Accelerating Cough-Based Algorithms for Pulmonary Tuberculosis Screening: Results From the CODA TB DREAM Challenge
Publication Date
10-1-2025
Document Type
Article
Publication Title
Open Forum Infectious Diseases
Abstract
Background Open-access data challenges can accelerate innovation in artificial intelligence-based tools. In the Cough Diagnostic Algorithm for Tuberculosis (CODA TB) DREAM Challenge, we developed and independently validated cough sound-based artificial intelligence algorithms for tuberculosis screening. Methods We included data from 2143 adults with ≥2 weeks of cough from outpatient clinics in India, Madagascar, the Philippines, South Africa, Tanzania, Uganda, and Vietnam. A standard tuberculosis evaluation was completed, and ≥3 solicited coughs were recorded using a smartphone. We invited teams to develop models using training data to classify microbiologically confirmed tuberculosis disease using (1) cough sound features only and/or (2) cough sound features with routinely available clinical data. After 4 months, they submitted the algorithms for independent test set validation. Models were ranked by area under the receiver operating characteristic curve (AUROC) and partial AUROC (pAUROC) to achieve at least 80% sensitivity and 60% specificity. Results Eleven cough models and 6 cough-plus-clinical models were submitted. AUROCs for cough models ranged from 0.69 to 0.74, and the highest performing model achieved 55.5% specificity (95% confidence interval, 47.7%-64.2%) at 80% sensitivity. The addition of clinical data improved AUROCs (range, 0.78-0.83); 5 of the 6 models reached the target pAUROC, and the highest performing model had 73.8% specificity (95% confidence interval, 60.8%-80.0%) at 80% sensitivity. The AUROC varied by country and was higher among male and human immunodeficiency virus-negative individuals. Conclusions In a short period, an open-access data challenge facilitated the development of new cough-based tuberculosis algorithms and demonstrated potential as a tuberculosis screening tool.
APA Citation
Jaganath, D.,
Sieberts, S.,
Raberahona, M.,
Huddart, S.,
Omberg, L.,
Rakotoarivelo, R.,
Lyimo, I.,
Lweno, O.,
Christopher, D.,
Nhung, N.,
Worodria, W.,
Yu, C.,
Chen, J.,
Chen, S.,
Chen, T.,
Huang, C.,
Huang, K.,
Mulier, F.,
Rafter, D.,
Shih, E.,
Tsao, Y.,
Wang, H.,
Wu, C.,
Bachman, C.,
Burkot, S.,
Dewan, P.,
Kulhare, S.,
Small, P.,
Yadav, V.,
Grandjean Lapierre, S.,
Theron, G.,
Cattamanchi, A.,
&
Ahuja, G.
(10-1-2025).
Accelerating Cough-Based Algorithms for Pulmonary Tuberculosis Screening: Results From the CODA TB DREAM Challenge.
Faculty Research and Scholarly Works.
DOI:10.1093/ofid/ofaf572