Evaluating the Usefulness of Artificial Intelligence-based Chest X-Ray Screening in Improving Tuberculosis Detection Among the High-Risk Tribal Population of Chhattisgarh, India: A Prospective Multi-Centre Study

TL;DR

This study shows that AI-based chest X-ray screening can significantly improve tuberculosis detection in a high-risk tribal population in India.

Contribution

The study demonstrates the effectiveness of AI in identifying TB cases, including asymptomatic ones, in a resource-limited tribal setting.

Findings

AI flagged 363 patients as TB presumptive, with a 44.63% TB confirmation rate.

AI identified 20 asymptomatic TB cases that would likely have been missed by symptom-based screening.

TB case notifications increased by 80.21% during the AI implementation period.

Abstract

India accounts for the highest Tuberculosis (TB) burden globally. The incidence and prevalence of TB are higher in tribal population than general population. In this study, we assessed the effectiveness of artificial intelligence (AI) based chest X-ray (CXR) interpretation software device (qXR version 3), in detecting TB from a predominantly tribal population setting.

In this multicenter prospective study, all the CXRs of patients aged > 15 years taken for any reason at 3 public health facilities in the Chhattisgarh state of India between 01 August 2023 and 31 March 2024 were included. Patients flagged by AI as TB presumptive were directed to undergo sputum testing, who are subsequently confirmed either microbiologically or clinically.

Out of 2745 CXRs screened, 363 patients (median age, 44 years [IQR: 30–53]; 261 [71.9%] male) were identified as presumptive for TB. 162 cases were confirmed with TB positivity rate of 44.63% (95% CI: 39.44–49.91). Among the AI-flagged cases, 51 (14.04%) patients were asymptomatic, and 20 (39.22%) of them were confirmed with TB. Descriptively, when compared with baseline (August-2022 to March-2023), an 80.21% (P < .001) increase in the number of TB case notifications was observed during the AI implemented period.

This study highlights the potential of AI to enhance TB detection and feasibility in a resource-limited tribal setting. Above 40% of the patients flagged by AI were subsequently confirmed to have the TB disease. Additionally, the study demonstrated the potential of AI in identifying asymptomatic individuals who would otherwise have been missed or diagnosed late.

This article presents the findings of a multicenter, prospective study that evaluated the effectiveness of AI software for analog chest X-ray interpretation in identifying tuberculosis (TB) among high-risk tribal population. The AI-enabled workflow have supplemented symptomatic screening, enhancing TB detection at the facilities with high positivity rate. Notably, the AI also identified asymptomatic patients who would have been missed otherwise in typical symptom-based TB screening approach.

Graphical AbstractThis graphical abstract is also available at Tidbit: https://tidbitapp.io/tidbits/evaluating-the-usefulness-of-artificial-intelligence-based-chest-x-ray-screening-in-improving-tuberculosis-detection-among-the-high-risk-tribal-population-of-chhattisgarh-india-a-prospective-multi-centre-study?utm_campaign=tidbitlinkshare&utm_source=ITP

This graphical abstract is also available at Tidbit: https://tidbitapp.io/tidbits/evaluating-the-usefulness-of-artificial-intelligence-based-chest-x-ray-screening-in-improving-tuberculosis-detection-among-the-high-risk-tribal-population-of-chhattisgarh-india-a-prospective-multi-centre-study?utm_campaign=tidbitlinkshare&utm_source=ITP

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