Practical Computer Vision Application to Detect Hip Fractures on Pelvic X-rays: a Bi-institutional Study

Background: Pelvic X-ray (PXR) is a ubiquitous modality to diagnose hip fractures. However, not all healthcare settings employ round-the-clock radiologists and PXR sensitivity for diagnosing hip fracture may vary depending on digital display. We aimed to validate a computer vision algorithm to detect hip fractures across two institutions' heterogeneous patient populations. We hypothesized a convolutional neural network algorithm can accurately diagnose hip fractures on PXR and a web application can facilitate its bedside adoption.

Methods: The development cohort comprised 4235 PXRs from Chang Gung Memorial Hospital (CGMH). The validation cohort comprised 500 randomly sampled PXRs from CGMH and Stanford's level I trauma centers. Xception was our convolutional neural network structure. We randomly applied image augmentation methods during training to account for image variations and used gradient-weighted class activation mapping to overlay heatmaps highlighting suspected fracture locations.

Results: Our hip fracture detection algorithm's area under the receiver operating characteristic curves were 0.98 and 0.97 for CGMH and Stanford's validation cohorts, respectively. Besides negative predictive value (0.88 Stanford cohort), all performance metrics-sensitivity, specificity, predictive values, accuracy, and F1 score-were above 0.90 for both validation cohorts. Our web application allows users to upload PXR in multiple formats from desktops or mobile phones and displays probability of the image containing a hip fracture with heatmap localization of the suspected fracture location.

Discussion: We refined and validated a high-performing computer vision algorithm to detect hip fractures on PXR. A web application facilitates algorithm use at the bedside, but the benefit of using our algorithm to supplement decision-making is likely institution dependent. Further study is required to confirm clinical validity and assess clinical utility of our algorithm.

Level Of Evidence: III, Diagnostic tests or criteria.