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Detection of developmental dysplasia of the hip in X-ray images using deep transfer learning
Mohammad Fraiwan1, Noran Al-Kofahi2, Ali Ibnian2
1Department of Computer Engineering, Jordan University of Science and Technology, Irbid, Jordan. mafraiwan@just.edu.jo.
Insights
This study introduces an AI-powered tool for detecting developmental dysplasia of the hip (DDH) in newborns using X-ray images. The deep learning model achieved 96.3% accuracy, offering a promising automated diagnostic method.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Pediatric Orthopedics
Background:
- Developmental dysplasia of the hip (DDH) is a common newborn condition affecting hip joint development.
- Early DDH diagnosis is crucial for effective treatment, potentially avoiding surgery and reducing brace duration.
- Pelvic X-rays are the standard for DDH diagnosis, but manual measurement can be time-consuming.
Purpose of the Study:
- To develop and evaluate an automated deep learning system for detecting DDH from pelvic X-ray images.
- To assess the performance of various deep transfer learning models in classifying DDH.
- To determine if AI can diagnose DDH without requiring explicit radiological measurements.
Main Methods:
- Collected 354 anteroposterior pelvic X-ray images (120 DDH, 234 normal) from two Jordanian hospitals.
- Utilized thirteen deep transfer learning models to build an image classification system.
- Evaluated model performance using metrics like accuracy, sensitivity, specificity, and F1 score.
Main Results:
- The DarkNet53 model achieved the highest mean DDH detection accuracy at 96.3%.
- All models demonstrated high sensitivity (recall) for DDH detection, with DarkNet53 achieving 100% recall.
- DarkNet53 reported an F1 score of 95%, precision of 90.6%, and specificity of 94.3%.
Conclusions:
- The developed automated method shows high accuracy for DDH screening and diagnosis.
- Deep transfer learning offers a viable AI-driven approach for DDH detection in medical imaging.
- Expanding the dataset with more X-ray images could further enhance the system's performance.
Background:
Developmental dysplasia of the hip (DDH) is a relatively common disorder in newborns, with a reported prevalence of 1-5 per 1000 births. It can lead to developmental abnormalities in terms of mechanical difficulties and a displacement of the joint (i.e., subluxation or dysplasia). An early diagnosis in the first few months from birth can drastically improve healing, render surgical intervention unnecessary and reduce bracing time. A pelvic X-ray inspection represents the gold standard for DDH diagnosis. Recent advances in deep learning artificial intelligence have enabled the use of many image-based medical decision-making applications. The present study employs deep transfer learning in detecting DDH in pelvic X-ray images without the need for explicit measurements.
Methods:
Pelvic anteroposterior X-ray images from 354 subjects (120 DDH and 234 normal) were collected locally at two hospitals in northern Jordan. A system that accepts these images as input and classifies them as DDH or normal was developed using thirteen deep transfer learning models. Various performance metrics were evaluated in addition to the overfitting/underfitting behavior and the training times.
Results:
The highest mean DDH detection accuracy was 96.3% achieved using the DarkNet53 model, although other models achieved comparable results. A common theme across all the models was the extremely high sensitivity (i.e., recall) value at the expense of specificity. The F1 score, precision, recall and specificity for DarkNet53 were 95%, 90.6%, 100% and 94.3%, respectively.
Conclusions:
Our automated method appears to be a highly accurate DDH screening and diagnosis method. Moreover, the performance evaluation shows that it is possible to further improve the system by expanding the dataset to include more X-ray images.
