Accelerating cough-based algorithms for pulmonary tuberculosis screening: Results from the CODA TB DREAM Challenge

Abstract Importance Open-access data challenges have the potential to accelerate innovation in artificial-intelligence (AI)-based tools for global health. A specimen-free rapid triage method for TB is a global health priority. Objective To develop and validate cough sound-based AI algorithms for tuberculosis (TB) through the Cough Diagnostic Algorithm for Tuberculosis (CODA TB) DREAM challenge. Design In this diagnostic study, participating teams were provided cough-sound and clinical and demographic data. They were asked to develop AI models over a four-month period, and then submit the algorithms for independent validation. Setting Data was collected using smartphones from outpatient clinics in India, Madagascar, the Philippines, South Africa, Tanzania, Uganda, and Vietnam. Participants We included data from 2,143 adults who were consecutively enrolled with at least two weeks of cough. Data were randomly split evenly into training and test partitions. Exposures Standard TB evaluation was completed, including Xpert MTB/RIF Ultra and culture. At least three solicited coughs were recorded using the Hyfe Research app. Main Outcomes and Measures We invited teams to develop models using 1) cough sound features only and/or 2) cough sound features with routinely available clinical data to classify microbiologically confirmed TB disease. 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 were submitted, as well as six cough-plus-clinical models. AUROCs for cough models ranged from 0.69-0.74, and the highest performing model achieved 55.5% specificity (95% CI 47.7-64.2) at 80% sensitivity. The addition of clinical data improved AUROCs (range 0.78-0.83), five of the six submitted models reached the target pAUROC, and highest performing model had 73.8% (95% CI 60.8-80.0) specificity at 80% sensitivity. In post-challenge subgroup analyses, AUROCs varied by country, and was higher among males and HIV-negative individuals. The probability of TB classification correlated with Xpert Ultra semi-quantitative levels. Conclusions and Relevance In a short period, new and independently validated cough-based TB algorithms were developed through an open-source and transparent process. Open-access data challenges can rapidly advance and improve AI-based tools for global health. Key Points Question Can an open-access data challenge support the rapid development of cough-based artificial intelligence (AI) algorithms to screen for tuberculosis (TB)? Findings In this diagnostic study, teams were provided well-characterized cough sound data from seven countries, and developed and submitted AI models for independent validation. Multiple models that combined clinical and cough data achieved the target accuracy of at least 80% sensitivity and 60% specificity to classify microbiologically-confirmed TB. Meaning Cough-based AI models have promise to support point-of-care TB screening, and open-access data challenges can accelerate the development of AI-based tools for global health.

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