Contactless ultrasound chest vibration mapping discriminates respiratory and cardiac patients from healthy individuals.

Contactless assessment of cardiopulmonary function remains an unmet need, with current approaches relying either on subjective clinical examination or on resource-intensive imaging. We evaluated a novel multipoint airborne ultrasound surface motion camera (SMC) designed to map thoracic vibration patterns without contact and to extract clinically relevant information through data-driven analysis. In a prospective observational study, clinically characterised participants underwent short-duration acquisitions during natural breathing and externally induced oscillations. The resulting signals were transformed into spatially and frequency-resolved maps and analysed using machine learning models to discriminate healthy individuals from patients with respiratory or cardiac disease. The approach proved feasible in a clinical setting and achieved excellent discrimination between healthy individuals and respiratory patients (area under the receiver operating characteristic curve (AUC) 0.90 {+/-} 0.07), including in patients with subtle abnormalities not detected by pulmonary function testing. Discrimination between healthy individuals and cardiac patients ranged from acceptable to excellent (AUC 0.76-0.90 depending on subgroup), with the highest performance observed in aortic stenosis. Model interpretability analyses revealed spatial and spectral patterns consistent with the known physiological organisation of lung mechanics and cardiac auscultation areas, supporting a structure-function relationship between recorded signals and underlying processes. These findings indicate that thoracic vibration transmission encodes spatially and spectrally organised information that can be captured without contact and exploited through explainable data-driven modelling. While the results require confirmation in larger populations, this approach may represent an operator-independent, low-burden extension of bedside assessment, with potential applications in early detection, triage, and monitoring of cardiopulmonary disease.