AI-Powered Algorithm Offers Quick, Contactless Blood Pressure and Diabetes Screening

A newly developed system that combines high-speed video with an artificial intelligence (AI)-powered algorithm may provide a quick, non-contact method for screening high blood pressure and Type 1 or Type 2 diabetes, eliminating the need for blood tests, blood pressure cuffs, or costly wearable devices. Although the system is still in the early stages of development, it could eventually offer rapid, contactless screenings for high blood pressure and diabetes, potentially aiding in the monitoring of treatment responses.

In the study, researchers at the University of Tokyo (Tokyo, Japan) evaluated the effectiveness of a high-speed video camera capable of capturing face and palm recordings at 150 images per second. By using wavelength data to detect pulse waves, the research team applied an AI algorithm to identify high blood pressure and diabetes through blood flow characteristics in the skin as captured by the video. When compared to continuous blood pressure monitor readings taken simultaneously with the video recordings, the video/algorithm combination demonstrated 94% accuracy in detecting stage 1 hypertension, as defined by the American Heart Association’s guidelines for blood pressure of 130/80 mm Hg or higher.

Image: The AI-powered algorithm offers quick, no-contact screenings for high blood pressure and diabetes (Photo courtesy of 123RF)

In comparison with continuous blood pressure monitor readings, the 30-second video/algorithm method showed 86% accuracy in detecting elevated blood pressure, while the 5-second video/algorithm method had 81% accuracy. Normal blood pressure readings were defined according to the Japanese Society of Hypertension guidelines (2018), which specify systolic pressure below 115 mm Hg and diastolic pressure below 75 mm Hg. When compared to hemoglobin A1c blood test results for diabetes screening, the video/algorithm system was 75% accurate in identifying individuals with diabetes. The A1c test measures average blood sugar levels over the previous 1-2 months. Although the technology shows promise, additional steps are needed before the video/algorithm combination can be applied outside of a research setting.

“To detect high blood pressure, we need to incorporate an algorithm that considers arrhythmias or irregular heartbeats,” said study author Ryoko Uchida, B.Sc. (Pharm.), a project researcher in the department of advanced cardiology at the University of Tokyo. “In the future, the prototype camera we used to develop the algorithm could be substituted with an affordable sensor that uses only the essential wavelengths and requires just a few seconds to gather data. Once it reaches that stage, it may be added to smartphones (or even hung on a mirror where someone sits for a few moments), may be mass-produced and inexpensive.”