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Improving rib fracture detection accuracy and reading efficiency with deep learning-based detection software: a

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Deep learning (DL) significantly improved radiologists' accuracy in detecting rib fractures on CT scans. This AI tool also reduced reading times, enhancing overall efficiency in radiological workflows.

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Rib fractures are common injuries in blunt chest trauma.
  • Accurate and efficient detection of rib fractures on CT is crucial for patient management.
  • Deep learning (DL) offers potential for improving diagnostic performance in medical imaging.

Purpose of the Study:

  • To evaluate the impact of deep learning (DL) on radiologists' accuracy and efficiency in detecting rib fractures using CT.
  • To compare DL as a concurrent reader versus a second reader for rib fracture detection.

Main Methods:

  • 198 blunt chest trauma patients undergoing thin-slice CT were included.
  • Two radiologists read CT scans in three sessions: unassisted, with DL as a concurrent reader, and with DL as a second reader.
  • Sensitivity, false-positive rates, and reading times were compared across sessions.

Main Results:

  • DL significantly increased sensitivity for rib fracture detection compared to unassisted reading for both radiologists (p < 0.05).
  • No significant difference in sensitivity was found between DL as a concurrent reader and DL as a second reader.
  • Reading time decreased by 36% (Radiologist 1) and 34% (Radiologist 2) when DL was used as a concurrent reader.

Conclusions:

  • Deep learning (DL) integration as a concurrent reader enhances CT-based rib fracture detection accuracy and reading efficiency.
  • DL can be effectively incorporated into radiology workflows to optimize the detection of rib fractures.