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.

使用图神经网络可以准确预测腹部内脏脂肪组织和腹部皮下脂肪组织的体积，相较于最先进的卷积神经网络，减少了训练时间和所需资源，也可以应用于廉价且易于获取的医学表面扫描替代昂贵的医学图像。