# Automated bone age assessment in rare pediatric growth disorders: a comparative study using Deeplasia

## TL;DR

This study shows that the Deeplasia AI system accurately assesses bone age in children with rare growth disorders, outperforming human experts.

## Contribution

Validates Deeplasia's performance on rare pediatric disorders and demonstrates its superiority over human raters in bone age assessment.

## Key findings

- Deeplasia achieved 89.9% 1-year accuracy for endocrine and syndromic conditions.
- For lysosomal storage disorders, Deeplasia had 81.2% 1-year accuracy.
- Deeplasia outperformed all individual human raters in direct comparisons.

## Abstract

Bone age (BA) assessment is essential for monitoring growth and maturation and guiding therapeutic interventions. While deep learning (DL) models offer high-speed automated BA prediction, their generalizability to rare pathological and diagnostically complex populations remains a significant concern. This study aims to validate the open-source DL system Deeplasia on external data from pediatric patients with various syndromic, endocrine, and lysosomal storage disorders (LSDs) and to compare its accuracy and consistency against multiple expert human raters.

We retrospectively assembled 1,138 hand radiographs from multiple centers, including patients with SHOX deficiency; Noonan syndrome; Silver–Russell syndrome; Ullrich–Turner syndrome; pseudohypoparathyroidism; congenital adrenal hyperplasia (CAH); precocious puberty and precocious pseudopuberty (cohort 1); mucopolysaccharidosis types I, II, III, IV, and VI; alpha-mannosidosis; and unclassified LSDs (cohort 2). For each radiograph, BA was evaluated using the Greulich and Pyle method by two to five human experts to obtain a mean BA reference. Model performance was assessed using the mean absolute error (MAE), root mean squared error (RMSE), and 1-year accuracy for each cohort and underlying conditions, sex, and age groups. Furthermore, Deeplasia’s performance was compared with that of individual raters by testing each rater and the model against the remaining experts.

Deeplasia achieved a mean MAE of 5.95 months, an RMSE of 8.01 months, and a 1-year accuracy of 89.9% for cohort 1 (endocrine and syndromic conditions). For cohort 2 (lysosomal storage disorders), Deeplasia achieved a mean MAE of 7.13 months, an RMSE of 9.56 months, and a 1-year accuracy of 81.2%. In direct comparisons between Deeplasia and individual raters tested against the remaining experts, Deeplasia outperformed all human raters.

Deeplasia was validated as a highly consistent, robust, and reliable tool for BA assessment in complex cases. It demonstrated superior accuracy compared with individual human raters and may assist clinicians in BA evaluation.