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Incorporated region detection and classification using deep convolutional networks for bone age assessment.
Toan Duc Bui1, Jae-Joon Lee2, Jitae Shin1
1Department Electrical and Computer Engineering, Sungkyunkwan University, Suwon 16419, Republic of Korea.
Artificial Intelligence in Medicine
|June 17, 2019
Summary
This study introduces a deep learning method for bone age assessment, combining expert knowledge with advanced AI. The novel approach achieves highly accurate results, outperforming existing methods in clinical evaluations.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Pediatric Endocrinology
Background:
- Bone age assessment is crucial for diagnosing endocrine and genetic disorders in children.
- Current methods may lack precision and efficiency in clinical practice.
Purpose of the Study:
- To develop an accurate and efficient deep learning-based bone age assessment tool.
- To integrate established clinical expertise (Tanner-Whitehouse methods) with modern AI techniques.
Main Methods:
- A hybrid approach combining Tanner-Whitehouse (TW3) methods with deep convolution networks.
- Utilized Faster-RCNN for region of interest (ROI) detection and Inception-v4 for classification.
- Integrated expert knowledge with deep learning feature engineering.
Main Results:
- Achieved a mean absolute error of approximately 0.59 years compared to expert radiologists.
- Demonstrated superior performance over existing state-of-the-art bone age assessment methods.
- The model effectively leverages both expert knowledge and data-driven features.
Conclusions:
- The proposed deep learning approach significantly enhances the accuracy of bone age assessment.
- This method offers a promising tool for endocrinology and genetic investigations.
- The integration of TW3 principles with deep learning represents a significant advancement in the field.
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