Abstract #
Peripheral artery disease (PAD) is a common manifestation of atherosclerotic cardiovascular disease (ASCVD) that is underdiagnosed in clinical practice. Photoplethysmography (PPG) serves as a widely available tool that captures peripheral vascular physiology, yet the quantitative links between PPG signal characteristics and the presence of PAD are underexplored. In analyzing 5,237 legs from N = 2362 unique patients, we find significant correlations with multiple PPG features and the ankle-brachial index (ABI), a commonly used non-invasive diagnostic test for PAD. Using these explainable features, we develop a machine learning model to detect PAD solely from PPG features (AUC = 0.83) and develop an enhanced model incorporating clinical information (AUC = 0.85). Additionally, our model is highly generalizable, performing similarly across demographics and comorbidities. These findings represent an initial step toward identifying an accessible, physiologically grounded digital biomarker associated with PAD, and lay the foundation for prospective studies to evaluate performance across clinical workflows and reference standards.
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Acknowledgements #
This research was funded in part by the American College of Cardiology Foundation, Accreditation Foundation Committee. The funder played no role in study design, data collection, analysis and interpretation of data, or the writing of this manuscript. M.R. was supported in part through participation in the Robert A. Winn Excellence in Clinical Trials Award Program.
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Ramsis, M., Fascetti, A.J., Naguib, M.H. et al. Development and validation of a digital biomarker for peripheral artery disease.
npj Digit. Med. (2026). https://doi.org/10.1038/s41746-026-02655-w Received:
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DOI: https://doi.org/10.1038/s41746-026-02655-w