Body Fat Estimation from Surface Meshes using Graph Neural Networks

TL;DR

This paper presents a novel approach using graph neural networks to accurately estimate visceral and subcutaneous fat volumes from body surface meshes, offering a cost-effective alternative to traditional imaging methods.

Contribution

It introduces a new method leveraging surface meshes and graph neural networks for fat estimation, improving accuracy and efficiency over existing CNN-based techniques.

Findings

High prediction accuracy for VAT and ASAT volumes.

Reduced training time and computational resources.

Potential for use with accessible surface scans instead of expensive imaging.

Abstract

Body fat volume and distribution can be a strong indication for a person's overall health and the risk for developing diseases like type 2 diabetes and cardiovascular diseases. Frequently used measures for fat estimation are the body mass index (BMI), waist circumference, or the waist-hip-ratio. However, those are rather imprecise measures that do not allow for a discrimination between different types of fat or between fat and muscle tissue. The estimation of visceral (VAT) and abdominal subcutaneous (ASAT) adipose tissue volume has shown to be a more accurate measure for named risk factors. In this work, we show that triangulated body surface meshes can be used to accurately predict VAT and ASAT volumes using graph neural networks. Our methods achieve high performance while reducing training time and required resources compared to state-of-the-art convolutional neural networks in this area. We furthermore envision this method to be applicable to cheaper and easily accessible medical surface scans instead of expensive medical images.