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Deep learning approach based on a patch residual for pediatric supracondylar subtle fracture detection
Qingming Ye1, Zhilu Wang1, Yi Lou2
1Zhejiang Sci-Tech University, Hangzhou, China.
Insights
A new deep learning model accurately detects pediatric supracondylar humerus fractures using healthy bone images, overcoming limited data challenges. This AI approach shows high accuracy, aiding diagnosis in children.
Area of Science:
- Pediatric Orthopedics
- Medical Imaging Analysis
- Artificial Intelligence in Healthcare
Background:
- Supracondylar humerus fractures are common pediatric elbow injuries.
- Diagnosis is challenging due to pediatric skeletal anatomy and limited labeled fracture data.
- Deep learning models require large datasets, which are scarce for this condition.
Purpose of the Study:
- To develop a deep learning model for automatic detection and localization of pediatric supracondylar fractures.
- To address the challenge of limited labeled data in pediatric fracture diagnosis.
- To improve diagnostic accuracy and efficiency in identifying these fractures.
Main Methods:
- Introduction of a multi-scale patch residual network (MPR) framework.
- Integration of a convolutional neural network (CNN) for feature extraction.
- Utilizing a multi-scale generative adversarial network trained on healthy bone images to model skeletal integrity.
Main Results:
- The MPR model achieved 90.5% accuracy, 89% sensitivity, 92% specificity, and a 0.906 F1 score on an independent test set.
- Performance surpassed emergency medicine physicians and neared pediatric radiologists' accuracy.
- Demonstrated a fast inference speed of 1.1 seconds per image sheet.
Conclusions:
- The proposed deep learning approach effectively detects pediatric supracondylar humerus fractures with high accuracy.
- Leveraging healthy images significantly reduces reliance on labeled fracture data.
- The model shows strong potential for clinical application in improving pediatric fracture diagnosis.
Abstract:
Supracondylar humerus fractures in children are among the most common elbow fractures in pediatrics. However, their diagnosis can be particularly challenging due to the anatomical characteristics and imaging features of the pediatric skeleton. In recent years, convolutional neural networks (CNNs) have achieved notable success in medical image analysis, though their performance typically relies on large-scale, high-quality labeled datasets. Unfortunately, labeled samples for pediatric supracondylar fractures are scarce and difficult to obtain. To address this issue, this paper introduces a deep learning-based multi-scale patch residual network (MPR) for the automatic detection and localization of subtle pediatric supracondylar fractures. The MPR framework combines a CNN for automatic feature extraction with a multi-scale generative adversarial network to model skeletal integrity using healthy samples. By leveraging healthy images to learn the normal skeletal distribution, the approach reduces the dependency on labeled fracture data and effectively addresses the challenges posed by limited pediatric datasets. Datasets from two different hospitals were used, with data augmentation techniques applied during both training and validation. On an independent test set, the proposed model achieves an accuracy of 90.5%, with 89% sensitivity, 92% specificity, and an F1 score of 0.906-outperforming the diagnostic accuracy of emergency medicine physicians and approaching that of pediatric radiologists. Furthermore, the model demonstrates a fast inference speed of 1.1 s per sheet, underscoring its substantial potential for clinical application.
