Researchers at the Technical University of Munich (TUM) and Helmholtz Munich have developed a method combining artificial intelligence (AI) and innovative high-resolution optoacoustic imaging technology that can be used to measure microvascular changes in the small blood vessels in skin and assess severity of diabetes. 

Optoacoustic imaging methods use light pulses to generate ultrasound inside tissue. The ultrasound waves generated are then recorded by sensors and converted to images. The signals are caused by tiny expansions and contractions of tissue that surrounds molecules that strongly absorb light. One such molecule is haemoglobin. Since haemoglobin is concentrated in blood vessels, optoacoustic imaging can produce detailed unique images of vessels in ways not possible by other non-invasive techniques. 

The team at TUM developed RSOM, short for ‘raster-scan optoacoustic mesoscopy’, and successfully employed RSOM to study the effects of diabetes on the human skin. 

Using RSOM images of the blood vessels in the legs of 75 people with diabetes and a control group, the team identified characteristics of diabetes using an AI algorithm. They created a list of 32 particularly significant characteristics of the skin microvasculature that are affected by the progression and severity of diabetes. Yet, only when they are combined and a score is calculated, can a link be drawn between the condition of the small blood vessels in the skin and the severity of diabetes.

“With RSOM, we can quantitatively describe the effects of diabetes,” said co-author Vasilis Ntziachristos. “With the emerging ability to make RSOM portable and cost-effective, these findings open up a new way for continuous monitoring of the status of those affected. In the future, it would take just a few minutes to determine whether therapies are having an effect, even at home environments.”

The findings were published in Nat Biomed Eng.