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. 2024 Jan 2;52(1):2.
doi: 10.1186/s41182-023-00560-6.

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

Kosuke Okada  1   2 , Norio Yamada  3 , Kiyoko Takayanagi  4 , Yuta Hiasa  5 , Yoshiro Kitamura  5 , Yutaka Hoshino  3 , Susumu Hirao  3 , Takashi Yoshiyama  3   4 , Ikushi Onozaki  3   6 , Seiya Kato  3
Affiliations

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

Kosuke Okada et al. Trop Med Health. .

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 needed to generalize the results to other countries by increasing the sample size and comparing the AI-CAD performance with that of more human readers.

Keywords: Active case finding; Artificial intelligence; CXR screening; Computer-aided detection; Pulmonary tuberculosis; Ultra-portable CXR.

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Conflict of interest statement

YK and YH are employees of FUJIFILM Corporation, and were not involved in data interpretation and performance evaluation of F-CAD. Other authors have no conflict of interests.

Figures

Fig. 1
Fig. 1
Interquartile ranges of TB scores by classification of human readings for chest X-ray
Fig. 2
Fig. 2
Performance of F-CAD and human reading
Fig. 3
Fig. 3
Precision Recall curve for F-CAD and human reading
Fig. 4
Fig. 4
Proposed algorithm for community-based ACF
See this image and copyright information in PMC

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