# Automated Analysis of Vertebral Body Surface Roughness for Adult Age Estimation: Ellipse Fitting and Machine-Learning Approach

## TL;DR

This paper introduces a new automated method using CT scans and machine learning to estimate adult age based on vertebral surface roughness.

## Contribution

The novel contribution is an objective, automated age estimation method using ellipse fitting and machine learning on vertebral CT data.

## Key findings

- Surface roughness metrics (DS) showed strong correlation with age (peak r = 0.60 at L3–L5).
- Random forest achieved the best accuracy with an SEE of 8.49 years in males.
- The method offers a transparent and observer-independent alternative to deep learning pipelines.

## Abstract

Background/Objectives: Vertebral degenerative features are promising but often subjectively scored indicators for adult age estimation. We evaluated an objective surface roughness metric, the “average distance to the fitted ellipse” score (DS), calculated automatically for every vertebra from C7 to S1 on routine CT images. Methods: CT scans of 176 adults (94 males, 82 females; 21–94 years) were retrospectively analyzed. For each vertebra, the mean orthogonal deviation of the anterior superior endplate from an ideal ellipse was extracted. Sex-specific multiple linear regression served as a baseline; support vector regression (SVR), random forest (RF), k-nearest neighbors (k-NN), and Gaussian naïve-Bayes pseudo-regressor (GNB-R) were tuned with 10-fold cross-validation and evaluated on a 20% hold-out set. Performance was quantified with the standard error of the estimate (SEE). Results: DS values correlated moderately to strongly with age (peak r = 0.60 at L3–L5). Linear regression explained 40% (males) and 47% (females) of age variance (SEE ≈ 11–12 years). Non-parametric learners improved precision: RF achieved an SEE of 8.49 years in males (R2 = 0.47), whereas k-NN attained 10.8 years (R2 = 0.45) in women. Conclusions: Automated analysis of vertebral cortical roughness provides a transparent, observer-independent means of estimating adult age with accuracy approaching that of more complex deep learning pipelines. Streamlining image preparation and validating the approach across diverse populations are the next steps toward forensic adoption.