Applicability of artificial intelligence-based computer-aided detection (AI–CAD) for pulmonary tuberculosis to community-based active case finding

• Published in: Tropical Medicine and Health
• Main Authors: Kosuke Okada, Norio Yamada, Kiyoko Takayanagi, Yuta Hiasa, Yoshiro Kitamura, Yutaka Hoshino, Susumu Hirao, Takashi Yoshiyama, Ikushi Onozaki, Seiya Kato
• Format: Article in Journal/Newspaper
• Language: English
• Published: BMC 2024
• Subjects: Pulmonary tuberculosis; Artificial intelligence; Computer-aided detection; Active case finding; Ultra-portable CXR; CXR screening; Arctic medicine. Tropical medicine; RC955-962; Arctic
• Online Access: https://doi.org/10.1186/s41182-023-00560-6; https://doaj.org/article/30ed4a9dfa42403ba1426c25c4aa2a93

Abstract Background Artificial intelligence-based computer-aided detection (AI–CAD) for tuberculosis (TB) has become commercially available and several studies have been conducted to evaluate the performance of AI–CAD for pulmonary tuberculosis (TB) in clinical settings. However, little is known about its applicability to community-based active case-finding (ACF) for TB. Methods We analysed an anonymized data set obtained from a community-based ACF in Cambodia, targeting persons aged 55 years or over, persons with any TB symptoms, such as chronic cough, and persons at risk of TB, including household contacts. All of the participants in the ACF were screened by chest radiography (CXR) by Cambodian doctors, followed by Xpert test when they were eligible for sputum examination. Interpretation by an experienced chest physician and abnormality scoring by a newly developed AI–CAD were retrospectively conducted for the CXR images. With a reference of Xpert-positive TB or human interpretations, receiver operating characteristic (ROC) curves were drawn to evaluate the AI–CAD performance by area under the ROC curve (AUROC). In addition, its applicability to community-based ACFs in Cambodia was examined. Results TB scores of the AI–CAD were significantly associated with the CXR classifications as indicated by the severity of TB disease, and its AUROC as the bacteriological reference was 0.86 (95% confidence interval 0.83–0.89). Using a threshold for triage purposes, the human reading and bacteriological examination needed fell to 21% and 15%, respectively, detecting 95% of Xpert-positive TB in ACF. For screening purposes, we could detect 98% of Xpert-positive TB cases. Conclusions AI–CAD is applicable to community-based ACF in high TB burden settings, where experienced human readers for CXR images are scarce. The use of AI–CAD in developing countries has the potential to expand CXR screening in community-based ACFs, with a substantial decrease in the workload on human readers and laboratory labour. Further studies are ...