Wrist and elbow fracture detection and segmentation by artificial intelligence using point-of-care ultrasound.

Purpose: Distal radius (wrist) and supracondylar (elbow) fractures are common in children presenting to Pediatric Emergency Departments (EDs). These fractures are treated conservatively or surgically depending on deformity severity. Radiographs are typically used for diagnosis but can increase wait times due to the need for radiation-safe rooms. Ultrasound (US) offers a radiation-free, faster alternative that can be performed at triage, but its noisy images are challenging to interpret.

Methods: We developed an artificial intelligence (AI) technique for the automatic diagnosis of fractures at the wrist and elbow. While most AI for diagnosis focuses on classification results only, we applied a more explainable pipeline that used US bony region segmentation from a CNN as the basis of fracture detection. Our approach was validated on 3,822 wrist US images and 1487 elbow US images. We compared the fracture detection results from classification models and multi-channel segmentation models.

Results: Combining the segmentation results with the original images showed superior performance in fracture detection at the individual patient level, achieving an accuracy of 0.889 and 0.750, sensitivity of 0.818 and 1.000, and specificity of 1.000 and 0.714 on the wrist and elbow dataset respectively. Besides, the multi-channel U-Net architecture effectively detected bony fracture regions in wrist US images.

Conclusion: These findings demonstrate that AI models can enable reliable, automatic wrist and elbow fracture detection in pediatric EDs, potentially reducing wait times and optimizing medical resource use.