New AI-Powered 3D Scanner Can Detect Your Risk of Metabolic Syndrome – Here’s How?

Mayo Clinic researchers are leveraging artificial intelligence (AI) in conjunction with an advanced 3D body-volume scanner—originally designed for the clothing industry—to assist doctors in predicting the risk and severity of metabolic syndrome. According to findings published in the European Heart Journal—Digital Health, this innovative approach provides a more precise alternative to traditional methods of assessing disease risk, such as body mass index (BMI) and waist-to-hip ratio.

Metabolic syndrome, a condition affecting over a third of the U.S. population and a quarter of people globally, can lead to severe health problems, including heart attacks and strokes. Despite its prevalence, there is no universally accepted screening method for the syndrome. However, researchers discovered that combining a 3D body volume scanner with imaging technology and algorithms developed by Mayo Clinic could offer clinicians a more accurate way to identify individuals with the syndrome and those at risk of developing it.

The impact of metabolic syndrome is significant, as it increases the likelihood of patients developing heart attacks, strokes, diabetes, cognitive diseases, and liver disease. Clinically, the syndrome is diagnosed when a person exhibits at least three of the following five conditions: abdominal obesity, high blood pressure, high triglycerides, low HDL cholesterol, and high fasting blood sugar.

“There is a critical need for a reliable and consistent measure of metabolic syndrome risk and severity,” says Dr. Betsy Medina Inojosa, a research fellow at Mayo Clinic and the study’s lead author. “Traditional methods like BMI and bioimpedance scales, which measure body fat and muscle, are often inaccurate for many individuals, and other types of scans are not widely accessible. Our research indicates that this AI model could serve as a valuable tool for guiding clinicians and patients in making informed decisions about their metabolic health.”

To create this tool, the researchers trained and validated an AI model on data from 1,280 volunteers who underwent comprehensive evaluations, including 3D body-volume scans, clinical questionnaires, blood tests, and traditional body measurements. An additional 133 volunteers were assessed using front- and side-view images taken with a mobile app called myBVI from Select Research to further test the tool’s ability to evaluate the presence and severity of metabolic syndrome.

Individuals with metabolic syndrome often have an apple-shaped body, characterized by excess weight around the abdomen. Diagnosing the syndrome traditionally involves lab tests, blood pressure readings, and body shape measurements, but there are no widely accepted routine screening strategies due to the variability and reproducibility challenges of these measurements.

“This preliminary study shows that using 3D imaging to measure a patient’s body volume index provides a highly accurate assessment of body shapes and volumes in key areas where unhealthy visceral fat tends to accumulate, such as the abdomen and chest,” says Dr. Francisco Lopez-Jimenez, director of Preventive Cardiology at Mayo Clinic in Rochester and the study’s senior author.

“The scans also capture the volume of the hips, buttocks, and legs—a measure associated with muscle mass and ‘healthy’ fat. The 3D data from these key body regions, whether obtained from a large, stationary 3D scanner or a mobile app, effectively identified the presence and severity of metabolic syndrome through imaging rather than invasive tests. The next step in our research is to expand the study to include a more diverse group of subjects.”

Mayo Clinic and the researchers involved have a financial interest in the technology discussed in this article. Any revenue generated by Mayo Clinic from this technology will be used to support its not-for-profit mission in patient care, education, and research.

For more information, refer to the original study by Betsy J Medina Inojosa et al., titled “Prediction of presence and severity of metabolic syndrome using regional body volumes measured by a multisensor white-light 3D scanner and validation using a mobile technology,” published in the European Heart Journal—Digital Health (2024). DOI: 10.1093/ehjdh/ztae059

Reference: “3D Body Scanner with AI Predicts Metabolic Syndrome Risk” (2024, August 15). Retrieved August 15, 2024.

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