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An Automated Deep Learning Method for Tile AO/OTA Pelvic Fracture Severity Grading from Trauma whole-Body CT

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Abstract

Admission trauma whole-body CT is routinely employed as a first-line diagnostic tool for characterizing pelvic fracture severity. Tile AO/OTA grade based on the presence or absence of rotational and translational instability corresponds with need for interventions including massive transfusion and angioembolization. An automated method could be highly beneficial for point of care triage in this critical time-sensitive setting. A dataset of 373 trauma whole-body CTs collected from two busy level 1 trauma centers with consensus Tile AO/OTA grading by three trauma radiologists was used to train and test a triplanar parallel concatenated network incorporating orthogonal full-thickness multiplanar reformat (MPR) views as input with a ResNeXt-50 backbone. Input pelvic images were first derived using an automated registration and cropping technique. Performance of the network for classification of rotational and translational instability was compared with that of (1) an analogous triplanar architecture incorporating an LSTM RNN network, (2) a previously described 3D autoencoder-based method, and (3) grading by a fourth independent blinded radiologist with trauma expertise. Confusion matrix results were derived, anchored to peak Matthews correlation coefficient (MCC). Associations with clinical outcomes were determined using Fisher’s exact test. The triplanar parallel concatenated method had the highest accuracies for discriminating translational and rotational instability (85% and 74%, respectively), with specificity, recall, and F1 score of 93.4%, 56.5%, and 0.63 for translational instability and 71.7%, 75.7%, and 0.77 for rotational instability. Accuracy of this method was equivalent to the single radiologist read for rotational instability (74.0% versus 76.7%, p = 0.40), but significantly higher for translational instability (85.0% versus 75.1, p = 0.0007). Mean inference time was < 0.1 s per test image. Translational instability determined with this method was associated with need for angioembolization and massive transfusion (p = 0.002–0.008). Saliency maps demonstrated that the network focused on the sacroiliac complex and pubic symphysis, in keeping with the AO/OTA grading paradigm. A multiview concatenated deep network leveraging 3D information from orthogonal thick-MPR images predicted rotationally and translationally unstable pelvic fractures with accuracy comparable to an independent reader with trauma radiology expertise. Model output demonstrated significant association with key clinical outcomes.

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Abbreviations

LSTM RNN:

Long short-term memory recurrent neural network

TP:

True positive

TN:

True negative

FP:

False positive

FN:

False negative

TPR:

True positive rate

TNR:

True negative rate

PPV:

Positive predictive value

NPV:

Negative predictive value

FNR:

False negative rate

FPR:

False positive rate

FDR:

False discovery rate

FOR:

False omission rate

DOR:

Diagnostic odds ratio

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Funding

1. NIH K08 EB027141-01A1 (PI: David Dreizin, MD). 2. RSNA Research Scholar Grant (#RSCH1605) (PI: David Dreizin, MD). 3. Accelerated Translational Incubator Pilot (ATIP) award, University of Maryland (PI: David Dreizin, MD).

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Authors and Affiliations

  1. Emergency and Trauma Imaging, Department of Diagnostic Radiology and Nuclear Medicine, R Adams Cowley Shock Trauma Center, University of Maryland School of Medicine, Baltimore, MD, USA

    David Dreizin, Alexis Boscak, Matthew Dattwyler & Uttam Bodanapally

  2. Friedrich-Alexander University, Schloßplatz, Erlangen, Germany

    Florian Goldmann & Andreas Maier

  3. Department of Radiology, Boston Medical Center, Boston University School of Medicine, Baltimore, MD, USA

    Christina LeBedis & Stephan Anderson

  4. Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD, USA

    Guang Li

  5. Department of Computer Science, Malone Center for Engineering in Healthcare, Johns Hopkins University, Baltimore, MD, USA

    Mathias Unberath

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  1. David Dreizin
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  2. Florian Goldmann
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  3. Christina LeBedis
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  4. Alexis Boscak
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  6. Uttam Bodanapally
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  8. Stephan Anderson
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Correspondence to David Dreizin.

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Dreizin, D., Goldmann, F., LeBedis, C. et al. An Automated Deep Learning Method for Tile AO/OTA Pelvic Fracture Severity Grading from Trauma whole-Body CT. J Digit Imaging 34, 53–65 (2021). https://doi.org/10.1007/s10278-020-00399-x

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