Pediatric Bone Age Prediction Using Deep Learning

2026-07-18

AI Summary

This study addresses the limitations of traditional bone age assessment, which relies on manual interpretation by radiologists and suffers from low efficiency and high expertise requirements. To overcome these challenges, the authors propose an automated bone age prediction method based on deep learning, leveraging the EfficientNetB4 architecture enhanced with an Additive Attention mechanism to improve focus on critical epiphyseal regions. The model is trained end-to-end on over 12,000 hand radiographs, incorporating tailored image preprocessing and a three-channel conversion strategy. This approach achieves stable training without overfitting and significantly improves prediction accuracy, yielding estimates closely aligned with chronological bone age. The results demonstrate the method’s effectiveness and clinical potential as a supportive tool in pediatric endocrinology diagnostics.

Abstract

Pediatric bone age prediction is a crucial task in clinical practice that can help diagnose endocrine disorders and provide insight into a child's growth and development. However, conventional bone age prediction methods are often labor-intensive and require specialized radiological expertise. This paper presents a Deep Learning (DL)-based approach to pediatric bone age prediction using EfficientNet with Additive Attention, a state-of-the-art neural network architecture for image classification and regression tasks. The method utilizes over 12,000 X-ray images from the RSNA bone age dataset. It involves image preprocessing, transforming them into three-channel images, and training a Convolutional Neural Network (CNN) to automatically learn the features of hand bone images. This approach provides a more effective and accurate solution for predicting bone age, which is critical in diagnosing pediatric endocrine diseases. This work uses two variations of the EfficientNet model (B0 and B4), where EfficientNetB4 is also finetuned with the Additive Attention mechanism. These three models predict the age for the original age, and their comparison is shown in curves. The predicted ages depict that in most cases, EfficientNetB4 and EfficientNetB4 with Additive Attention (EN-AA) successfully predicted the bone ages more accurately regarding the original age, and their performance was better than the EfficientNetB0. Specific performance metrics are provided to underscore this improvement. Learning curves for training and validation loss confirm effective learning without overfitting or underfitting, further validating our approach's efficacy in pediatric endocrine disease diagnosis.

Problem

• pediatric bone age prediction
• endocrine disorders
• growth and development
• radiological expertise
• clinical diagnosis

Innovation

• EfficientNet
• Additive Attention
• Bone Age Prediction
• Deep Learning
• Pediatric Radiology