Digital Eye: Deep Learning for Detecting Physeal Fractures of the Pediatric Distal Radius.

BACKGROUND

Distal radial fractures (DRFs) are some of the most common pediatric injuries, often involving the physis. Diagnostic accuracy can be challenging for physicians unfamiliar with managing pediatric injuries, with misdiagnosis rates reported as high as 46% [1-3]. These patients frequently do not present to a pediatric orthopaedic surgeon or musculoskeletal radiologist initially. Therefore, the development of tools to classify DRFs by physeal involvement could improve diagnostic accuracy and hence enhance patient safety. We propose to fine-tune convolutional neural networks (CNNs), a class of artificial intelligence models that are highly effective at analyzing and interpreting visual data like radiographs, to classify skeletally immature DRF radiographs by physeal involvement.

METHODS

DRF radiographs from skeletally immature patients aged 4 to 18 years were identified from a database at a single tertiary-care children's hospital. All fractures were classified regarding physeal involvement by a pediatric orthopaedic surgeon. We fine-tuned a particularly powerful family of CNNs (EfficientNet) on the dataset and calculated the accuracy, precision, recall, and F1-score of the classification. To interpret the model's predictions, we used gradient-weighted class activation mapping (Grad-CAM), a technique that visualizes the gradient-weighted class activation maps of the convolutional layers, to highlight regions of the radiographs that the model learned to focus on for classification.

RESULTS

2,103 radiographs comprised of anteroposterior and lateral views from 1,082 patients (655 [60.5%] male, mean age: 10 ± 5.7 years) were included. There were 425 views of 203 physeal fractures and 1,678 views of 879 nonphyseal fractures. The best model was EfficientNet-B2, which achieved a validation accuracy of 84% and a test accuracy of 84% in identifying physeal fractures. The model had high precision (81%), recall (89%), and F1-score (0.86) on the test set.

CONCLUSIONS

Fine-tuning an EfficientNet model can achieve high accuracy in identifying physeal fractures. Furthermore, Grad-CAM provides interpretability and transparency for the model's predictions, highlighting regions of interest and potential sources of error. Results suggest that CNNs can be a powerful tool for fracture classification and that EfficientNet is a promising architecture for this task. Future work should include expanding the dataset with more diverse and balanced samples and testing the model on different views and fracture patterns.

KEY CONCEPTS: (1)Patients with physeal injuries often do not present to orthopaedists initially.(2)Tools to assist urgent care and emergency room physicians should be aimed at identifying which fractures need orthopaedic follow-up.(3)Convolutional neural networks are a worthwhile option for this task.(4)Historically, these networks have had difficulty differentiating a fracture from a physis.(5)Fine tuning EfficientNet in this study demonstrates an ability to separate physeal fractures from nonphyseal fractures, which could assist "front line" evaluators in determining the necessity of orthopaedic follow-up.

LEVEL OF EVIDENCE: III, retrospective cohort study.