Assessment of a Deep Learning Algorithm for the Detection of Rib Fractures on Whole-Body Trauma Computed Tomography

Korean Journal of Radiology : Official Journal of the Korean Radiological Society
Thomas WeikertAlexander Walter Sauter

Abstract

To assess the diagnostic performance of a deep learning-based algorithm for automated detection of acute and chronic rib fractures on whole-body trauma CT. We retrospectively identified all whole-body trauma CT scans referred from the emergency department of our hospital from January to December 2018 (n = 511). Scans were categorized as positive (n = 159) or negative (n = 352) for rib fractures according to the clinically approved written CT reports, which served as the index test. The bone kernel series (1.5-mm slice thickness) served as an input for a detection prototype algorithm trained to detect both acute and chronic rib fractures based on a deep convolutional neural network. It had previously been trained on an independent sample from eight other institutions (n = 11455). All CTs except one were successfully processed (510/511). The algorithm achieved a sensitivity of 87.4% and specificity of 91.5% on a per-examination level [per CT scan: rib fracture(s): yes/no]. There were 0.16 false-positives per examination (= 81/510). On a per-finding level, there were 587 true-positive findings (sensitivity: 65.7%) and 307 false-negatives. Furthermore, 97 true rib fractures were detected that were not mentioned in the written CT re...Continue Reading

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Citations

Dec 6, 2020·EBioMedicine·A BlumP Gondim Teixeira
Jun 5, 2021·Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society·Riel Castro-ZuntiSeok-Bum Ko
Oct 29, 2021·Forensic Science, Medicine, and Pathology·Victor IbanezAkos Dobay
Dec 8, 2021·Scientific Reports·Liding YaoMinming Zhang

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