Beyond Classification: A Cough Regression Benchmark for Respiratory Acoustic Foundation Models
arXiv:2606.15436v1 Announce Type: cross Abstract: Respiratory acoustic foundation models (FMs) excel at cough classification, yet their ability to predict continuous health quantities from cough audio remains largely unexplored, despite the clinical value of passive age, BMI, and disease probability estimation in settings where physical measurements are unavailable. We introduce the multi-model, multi-target cough regression benchmark evaluating five FMs (OPERA-CT, OPERA-CE, OPERA-GT, HeAR, M2D+Resp) across six targets on three datasets under subject-disjoint protocols, comparing linear, MLP-s
The proliferation of advanced AI models and growing datasets for biomedical applications allows for the development of more nuanced diagnostic tools beyond simple classification.
This research expands the utility of AI in healthcare, moving towards continuous, non-invasive health monitoring and prediction, which is critical for early intervention and remote care.
AI models for acoustic health monitoring are evolving from binary classification (e.g., diseased/healthy) to quantitative regression, enabling more precise predictions of continuous health metrics like age, BMI, and disease probability.
- · AI healthcare developers
- · Remote patient monitoring services
- · Patients with chronic respiratory conditions
- · Wearable tech companies
- · Traditional diagnostic methods reliant on in-person visits
- · Companies slow to adopt AI in medical diagnostics
Improved early detection and management of respiratory diseases through passive monitoring.
Increased demand for robust, privacy-preserving acoustic data collection and analysis infrastructure.
Potential for integration into smart home devices and ubiquitous sensors, transforming everyday environments into continuous health observatories.
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Read at arXiv cs.AI