External validation of cough-based algorithms for pulmonary tuberculosis screening from the CODA TB DREAM challenge using cough data from Peru.

The COugh Diagnostic Algorithm for Tuberculosis (CODA TB) DREAM Challenge recently evaluated the performance of artificial intelligence (AI) algorithms for tuberculosis (TB) screening using cough sounds. Eleven AI models were developed using a dataset of 733,756 cough sounds collected from 2143 adults from seven countries. This study evaluates the CODA Challenge AI models with an external independent cough dataset from Peru. Cough recordings from 303 coughing adults were collected from health facilities in Lima, Peru. The AUCs of the models ranged from 0.480 to 0.615, showing a decrease in performance compared to their performance when internally validated using the CODA Challenge, which ranged from 0.689 to 0.743. The best performing model in the CODA Challenge was also the best performing model in this external validation. Sub-group analyses revealed that models performed better in older (≥ 35 years) populations and among people with prior TB. The external validation revealed limitations in the generalizability of the CODA Challenge models to other settings. While some models showed promise, the overall performance decline highlights the need for continued model validation on external datasets. It also underscores the importance of developing context-specific models to account for population-specific factors that influence cough characteristics and TB prevalence.