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Author Spotlight: Advanced Techniques for Characterizing Tissue Mineralization in Bone Regeneration Research

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An accurate pediatric bone age prediction model using deep learning and contrast conversion.

Dong Hyeok Choi^1,2,3, So Hyun Ahn^4,5, Rena Lee⁶

¹Department of Medicine, Yonsei University College of Medicine, Seoul, Korea.

Ewha Medical Journal

|July 24, 2025

Summary

Deep learning models accurately predict pediatric bone age using hand X-rays. Image preprocessing techniques like histogram equalization (HE) did not significantly impact prediction accuracy, improving clinical growth assessments.

Keywords:

Bone age measurement Deep learning X-ray image

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Area of Science:

Radiology
Artificial Intelligence
Pediatric Endocrinology

Background:

Accurate bone age assessment is crucial for pediatric growth monitoring and clinical decision-making.
Traditional methods for bone age determination can be subjective and time-consuming.

Purpose of the Study:

To develop an accurate pediatric bone age prediction model using deep learning and contrast conversion techniques.
To enhance clinical decision-making in pediatric growth assessment.

Main Methods:

Utilized various deep learning models (CNN, ResNet50, VGG19, Inception V3, Xception) trained on pediatric hand X-ray images.
Applied contrast conversion techniques (fuzzy contrast enhancement, CLAHE, HE) for image preprocessing.
Evaluated model performance using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE).

Main Results:

The Xception model achieved the best performance with an MAE of 41.12.
Histogram equalization (HE) improved image quality, enhancing SNR and contrast.
Bone age prediction accuracy improved, with MAE decreasing from 2.11 to 0.24 and RMSE from 0.21 to 0.02 after preprocessing.

Conclusions:

Deep learning models, particularly Xception, show promise for accurate pediatric bone age prediction.
Image preprocessing techniques like HE can enhance image quality but do not significantly alter prediction performance.
The findings support the integration of AI-powered tools for improved pediatric growth assessment.