An Interpretable Approach to Automated Severity Scoring in Pelvic Trauma
- URL: http://arxiv.org/abs/2105.10238v1
- Date: Fri, 21 May 2021 09:52:33 GMT
- Title: An Interpretable Approach to Automated Severity Scoring in Pelvic Trauma
- Authors: Anna Zapaishchykova, David Dreizin, Zhaoshuo Li, Jie Ying Wu, Shahrooz
Faghih Roohi, Mathias Unberath
- Abstract summary: Pelvic ring disruptions result from blunt injury mechanisms and are often found in patients with multi-system trauma.
To grade pelvic fracture severity in trauma victims based on whole-body CT, the Tile AO/OTA classification is frequently used.
This paper introduces an automated yet interpretable pelvic trauma decision support system to assist radiologists in fracture detection and Tile grade classification.
- Score: 3.762671763237911
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Pelvic ring disruptions result from blunt injury mechanisms and are often
found in patients with multi-system trauma. To grade pelvic fracture severity
in trauma victims based on whole-body CT, the Tile AO/OTA classification is
frequently used. Due to the high volume of whole-body trauma CTs generated in
busy trauma centers, an automated approach to Tile classification would provide
substantial value, e.,g., to prioritize the reading queue of the attending
trauma radiologist. In such scenario, an automated method should perform
grading based on a transparent process and based on interpretable features to
enable interaction with human readers and lower their workload by offering
insights from a first automated read of the scan. This paper introduces an
automated yet interpretable pelvic trauma decision support system to assist
radiologists in fracture detection and Tile grade classification. The method
operates similarly to human interpretation of CT scans and first detects
distinct pelvic fractures on CT with high specificity using a Faster-RCNN model
that are then interpreted using a structural causal model based on clinical
best practices to infer an initial Tile grade. The Bayesian causal model and
finally, the object detector are then queried for likely co-occurring fractures
that may have been rejected initially due to the highly specific operating
point of the detector, resulting in an updated list of detected fractures and
corresponding final Tile grade. Our method is transparent in that it provides
finding location and type using the object detector, as well as information on
important counterfactuals that would invalidate the system's recommendation and
achieves an AUC of 83.3%/85.1% for translational/rotational instability.
Despite being designed for human-machine teaming, our approach does not
compromise on performance compared to previous black-box approaches.
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