Peripheral artery disease, often called PAD, is a serious health problem that affects millions of people around the world.
In the United States alone, doctors estimate that between 8 and 12 million people live with the condition.
PAD happens when fatty plaque made of cholesterol and other substances builds up inside blood vessels. Over time, these blood vessels become narrower, reducing blood flow to the legs and feet.
Many people with PAD do not know they have it because symptoms can develop slowly or remain hidden for years.
Some patients may notice leg pain while walking, numbness, weakness, or cold feet, but others experience almost no symptoms until the disease becomes severe. In advanced cases, the lack of blood flow can damage tissue and lead to infections, wounds that do not heal, and even limb amputation.
Doctors consider PAD especially dangerous because it is also linked to a much higher risk of heart attacks and strokes. The condition is more common in older adults, smokers, people with diabetes, and patients with high blood pressure or high cholesterol.
Researchers also know that PAD disproportionately affects underserved and marginalized communities, where access to specialized healthcare may be limited.
One major problem with PAD is that diagnosing it can be difficult. The standard test for the disease is called the ankle-brachial index, or ABI.
During this test, doctors compare blood pressure in the ankle with blood pressure in the arm. Although the test works well, it usually requires a visit to a specialized clinic and can take between 15 and 30 minutes to complete.
Now, researchers at the University of California San Diego have developed a much faster and simpler method that could help doctors detect PAD earlier. Their new study shows that a technology called photoplethysmography, or PPG, combined with artificial intelligence can identify PAD with high accuracy.
The research was published in the journal npj Digital Medicine.
PPG is a simple light-based technology already used in many medical devices. It works by shining light into tissue and measuring how much light reflects back. Tiny changes in blood flow affect the reflected light signal, allowing the device to detect changes in blood volume.
According to co-first author Ava J. Fascetti, a PhD student at UC San Diego, the researchers used the technology on the toes of patients. A photosensor measured the reflected light and created what scientists call a PPG signal. These signals contain information about blood circulation in the body.
The idea for the study began when Dr. Mattheus Ramsis, assistant professor of medicine and medical director of cardiology informatics at UC San Diego, learned that PPG recordings were already routinely collected during ABI testing.
He realized that the PPG data itself might contain enough information to identify PAD without needing the full ABI procedure.
To test the idea, Ramsis worked with fourth-year medical student Mustafa H. Naguib to build a very large dataset. The team collected more than 10,000 toe PPG recordings from over 3,500 patients who underwent ABI testing at UC San Diego Health between 2020 and 2025.
The researchers carefully analyzed the recordings and identified 78 features strongly linked to ABI results. They then trained a machine learning model to predict whether a patient had PAD using only the PPG data.
The results were impressive. The AI system correctly identified PAD cases about 83% of the time. In comparison, traditional clinical risk assessment methods alone usually achieve only around 60% to 65% accuracy.
The researchers found that adding smoking status to the model improved performance slightly even further. Importantly, the system appeared to work similarly well across different racial and ethnic groups, including Black, Hispanic, and white patients.
It also worked well for patients with other serious conditions such as diabetes, coronary artery disease, and kidney failure.
The scientists believe this technology could eventually make PAD screening far more accessible. Unlike ABI testing, which requires special equipment and clinic visits, PPG signals can potentially be collected using smartphones, pulse oximeters, and wearable devices.
Because most people already own smartphones or smart devices, the researchers think future screening tools could help remove barriers related to transportation, cost, and access to specialist clinics.
Dr. Ramsis explained that a quick PPG screening could be used during routine medical visits or even performed by high-risk patients themselves at home using a smartphone app. Earlier diagnosis could allow doctors to begin treatment before severe complications such as limb amputation occur.
The researchers emphasized that they are not trying to replace ABI testing at this stage. Instead, they see PPG screening as a fast and inexpensive way to identify patients who may need further medical evaluation.
The study also highlights the growing role of artificial intelligence in medicine. AI systems are becoming increasingly useful for analyzing large amounts of physiologic data that would be difficult for humans to interpret quickly.
In reviewing the findings, this study appears highly promising because it combines inexpensive technology with artificial intelligence to solve a major healthcare problem.
The large number of patient recordings strengthens the reliability of the results, and the consistent performance across different patient groups is encouraging. However, the system still needs to be tested using different devices and in real-world settings outside a single healthcare system.
More validation studies will be important before widespread use becomes possible. Even so, the research suggests that future smartphone-based screening tools may help detect PAD earlier, reduce amputations, and improve healthcare access for millions of people.
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