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

David Dreizin1, Florian Goldmann2, Christina LeBedis3

  • 1Emergency 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. daviddreizin@gmail.com.

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Summary

An AI model accurately classifies pelvic fracture instability from CT scans, matching radiologist performance and predicting patient outcomes. This automated tool aids in rapid triage for trauma patients with pelvic injuries.

Keywords:
Convolutional neural networkDeep learningPelvic fracturePelvic instabilityPelvic ring disruptionTile classification

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Pelvic fracture severity assessment is crucial for trauma patient management.
  • Current grading systems like Tile AO/OTA correlate with intervention needs.
  • Automated analysis of trauma CT scans could improve triage efficiency.

Purpose of the Study:

  • To develop and validate an automated deep learning method for classifying pelvic fracture instability.
  • To compare the AI model's performance against human radiologists and existing methods.
  • To assess the association of AI-predicted instability with clinical outcomes.

Main Methods:

  • A triplanar parallel concatenated network with a ResNeXt-50 backbone was trained on 373 trauma CT scans.
  • Orthogonal multiplanar reformatted (MPR) views were used as input.
  • The model's classification of rotational and translational instability was compared to an LSTM RNN, a 3D autoencoder, and a radiologist.

Main Results:

  • The triplanar network achieved high accuracy in discriminating translational (85%) and rotational (74%) instability.
  • Model performance was comparable to a radiologist for rotational instability and superior for translational instability.
  • Inference time was less than 0.1 seconds per image, and predicted instability correlated with angioembolization and transfusion needs.

Conclusions:

  • A deep learning model using multiview CT data can accurately predict pelvic fracture instability.
  • The AI's performance rivals that of experienced radiologists.
  • This automated approach shows promise for improving trauma care and patient outcomes.