AI Atlas Reveals How Body Fat and Muscle Predict Disease Risk

Scientists have developed an AI-powered atlas of human body composition, offering a far more precise way to assess health risks than traditional methods. The research, based on over 66,000 whole-body MRI scans, tracks how fat and muscle change with age, height and sex. Experts say this could transform how doctors predict disease and tailor treatments. The study used advanced imaging and open-source AI to create detailed reference curves for body composition across the lifespan. Unlike BMI, which only considers weight and height, this approach maps fat and muscle distribution with high accuracy. The AI framework requires minimal human input, making it practical for routine medical imaging like chest or abdominal CTs and MRIs.

Findings reveal stark links between body composition and serious health risks. High levels of intramuscular fat raise the chance of major cardiovascular events by 1.54 times. Visceral fat, the type stored around organs, increases diabetes risk by 2.26 times. Meanwhile, low skeletal muscle mass independently predicts a 1.44-fold higher risk of death from any cause. The research also highlights applications in cancer care. Muscle loss and fat distribution can affect treatment responses and the likelihood of cancer returning. By integrating this AI tool into clinical practice, doctors could better monitor patients and adjust therapies based on precise body composition data.

The new atlas provides a clearer picture of how body fat and muscle influence long-term health. With its open-source AI framework, hospitals can now apply these insights to existing scans without extra equipment. This could lead to earlier interventions for conditions like diabetes, heart disease and cancer, improving patient outcomes.