonstrated an exceptional sensitivity of 100 %, successfully identifying all 43 TB cases. Nevertheless, it is also susceptible to misclassify other diseases as TB, resulting in low specificity score of 66.7 %. The comparison function determined that expert physicians and AI-assisted chest x-ray diagnostic system were 58 % in exact agreement and 100 % in within one grade agreement. Conclusions: The AI system successfully detected all TB patients identified in this study and demonstrated a reasonable comparison function. Therefore, our AI assisted chest x-ray diagnostic system is feasible and practical for TB screening.