3D body scanner breakthrough uses AI to predict metabolic risk

Researchers at Mayo Clinic are harnessing the power of artificial intelligence (AI) and an advanced 3D body-volume scanner to revolutionize how doctors assess the risk and severity of metabolic syndrome. Originally designed for the clothing industry, this 3D scanning technology has now found a significant application in healthcare.

According to findings published in the European Heart Journal - Digital Health, the combination of AI with 3D scanning offers a more accurate alternative to traditional measures of disease risk, such as body mass index (BMI) and waist-to-hip ratio.

Metabolic syndrome is a serious condition that increases the likelihood of heart attacks, strokes, and other severe health issues. Affecting over a third of the U.S. population and about a quarter of people worldwide, metabolic syndrome has been difficult to screen for consistently.

Now, researchers believe that combining 3D body-volume scanning with imaging technology and specialized algorithms developed by Mayo Clinic could offer a more precise method to identify those who have the syndrome and those at risk of developing it.

The consequences of metabolic syndrome extend beyond cardiovascular issues. It significantly raises the chances of developing diabetes, cognitive impairments, and liver disease.

Clinically, metabolic syndrome is diagnosed when at least three of five conditions are present: abdominal obesity, high blood pressure, high triglycerides, low HDL cholesterol, and high fasting blood sugar levels. These health issues can create long-term challenges for patients, making early detection and intervention crucial.

"There is a need for a reliable, repeatable measure of metabolic syndrome risk and severity," explains Dr. Betsy Medina Inojosa, a research fellow at Mayo Clinic and lead author of the study. She points out that current methods, like BMI and bioimpedance scales that measure body fat and muscle, often fail to provide accurate assessments for many people.

In addition, other types of scans aren’t widely available or consistent. "Our research shows that this AI model may also be a tool to guide clinicians and patients to take action and seek outcomes that are a better fit for their metabolic health," she adds.

To create the AI tool, Mayo Clinic researchers trained the model using data from 1,280 volunteers. These participants underwent 3D body-volume scans, answered standardized clinical questionnaires, took blood tests, and had their body shape measured.

In addition, another 133 volunteers were evaluated using a mobile app called myBVI, developed by Select Research. This app took front- and side-view images to further test how well the tool could evaluate the presence and severity of metabolic syndrome.

The hallmark of metabolic syndrome is often an "apple-shaped" body, where excess weight is concentrated in the abdominal area. While diagnosis typically involves lab tests, blood pressure readings, and body shape assessments, a widely accepted and reproducible screening strategy has been elusive due to the variability in available measurements. The new approach using 3D body-volume scans seeks to address that gap.

Dr. Francisco Lopez-Jimenez, director of Preventive Cardiology at Mayo Clinic and senior author of the study, highlights the accuracy of this digital measurement. "This small study finds that digitally measuring a patient's body volume index with 3D imaging provides a highly accurate measurement of shapes and volumes in critical regions where unhealthy visceral fat is deposited, such as the abdomen and chest," he says.

He further notes that the scans also record measurements of the hips, buttocks, and legs, which are linked to muscle mass and so-called 'healthy' fat. "The 3D information about body volume in these key regions, whether from the large, stationary 3D scanner or from the mobile app, accurately flagged the presence and severity of metabolic syndrome using imaging instead of invasive tests," he explains.

Moving forward, the researchers aim to expand their study to include a broader and more diverse group of participants. This will help them refine the technology and ensure its effectiveness across various populations. By improving the accuracy and availability of screening methods for metabolic syndrome, the AI-based tool holds promise for early detection and better health outcomes.

These apps are predominantly used in fashion retail, healthcare, and fitness industries to offer personalized experiences, whether it’s for clothing, body composition tracking, or custom healthcare solutions.

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