An Artificial Intelligence Framework for Measuring Human Spine Aging Using MRI

TL;DR

This study introduces a deep learning framework that estimates human spine age from MRI images, correlating the predicted age gap with degenerative conditions and lifestyle factors to assess spine health.

Contribution

It presents a novel computer vision-based deep learning method for spine age estimation using MRI, incorporating data clustering and ablation studies for model optimization.

Findings

Spine age gap correlates with degenerative conditions.

Model accurately predicts spine age from MRI images.

Lifestyle factors influence spine aging indicators.

Abstract

The human spine is a complex structure composed of 33 vertebrae. It holds the body and is important for leading a healthy life. The spine is vulnerable to age-related degenerations that can be identified through magnetic resonance imaging (MRI). In this paper we propose a novel computer-vison-based deep learning method to estimate spine age using images from over 18,000 MRI series. Data are restricted to subjects with only age-related spine degeneration. Eligibility criteria are created by identifying common age-based clusters of degenerative spine conditions using uniform manifold approximation and projection (UMAP) and hierarchical density-based spatial clustering of applications with noise (HDBSCAN). Model selection is determined using a detailed ablation study on data size, loss, and the effect of different spine regions. We evaluate the clinical utility of our model by calculating the difference between actual spine age and model-predicted age, the spine age gap (SAG), and examining the association between these differences and spine degenerative conditions and lifestyle factors. We find that SAG is associated with conditions including disc bulges, disc osteophytes, spinal stenosis, and fractures, as well as lifestyle factors like smoking and physically demanding work, and thus may be a useful biomarker for measuring overall spine health.