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

TL;DR

This study introduces a semi-supervised $p$-Laplacian regression model using 3D optical imaging data to predict body composition variables, offering a cost-effective alternative to DXA scans with promising accuracy.

Contribution

It is the first application of a $p$-Laplacian model for regression in predicting body composition, demonstrating effectiveness in data-limited healthcare scenarios.

Findings

$p$-Laplacian model achieved ~13% error for ALM, ~10% for BMD, ~20% for BFP with 10% training data.

Support Vector Regression (SVR) performed best among supervised methods, with ~8% error for ALM and BMD.

Least Squares SVR achieved ~11% error for BFP with 80% training data.

Abstract

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 $\sim13\%$ for ALM, $\sim10\%$ for BMD, and $\sim20\%$ 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 $\sim 8\%$ for both, while Least Squares SVR performed the best for BFP with $\sim 11\%$ 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.