Machine learning prediction of combat basic training injury from 3D body shape images

Abstract

Introduction: Athletes and military personnel are both at risk of disabling injuries due to extreme physical activity. A method to predict which individuals might be more susceptible to injury would be valuable, especially in the military where basic recruits may be discharged from service due to injury. We postulate that certain body characteristics may be used to predict risk of injury with physical activity.

Methods: US Army basic training recruits between the ages of 17 and 21 (N = 17,680, 28% female) were scanned for uniform fitting using the 3D body imaging scanner, Human Solutions of North America at Fort Jackson, SC. From the 3D body imaging scans, a database consisting of 161 anthropometric measurements per basic training recruit was used to predict the probability of discharge from the US Army due to injury. Predictions were made using logistic regression, random forest, and artificial neural network (ANN) models. Model comparison was done using the area under the curve (AUC) of a ROC curve.

Results: The ANN model outperformed two other models, (ANN, AUC = 0.70, [0.68,0.72], logistic regression AUC = 0.67, [0.62,0.72], random forest AUC = 0.65, [0.61,0.70]).

Conclusions: Body shape profiles generated from a three-dimensional body scanning imaging in military personnel predicted dischargeable physical injury. The ANN model can be programmed into the scanner to deliver instantaneous predictions of risk, which may provide an opportunity to intervene to prevent injury.

Fig 1. Work-flow diagram describing the data preparation to model evaluation process.

Fig 2

ROC curves for logistic regression (Panel A), random forest (Panel B) and neural network (Panel C) models over repeated, stratified, 3-fold cross-validation. Solid curve represents the mean ROC curve. AUC for each model were 0.67 ± 0.05, 0.65 ± 0.04, and 0.70 ± 0.02, respectively. Panel D is a plot of the AUC for each model ± 95% confidence interval.