Automated Deep Learning Estimation of Anthropometric Measurements for Preparticipation Cardiovascular Screening

TL;DR

This paper introduces a fully automated deep learning method to accurately estimate key anthropometric measurements from 2D images, aiding cardiovascular screening in athletes at scale.

Contribution

It presents a novel deep learning approach using synthetic images to estimate anthropometric measurements, improving scalability and accuracy over manual methods.

Findings

ResNet50 achieved a mean MAE of 0.668 cm.

All models attained sub-centimeter accuracy.

Deep learning enables scalable anthropometric measurement estimation.

Abstract

Preparticipation cardiovascular examination (PPCE) aims to prevent sudden cardiac death (SCD) by identifying athletes with structural or electrical cardiac abnormalities. Anthropometric measurements, such as waist circumference, limb lengths, and torso proportions to detect Marfan syndrome, can indicate elevated cardiovascular risk. Traditional manual methods are labor-intensive, operator-dependent, and challenging to scale. We present a fully automated deep-learning approach to estimate five key anthropometric measurements from 2D synthetic human body images. Using a dataset of 100,000 images derived from 3D body meshes, we trained and evaluated VGG19, ResNet50, and DenseNet121 with fully connected layers for regression. All models achieved sub-centimeter accuracy, with ResNet50 performing best, achieving a mean MAE of 0.668 cm across all measurements. Our results demonstrate that deep learning can deliver accurate anthropometric data at scale, offering a practical tool to complement athlete screening protocols. Future work will validate the models on real-world images to extend applicability.