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Updated: Oct 4, 2025
Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos
Published on: March 21, 2021
Body Composition Prediction-BOMP: A New Tool for Assessing Fat and Lean Body Mass
Simon Lebech Cichosz1, Peter Vestergaard2, Ole Hejlesen1
1Department of Health Science and Technology, Aalborg University, Aalborg, Denmark.
Background:
Pragmatic and easy-to-use alternatives to estimating body composition, such as lean body mass and fat mass, could be valuable tools for assessing the risk of diabetes or other metabolic diseases. Previous work has shown how demographic and anthropometric data could be used in a neural network to estimate body composition with high precision. However, there is still a need for a publicly available and user-friendly format before these results can have clinical impact.
Methods:
We used data from 18 430 NHANES participants and stepwise linear regression with inclusion of linear, interactions, and quadratic terms to model lean body and fat mass. HTML and Javascript was used to develop a webapp as a frontend of the model.
Results:
The models had a correlation cofficent R = 0.99-0.98 (P < .001) withstandard error of estimate [SEE] = 2.07-2.05.
Conclusions:
The results indicate that it is possible to develop a "white-box" model with high precision.The proof of concept webapp is available as open source under the MIT license.
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