Development and validation of a digital biomarker for peripheral artery disease

Researchers developed and validated a digital biomarker for peripheral artery disease (PAD) using photoplethysmography (PPG) signals, analyzing 5,237 legs from 2,362 patients. The machine learning model detected PAD from PPG features alone with an AUC of 0.83, and an enhanced model incorporating clinical information achieved an AUC of 0.85. The findings represent a step toward an accessible, physiologically grounded digital screening tool for the underdiagnosed condition.

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. Similar content being viewed by others 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. Author information Authors and Affiliations Corresponding author Ethics declarations Competing interests The authors declare no competing interests. Additional information Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Supplementary information Rights and permissions Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author s and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ http://creativecommons.org/licenses/by/4.0/ . About this article Cite this article 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: Accepted: Published: DOI: https://doi.org/10.1038/s41746-026-02655-w