The Development and Evaluation of AI-based Tuberculosis Screening with a Digital Stethoscope used to Capture Lung Sounds. A Case-Control Study

Abstract Background Tuberculosis (TB) remains a leading global cause of preventable death, with 10.8 million cases and 1.3 million deaths reported in 2023. Current methods for TB screening include symptom-based screening, and chest X-ray (CXR) with computer-aided detection (CAD-CXR). Each method has limitations related to cost, accessibility, and screening efficacy. As a result, an estimated 2.6 million TB cases were missed in 2023. AI-based TB screening using lung sounds captured by a digital stethoscope offers a potential solution to these challenges, enhancing access, efficacy, and cost-efficiency. Methods A dataset comprising 49,770 anonymized chest auscultation recordings from 1,659 participants (cases and controls) were collected by trained nurses in South Africa’s Western Cape province from June 2021 to November 2022 using AI Diagnostics’ prototype digital stethoscope. Consenting participants suspected to have TB that reported a recent sputum TB Xpert Ultra test were recruited from 34 primary care clinics. After stratification and data preparation, a final dataset of 1,169 participants was partitioned into an 80% training and 20% hold-out test set. A pre-trained transformer- based architecture was fine-tuned using K-fold cross-validation. The ensemble model’s ability to predict pulmonary TB was evaluated on the hold-out test set, with sputum Xpert Ultra as the reference standard. Results The AI model achieved a mean Area under the Receiver Operating Curve (AUC-ROC) of 0.79 (95% CI: 0.73-0.85). At a sensitivity of 89.9% (95% CI: 82.4%-94.4%), the ensemble model has a specificity of 50.4% (95% CI: 42.0%–58.7%) for predicting pulmonary TB using lung sounds. Conclusion AI-based digital chest auscultation for TB, with a sensitivity of 89.9% and specificity of 50.4% in this study, shows early promise as an alternative or adjunct to current TB screening methods. In addition, the method’s portability and low cost have the potential to significantly improve TB screening access. Future independent studies in diverse, unselected populations with high TB prevalence are required to validate model generalizability. Key messages What is already known on this topic Tuberculosis (TB) is a major global health issue, especially in low-resource areas. Existing screening methods like symptom checks, chest X-rays, and CAD tools are often costly, hard to access, or not sensitive enough. AI has shown promise in detecting other lung conditions using sound, but its use for TB screening has not been well studied. What this study adds This is the first large study showing early promise that AI can detect TB from lung sounds using a digital stethoscope. This technology could be further developed as a low-cost and portable screening tool which aligns well with the World Health Organization’s End TB Strategy. How this study might affect research, practice, or policy This study would encourage further research into AI-based auscultation in different populations and settings, helping build more the evidence base supporting the use of AI in disease screening. Further research would also support the development of more accurate and generalizable models. In clinical practice AI-based digital stethoscopes could be used for early TB screening, allowing faster diagnosis and treatment. This would be especially important in asymptomatic TB cases where symptom-based screening would miss all cases. From a policy perspective, the results of this study would support further research which may support the inclusion of this technology in national and global TB screening guidelines and WHO endorsement. This study was commercially funded by the technology provider, AI Diagnostics Pty (Ltd).