Korean Journal of Radiology

The Korean Society of Radiology (대한영상의학회)
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Assessment of a Deep Learning Algorithm for the Detection of Rib Fractures on Whole-Body Trauma Computed Tomography

  • Thomas Weikert (Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel) ;
  • Luca Andre Noordtzij (Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel) ;
  • Jens Bremerich (Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel) ;
  • Bram Stieltjes (Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel) ;
  • Victor Parmar (Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel) ;
  • Joshy Cyriac (Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel) ;
  • Gregor Sommer (Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel) ;
  • Alexander Walter Sauter (Clinic of Radiology and Nuclear Medicine, University Hospital Basel, University of Basel)
  • Received : 2019.09.01
  • Accepted : 2020.02.19
  • Published : 2020.07.01
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Abstract

Objective: 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. Materials and Methods: 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). Results: 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 reports. A major factor associated with correct detection was displacement. Conclusion: We found good performance of a deep learning-based prototype algorithm detecting rib fractures on trauma CT on a per-examination level at a low rate of false-positives per case. A potential area for clinical application is its use as a screening tool to avoid false-negative radiology reports.

Keywords

Acknowledgement

We acknowledge the provision of the rib fracture detection algorithm prototype by Aidoc Medical (Tel Aviv, Israel).

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