Hand X-rays findings and a disease screening for Turner syndrome through deep learning model
Yirou Wang1, Yumo Wang2, Feihan Hu1
1Department of Endocrinology, Genetics and Metabolism, Shanghai Children's Medical Center, School of Medicine, Shanghai Jiao Tong University, Shanghai, China.
BMC Pediatrics
|March 8, 2025
Summary
Hand X-ray features can help diagnose Turner syndrome (TS) in girls. Deep learning models improve diagnostic accuracy for this common cause of short stature.
Area of Science:
- Radiology
- Genetics
- Artificial Intelligence
Background:
- Turner syndrome (TS) is a significant cause of short stature in girls.
- Clinical misdiagnosis and missed diagnoses of TS occur.
- Hand skeletal characteristics in TS patients require further analysis.
Purpose of the Study:
- Analyze hand X-ray parameters for diagnosing TS.
- Develop a deep learning-based screening model for TS.
Main Methods:
- Retrospective case-control study of 101 pediatric TS patients.
- Comparison of hand X-ray parameters.
- Development of a predictive model using deep learning (ResNet50).
Main Results:
- Identified four diagnostic hand radiograph parameters: metacarpal length ratio, ulnoradial tangent distance, carpal angle, and ulnar-radial angle.
- Ulnoradial tangent distance cutoff of 0.40 cm yielded 92.57% specificity.
- ResNet50 model achieved 78.89% accuracy, 76.67% specificity, and 83.33% sensitivity on test data.
Conclusions:
- Specific hand radiograph parameters show potential as diagnostic indicators for TS.
- Deep learning models significantly improve the diagnostic precision for TS.
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