Related Experiment Video
Updated: May 23, 2026
06:45
Automated Joint Space Detection Improves Bone Segmentation Accuracy
Published on: November 28, 2025
A two-stream network with global-local feature fusion for bone age assessment
Qiong Lou1, Han Yang1, Fang Lu2
1School of Science, Zhejiang University of Science and Technology, Hangzhou, China.
Scientific Reports
|May 21, 2026
Summary
This study introduces BoNet+, a deep learning model for bone age assessment (BAA). It achieves high accuracy by balancing global and local skeletal features, improving clinical efficiency.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Radiology
Background:
- Bone age assessment (BAA) is crucial for evaluating growth and maturity.
- Current deep learning methods struggle to balance global and local skeletal features.
Purpose of the Study:
- To develop an automated bone age assessment system with enhanced accuracy.
- To introduce the BoNet+ model for improved BAA.
Main Methods:
- A two-stream deep learning architecture (BoNet+) was developed.
- Incorporated Transformer module for global features and RFAConv module for local features.
- Concatenated global and local features, optimized with Inception-V3.
Main Results:
- Achieved Mean Absolute Errors (MAEs) of 3.81 months on RSNA dataset.
- Achieved MAEs of 5.65 months on RHPE dataset.
- Results are comparable to state-of-the-art methods.
Conclusions:
- BoNet+ offers automatic, high-precision, and objective bone age assessment.
- The model effectively reduces clinical workload.
- Demonstrates a promising advancement in deep learning for BAA.
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