# Predicting Anthropometric Body Composition Variables Using 3D Optical Imaging and Machine Learning

arXiv: https://arxiv.org/abs/2506.14815 · HTML: https://mlsanity.com/paper/2506.14815/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 37 references that are in the map.
- First posted on arXiv 8 Jun 2025.

## Builds on (most cited first, 5 of 5)

- [Generalizing from a Few Examples: A Survey on Few-Shot Learning](https://mlsanity.com/paper/1904.05046/) (2019)
- [A Comparative Analysis of XGBoost](https://mlsanity.com/paper/1911.01914/) (2019)
- [Rates of Convergence for Laplacian Semi-Supervised Learning with Low Labeling Rates](https://mlsanity.com/paper/2006.02765/) (2020)
- [Semi-Supervised Learning for Bone Mineral Density Estimation in Hip X-ray Images](https://mlsanity.com/paper/2103.13482/) (2021)
- [Consistency of semi-supervised learning, stochastic tug-of-war games, and the p-Laplacian](https://mlsanity.com/paper/2401.07463/) (2024)

## Built on by (most cited first, 1 of 1)

- [Target-Aware State-Adaptive p-Dirichlet Graph Neural Regression for Non-Invasive Body-Composition Estimation](https://mlsanity.com/paper/2608.29496/) (2026)

## Abstract (arXiv)

Accurate prediction of anthropometric body composition variables, such as Appendicular Lean Mass (ALM), Body Fat Percentage (BFP), and Bone Mineral Density (BMD), is essential for early diagnosis of several chronic diseases. Currently, researchers rely on Dual-Energy X-ray Absorptiometry (DXA) scans to measure these metrics; however, DXA scans are costly and time-consuming. This work proposes an alternative to DXA scans by applying statistical and machine learning models on biomarkers (height, volume, left calf circumference, etc) obtained from 3D optical images. The dataset consists of 847 patients and was sourced from Pennington Biomedical Research Center. Extracting patients' data in healthcare faces many technical challenges and legal restrictions. However, most supervised machine learning algorithms are inherently data-intensive, requiring a large amount of training data. To overcome these limitations, we implemented a semi-supervised model, the p-Laplacian regression model. This paper is the first to demonstrate the application of a p-Laplacian model for regression. Our p-Laplacian model yielded errors of ~13% for ALM, ~10% for BMD, and ~20% for BFP when the training data accounted for 10 percent of all data. Among the supervised algorithms we implemented, Support Vector Regression (SVR) performed the best for ALM and BMD, yielding errors of ~ 8% for both, while Least Squares SVR performed the best for BFP with ~ 11% error when trained on 80 percent of the data. Our findings position the p-Laplacian model as a promising tool for healthcare applications, particularly in a data-constrained environment.