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Machine Learning Detection and Characterization of Splenic Injuries on Abdominal Computed Tomography.

Mohammad Hamghalam1,2, Robert Moreland3,4, David Gomez5,6,7,8

  • 1School of Computing and Department of Biomedical and Molecular Sciences, Queen's University, Kingston, ON, Canada.

Canadian Association of Radiologists Journal = Journal L'Association Canadienne Des Radiologistes
|January 8, 2024
PubMed
Summary

This study developed a machine learning system to automate the detection of splenic injuries from CT scans. The AI model shows promise in accurately classifying normal, low-grade, and high-grade spleen injuries, aiding faster patient management.

Keywords:
AASTThe American Association for the Surgery of Traumacomputed tomographyinjury scoring scalemachine learningsplenic injury

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Area of Science:

  • Radiology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Multi-detector contrast-enhanced abdominal computed tomography (CT) is crucial for diagnosing traumatic splenic injuries.
  • Rapid interpretation of CT scans is vital but challenging in busy emergency settings.
  • Machine learning offers potential for automating spleen injury detection and classification.

Purpose of the Study:

  • To develop a machine learning system for automated detection and classification of traumatic splenic injuries.
  • To improve the speed and accuracy of spleen injury assessment in emergency radiology.

Main Methods:

  • A 2-stage machine learning approach was employed.
  • Spleens were segmented from CT images, followed by classification using a 3D dense convolutional neural network (DenseNet).
  • American Association for the Surgery of Trauma (AAST) injury grades (normal, low-grade I-III, high-grade IV-V) were used for classification.

Main Results:

  • The study analyzed 1216 trauma CT scans (608 with splenic injuries, 608 without).
  • The model achieved area under the curve (AUC) values of 0.84 for normal spleens, 0.69 for low-grade injuries, and 0.90 for high-grade injuries.
  • Ground truth labels were established by five experienced abdominal radiologists using the AAST scale.

Conclusions:

  • The developed machine learning method demonstrates feasibility for automating spleen injury detection.
  • This automation has potential applications in prioritizing radiologist worklists and stratifying injury severity.
  • Further research is needed to optimize the model and validate its performance in clinical settings.