Publication

Improving traumatic fracture detection on radiographs with artificial intelligence support: a multi-reader study

Authors:

Rikke Bachmann, MSc, Gozde Gunes, MD, Stine Hangaard, MD, PhD, Andreas Nexmann, MSc, Pavel Lisouski, MSc, Mikael Boesen, MD, PhD, Michael Lundemann, PhD, and Scott G Baginski, MD

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Objective

This study aimed to assess how well nonspecialist readers could detect traumatic fractures on appendicular skeletal X-rays, both with and without the assistance of RBfracture™.

Key findings

  • Using RBfracture increased sensitivity from 72% to 80% and specificity by 81% to 85% compared to reading without RBfracture
  • The increase in sensitivity resulted in a relative reduction of missed fractures by 29%
  • The largest gain in fracture detection performance with RBfracture was on nonobvious fractures (with a significant increase in sensitivity from 60% to 71%)

Open access

This research is open access

This original research can be read in BJR|Open in volume 6, issue 1, published January 2024.

Funding and conflicts of interests

This study was funded by Radiobotics, the manufacturer of RBfracture. R.B., M.L., P.L., and A.N. are employees of Radiobotics. M.B. is a member of the clinical advisory board of Radiobotics.

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