A research team at the University of California, San Diego has developed a machine learning-based screening approach for peripheral artery disease (PAD) that uses a light-based technology called photoplethysmography (PPG) that can measure changes in blood volume in tissue. The researchers reported that short-duration PPG recordings in a patient’s toe, analyzed by machine learning models, identified PAD with a high degree of accuracy and may provide the basis for a scalable digital screening tool that could eventually be deployed through smartphones, pulse oximeters, and wearable devices. The team’s findings are published in npj Digital Medicine.
“PPG works by shining a light into tissue, in our case, the toe,” said co-first author Ava J. Fascetti, a PhD student in the digital health technology lab of senior author Edward J. Wang, PhD. “A photosensor measures how much light is reflected back, allowing us to detect tiny changes in blood volume: what we call the PPG signal.”
PAD is caused by plaque buildup in arteries that restricts blood flow, particularly to the legs and lower extremities. The disease affects an estimated 12 million Americans and 200 million adults worldwide. PAD substantially increases the risk of limb loss and major cardiovascular events, yet many patients are not diagnosed until later stages of disease progression. The researchers noted that the condition disproportionately affects underserved populations and is underdiagnosed in part because the current standard diagnostic, ankle-brachial index (ABI), requires specialized equipment, staff, and clinic visits.
“There exists a glaring unmet clinical need to develop technology to meet the demands of modern practice,” the researchers wrote. Further, ABI testing, introduced about 60 years ago, has has remained largely unchanged and has long-standing barriers to widespread use in primary care and under-resourced settings.
The current study originated from discussions between co-first author Mattheus Ramsis, MD, and assistant professor of medicine and medical director of cardiology informatics, and co-author Elsie G. Ross, MD, an associate professor of surgery in vascular and endovascular surgery, who noted that vascular labs conducting ABI testing often also collected toe PPG waveforms.
“The light-bulb went off for me at that moment,” Ramsis said.
PPG works by shining light into tissue and measuring backscattered light associated with blood volume changes. PPG has previously been used to identify cardiovascular and metabolic conditions including diabetes and atrial fibrillation. Earlier research efforts to use PPG for PAD detection had relied on small datasets, long recordings and less interpretable deep-learning approaches.
For their approach, the UCSD team assembled a dataset containing more than 10,000 toe PPG recordings from more than 3,500 patients who underwent ABI testing at UC San Diego Health between 2020 and 2025. Using these data, the researchers extracted 78 waveform features from the PPG signals that correlated significantly with ABI measurements. Those features were then used to train an explainable support vector machine model designed to identify PAD from PPG data alone.
Ramsis said the model correctly distinguished PAD cases approximately 83% of the time using only PPG data, compared with roughly 60% to 65% performance typically achieved using clinical risk-factor assessments alone. Incorporating smoking status of the patients further improved the performance of the new method.
Importantly, the model performed consistently across Black, Hispanic, and White patient populations, and among patients with diabetes, coronary artery disease, and end-stage renal disease. The researchers also reported similar performance across two UC San Diego Health campuses that used different equipment and staff.
The investigators noted that the physiologic basis for their findings align with established vascular biology. In PAD, reduced blood flow and arterial stiffness alter the morphology of PPG waveforms. Healthier patients demonstrated steeper systolic upstrokes and narrower waveform widths, while patients with PAD showed more dampened signals.
“Our findings support the existence of a reproducible PPG-derived digital biomarker that captures peripheral vascular pathophysiology relevant to ABI-defined PAD,” the researchers wrote.
The researchers said they don’t think their new model should replace ABI testing. Instead, they envision PPG screening as a complementary tool that could serve to identify patients earlier that might need further vascular evaluation.
The team said prospective deployment studies are already underway to evaluate performance in clinical settings and across additional reference standards, including toe pressure measurements, ultrasound imaging, and angiography. Additional research will also gauge performance in consumer-grade environments, including smartphones and wearable devices, and assess how the screening tool functions in broader patient populations outside specialized vascular clinics.
“If we can catch PAD early enough to prevent a limb amputation, that would be the ultimate impact: preserving limb function, reducing mortality, and addressing barriers in underserved populations,” Ramsis said.
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