Beyond isolated cough events: AI-based tuberculosis screening through temporal analysis of cough sounds

Tuberculosis (TB) is a major global health challenge, with many cases remaining undiagnosed due to limited access to screening and diagnostic services. Artificial intelligence (AI) systems based on cough sound analysis offer a scalable and accessible approach to TB screening, but most previous studies have analysed isolated cough events, despite the possibility that diagnostically useful information is encoded in the temporal dynamics of cough episodes. We evaluated an AI-based screening framework using cough recordings collected under real-world clinical conditions from 500 participants in Zambia, including 201 individuals with bacteriologically confirmed TB, 150 symptomatic patients with other respiratory diseases, and 149 healthy controls. Using multiple pre-trained speech foundation models fine-tuned on cough sounds, we systematically investigated the influence of temporal context by varying the audio input window from 1 to 6 s, measured from the onset of each cough episode. Across all evaluated models, diagnostic performance consistently peaked with a 3 s input window, indicating that useful information extends beyond individual cough events and is encoded within the short-term temporal dynamics of cough episodes. The best audio-only model achieved an area under the receiver operating characteristic curve (AUROC) of 85.2% for distinguishing TB from all other participants and 80.1% for distinguishing TB from symptomatic non-TB respiratory disease. Incorporating demographic and clinical variables improved AUROC to 92.1% and 84.2%, respectively. Performance remained robust across recording devices, participants with HIV co-infection, and varying acoustic conditions. These findings demonstrate that preserving temporal context improves AI-based cough screening for TB and suggest that analysing cough episodes, rather than isolated cough events, may enhance diagnostic performance in real-world settings. More broadly, the results highlight the importance of temporal context in the design of future respiratory sound datasets and AI-based diagnostic systems.