Traumatic Rib Fracture: Conservative Therapy or Surgical Fixation?

Mayberry, John; Schipper, Paul H.

doi:10.1007/978-1-84996-492-0_57

Cited by 2 publications

(1 citation statement)

References 29 publications

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“…This fact in combination with the lack of time (2), a noisy work environment (3), satisfaction of search (4), and the frequent co-occurence of multiple traumas on whole-body computed tomography (CT) (5) lead to a significant number of missed rib fractures in this setting (6). While most rib fractures heal without surgical intervention (7), there are three reasons why it is nonetheless important to detect rib fractures: first, they are indicators of trauma-associated conditions that require immediate treatment, such as pneumothorax, and their onset can be delayed for several days (8). Second, often as a consequence of inadequate pain control, respiratory complications, such as posttraumatic pneumonia occur secondary to rib fractures (9,10).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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