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X-ray Imaging01:24

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German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with...
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Improving traumatic fracture detection on radiographs with artificial intelligence support: a multi-reader study.

Rikke Bachmann1, Gozde Gunes2, Stine Hangaard3

  • 1Radiobotics ApS, Copenhagen, Denmark.

BJR Open
|May 17, 2024
PubMed
Summary

Artificial intelligence (AI) tools significantly improve nonspecialist readers' ability to detect traumatic fractures on appendicular skeleton radiographs. This AI support enhances both sensitivity and specificity without increasing reading time, aiding in fracture diagnosis.

Keywords:
artificial intelligencediagnostic performancefracture detectionmulti-reader study

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Nonspecialist readers often face challenges in accurately detecting traumatic fractures on radiographs.
  • The integration of artificial intelligence (AI) offers potential to enhance diagnostic performance in medical imaging.

Purpose of the Study:

  • To evaluate the diagnostic performance of nonspecialist readers in detecting appendicular skeleton traumatic fractures with and without AI support.
  • To assess the impact of AI on sensitivity, specificity, and interpretation time for fracture detection.

Main Methods:

  • A retrospective, multi-reader, multi-case study involving 15 nonspecialist readers assessing 340 radiographic exams.
  • Readers evaluated exams with and without an AI fracture detection support tool, with reading times recorded.
  • Sensitivity, specificity, and false positives per patient were calculated against a reference standard established by consultant radiologists.

Main Results:

  • AI support significantly improved patient-wise sensitivity (72% to 80%) and specificity (81% to 85%).
  • AI led to a 29% relative reduction in missed fractures and a 21% relative reduction in false positives per patient.
  • The most substantial gains were observed in detecting nonobvious fractures, with sensitivity increasing by 11 percentage points.

Conclusions:

  • AI fracture detection support tools enhance the diagnostic performance of nonspecialist readers for traumatic appendicular skeleton fractures.
  • The AI tool improved both sensitivity and specificity without adversely affecting interpretation time.
  • This study highlights the novel application of AI in differentiating obvious and nonobvious fractures, advancing AI reader comparison studies.