Download PDFOpen PDF in browserAutomatic method for computing radiographic parameters of radial metaphyseal fractures in radiographs for surgical decision support4 pages•Published: December 13, 2022AbstractPurposeDistal radius fractures (DRF) are common types of fractures with a high incident rate. DRF can be treated either by cast or surgery. To determine the clinical procedure and the operative management, standardized guidelines have become increasingly common. As operative indications are controversial, radiographic parameters (RPs) can provide objective support for effective decision making. Calculating the RPs manually from radiographs is time consuming and subject to observer variability and clinician experience. Our aim was to develop an automatic method for accurately and reliably computing 10 RPs associated with DRF in anteroposterior (AP) and lateral radiographs of a fractured hand with and without cast. Methods The inputs are the AP and lateral radiographs of the fractured hand with or without cast. The outputs are 10 RP values and composite images showing the landmark points and axes used in the RPs computation on the radiographs. Our method comprises three main steps: 1) segmentation of the radius and the ulna with a deep learning radiograph pixel classifier; 2) landmark points and axis extraction from the segmentations using geometric model-based methods; 3) RPs computation from the landmarks and generation of composite images. Our study tested the accuracy of step 2. The dataset consists of 20 pairs of AP and lateral radiographs. Ground truth radius and ulna segmentations were manually performed by an expert clinician co-author. Ground truth landmarks were manually located and annotated by the two expert clinician co-authors. The computed RP was considered accurate (in range) when its value was inside the inter and intra observer variability range of the manual annotation. The overall accuracy of the AP and lateral measurements was obtained by averaging the accuracy of each RP. Results The accuracy of the computed AP RPs is 92.7%. The Radial Length and Radial Shift are within the observer variability range; for the Radial Angle, Ulnar Variance and Step all cases are within range except for one outlier; the Gap has two outlier cases. The accuracy of the computed lateral RPs is 100%: all four Palmer Tilt, Dorsal Shift, Gap, and Step are within the clinician observer variability. Conclusion Automatic computation of distal radius fractures RPs from AP and lateral radiographs of hands with and without cast can be performed accurately. Precise and consistent measurement of RPs may improve the clinical decision making process. Keyphrases: artificial intelligence and deep learning, automatic radiographic measurement, distal radius fracture, radiographic parameters In: Ferdinando Rodriguez Y Baena, Joshua W Giles and Eric Stindel (editors). Proceedings of The 20th Annual Meeting of the International Society for Computer Assisted Orthopaedic Surgery, vol 5, pages 166-169.
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