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A Novel Dual-Output Deep Learning Model Based on InceptionV3 for Radiographic Bone Age and Gender Assessment.

Baraa Rayed¹, Hakan Amasya^2,3,4, Mana Sezdi^5,4

¹Biomedical Engineering Department, Institute of Graduate Studies, Istanbul University-Cerrahpasa, 34320, Istanbul, Turkey. baraarayed@ogr.iuc.edu.tr.

Journal of Imaging Informatics in Medicine

|August 4, 2025

Summary

This study introduces a computer-assisted system for predicting bone age and gender from hand-wrist radiographs. The AI model shows promising results, though high hardware requirements may limit clinical use.

Keywords:

Automatic bone age Convolutional neural network (CNN)Deep learning X-ray

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Area of Science:

Radiology
Artificial Intelligence
Medical Imaging

Background:

Hand-wrist radiographs are standard for bone age assessment.
Traditional methods have limitations.
Computer-assisted systems can improve accuracy and efficiency.

Purpose of the Study:

To develop a multi-output prediction model for bone age and gender using digital radiographs.
To leverage deep learning for enhanced diagnostic capabilities.

Main Methods:

Utilized the InceptionV3 architecture with specialized convolutional neural network layers (e.g., Squeeze-and-Excitation block).
Trained and tested on the 2017 RSNA Pediatric Bone Age Challenge dataset (14,048 samples).
Employed a 7:2:1 ratio for training, validation, and testing data splits.

Main Results:

Achieved a mean absolute error of 3.1 for bone age prediction.
Reached 95% accuracy and 97% AUC for gender classification.
High intra-class correlation (0.997) for bone age and Cohen's kappa (0.898) for gender.

Conclusions:

The proposed AI model demonstrates high accuracy in predicting bone age and gender from radiographs.
The model's ability to identify common and discrete features enhances efficiency.
Future work should focus on dataset expansion and algorithm simplification for broader clinical adoption.