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Updated: Oct 29, 2026
Imaging of the Microstructural Failure Mechanism in the Human Hip
Published on: September 29, 2023
HipSAFE: detecting hip fractures on ultrasound imaging using deep learning in a porcine cadaveric model
Nicholas J Yee1, Yohannes Soenjaya2, Noah Kates Rose3
1Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, ON, Canada; Division of Orthopaedic Surgery, Department of Surgery, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada; Institute of Biomedical Engineering, Faculty of Applied Science & Engineering, University of Toronto, Toronto, ON, Canada.
Objective:
Hip fractures require timely diagnosis and surgical intervention, and current approaches require diagnosis with pelvic radiographs in emergency departments. Portable, diagnostically reliable point-of-care ultrasound (US) could reduce unnecessary transfers and shorten time to surgery. The objective of this work was to evaluate, in a porcine cadaver model, the accuracy of HipSAFE, a deep-learning-assisted US tool intended to reduce operator dependence and support pre-hospital hip fracture triage by nurses and paramedics.
Methods:
The bilateral hind limbs of 15 juvenile porcine cadavers (30 hips) were imaged by operators with no sonographic training before and after inducing a displaced iatrogenic pertrochanteric hip fracture. The limbs were split at the cadaver level into training (n = 16), validation (n = 6) and held-out test (n = 8) sets. Frame-level classifiers (MobileNetV3, EfficientNet-Lite and ResNet) were pre-trained on ImageNet and fine-tuned. Cine clip predictions used a moving average with Youden index threshold calibration. Model F1-scores were compared against the diagnostic radiologists using a femur-level paired bootstrap test with Holm correction.
Results:
Cine clip performance was reported on the 78 clips collected by 31 operators on 4 cadavers (8 femurs). Most architectures achieved high cine clip performance. EfficientNet-Lite0 reached an F1-score of 0.944 (95% confidence interval [CI], 0.920-0.980), sensitivity of 89.5% (95% CI, 75.9%-95.8%) and specificity of 100.0% (95% CI, 91.2%-100.0%); the majority voting ensemble reached an F1-score of 0.932 (95% CI, 0.903-0.967). The operators achieved an F1-score of 0.667 (0.581--0.750) and the two diagnostic radiologists achieved an F1-score of 0.684 (95% CI, 0.531-0.800). Although growth plates were present in intact and fractured femurs, the Grad-CAM attribution maps suggest that the models had difficulty distinguishing growth plates from fracture features.
Conclusion:
In this pre-clinical setting, a lightweight, deep-learning pipeline accurately identified hip fractures on US imaging, motivating future work on the feasibility of US with deep learning for pre-hospital hip trauma triage.
