Out of the Laboratory and Into the Clinic

The results of this study demonstrate that pretrained ML models can achieve near-perfect VPD detection in highly standardized acoustic environments. However, models are highly susceptible to domain shift caused by variations in recording devices and acoustic environments. Out-of-domain testing results were mediocre but suggest that real-world clinical detection of VPD by ML models is feasible. These results shed light on the difficulties of real-world model generalization and emphasize that for clinically deployable diagnostic tools, cross-domain robustness is as important as, if not more important than, achieving maximal accuracy on a benchmark dataset. The development of a software-based screening tool has the power to improve VPD screening, particularly in low- and middle-income countries.