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Assessment of the Abdomen II: Percussion01:18

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Percussion is a fundamental technique used to assess the liver, spleen, and abdominal organs by tapping the abdomen and interpreting the resulting sounds. This method helps identify fluid, distention, and masses through variations in sound, such as the high-pitched tympany of air-filled areas and the dullness of solid masses. Understanding how to percuss these organs provides valuable information for healthcare professionals in diagnosing conditions early.
Percussion
Percussion is an essential...
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RSNA 2023 Abdominal Trauma AI Challenge: Review and Outcomes.

Sebastiaan Hermans1, Zixuan Hu1, Robyn L Ball1

  • 1From the Department of Medical Imaging, St Michael's Hospital, Unity Health Toronto, 30 Bond St, Toronto, ON, Canada M5B 1W8 (S.H., Z.H., H.M.L., I.Y., E.C.); Edward S. Rogers Department of Electrical and Computer Engineering, University of Toronto, Toronto, Ontario, Canada (Z.H., E.S.); The Jackson Laboratory, Bar Harbor, Me (R.L.B.); Department of Radiology, The Ohio State University, Columbus, Ohio (L.M.P.); Department of Medical Imaging, Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Ontario, Canada (F.H.B.); Department of Radiology, Scripps Clinic Medical Group and University of California San Diego, San Diego, Calif (J.D.R.); Radiological Society of North America, Oak Brook, Ill (M.V.); Department of Radiology, Thomas Jefferson University, Philadelphia, Pa (A.E.F.); Department of Radiology, Weill Cornell Medicine, New York, NY (G.S.); Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, Calif (J.M.), Department of Radiology, Vancouver General Hospital, Vancouver, Canada (S.N.); Department of Radiology, Memorial Sloan-Kettering Cancer Center, New York, NY (B.S.M.); Department of Radiology and Biomedical Imaging, Yale University School of Medicine, New Haven, Conn (M.A.D.); Duke University School of Medicine, Durham, NC (K.M.); North York General Hospital, Toronto, Ontario, Canada (E.S.); and Department of Medical Imaging, University of Toronto, Toronto, Ontario, Canada (E.C.).

Radiology. Artificial Intelligence
|November 6, 2024
PubMed
Summary

Machine learning models excelled at detecting abdominal trauma on CT scans, especially high-grade injuries. These AI models show promise for future research in diagnosing traumatic injuries.

Keywords:
Abdominal TraumaAmerican Association for the Surgery of TraumaArtificial IntelligenceCTMachine Learning

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Area of Science:

  • Radiology and Medical Imaging
  • Artificial Intelligence in Medicine
  • Trauma Surgery

Background:

  • Abdominal trauma detection is critical for patient outcomes.
  • Machine learning (ML) offers potential for improving diagnostic accuracy.
  • The 2023 RSNA AI Challenge focused on abdominal trauma detection using CT scans.

Purpose of the Study:

  • To evaluate the performance of top-performing ML models from the 2023 RSNA Abdominal Trauma Detection AI Challenge.
  • To assess the models' ability to detect various types of abdominal injuries on CT scans.

Main Methods:

  • Retrospective assessment of eight award-winning ML models.
  • Utilized a multicenter dataset of 4274 abdominal trauma CT scans.
  • Models were evaluated for detecting solid organ injuries (liver, spleen, kidneys), bowel/mesenteric injuries, and active extravasation using AUC metrics.

Main Results:

  • Models demonstrated high performance in detecting solid organ injuries, with mean AUCs ranging from 0.91 to 0.94.
  • Exceptional performance was noted for high-grade solid organ injuries, achieving mean AUCs of 0.98.
  • Mean AUCs for bowel/mesenteric injuries and active extravasation were 0.85.

Conclusions:

  • The award-winning ML models showed strong capabilities in identifying traumatic abdominal injuries on CT scans.
  • High-grade injuries were detected with particularly high accuracy.
  • These models establish a performance baseline for future AI development in abdominal trauma diagnostics.