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Determination of Skeletal Age From Hand Radiographs Using Deep Learning
Joshua T Bram1, Ayoosh Pareek2, Samuel A Beber1
1Lerner Children's Pavilion, Hospital for Special Surgery, New York, New York, USA.
The American Journal of Sports Medicine
|August 15, 2025
Summary
This study developed a deep learning (DL) model to accurately estimate skeletal age from hand radiographs, significantly reducing estimation time. The model outperforms previous methods, aiding surgeons in treatment planning for young patients.
Area of Science:
- Radiology
- Artificial Intelligence
- Orthopedics
Background:
- Skeletal age estimation is crucial for surgical planning in pediatric patients.
- Traditional methods using hand radiographs are time-consuming.
Purpose of the Study:
- To develop accurate and reliable deep learning (DL) models for skeletal age determination from hand radiographs.
Main Methods:
- Utilized three public datasets (RSNA, RHPE, DHA) with hand radiographs, sex, and skeletal age.
- Trained a ConvNeXt model, incorporating chronological age in a second iteration.
- Validated models on diverse datasets, including an institutional set.
Main Results:
- The DL model achieved a mean absolute error (MAE) of 3.68 months on the RSNA test set.
- Model performance surpassed previous state-of-the-art methods, showing significant improvements (e.g., 19.8% on DHA).
Conclusions:
- Developed a robust DL model for bone age prediction using over 20,000 hand radiographs.
- The model offers a highly accurate and reliable platform for clinical use, improving surgical selection and saving time.
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