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Improving Radiographic Fracture Recognition Performance and Efficiency Using Artificial Intelligence.

Ali Guermazi1, Chadi Tannoury1, Andrew J Kompel1

  • 1From the Departments of Radiology (A. Guermazi, A.J.K., A.M.M., H.R., A.C.M., D.H.), Orthopaedic Surgery (C.T., X.L.), and Family Medicine (D.C.), Boston University School of Medicine, Boston, Mass; Department of Radiology, VA Boston Healthcare System, 1400 VFW Parkway, Suite 1B105, West Roxbury, MA 02132 (A. Guermazi); Gleamer, Paris, France (A.D., A.T., E.L., A.P., N.E.); Department of Biostatistics, CHU Rouen, Rouen, France (A. Gillibert); Department of Rheumatology, Harvard Vanguard Medical Associates, Braintree, Mass (Y.L.); Department of Radiology, Musculoskeletal Division, Massachusetts General Hospital, Harvard Medical School, Boston, Mass 02114 (M.J.); Sorbonne Université, CNRS, Institut des Systèmes Intelligents et de Robotique, Paris, France (A.P.); Department of Orthopaedic Surgery, The Mount Sinai Hospital, New York, NY (R.L.P.); University Health Services and Primary Care Sports Medicine, Boston College, Chestnut Hill, Mass (D.C.); and Department of Radiology, Stony Brook University Renaissance School of Medicine, Stony Brook, NY (D.H.).

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Summary
This summary is machine-generated.

Artificial intelligence (AI) significantly improved fracture detection sensitivity and specificity in radiographs for physicians. This AI assistance enhanced diagnostic performance without increasing reading time, aiding both radiologists and nonradiologists.

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Area of Science:

  • Radiology
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Missed fractures are a frequent cause of diagnostic discrepancies in radiographic interpretations.
  • This study addresses the diagnostic performance gap in fracture detection on radiographs.

Purpose of the Study:

  • To evaluate the impact of artificial intelligence (AI) assistance on physician diagnostic performance for detecting fractures on radiographs.
  • To assess improvements in sensitivity and specificity with AI support.

Main Methods:

  • Retrospective diagnostic study utilizing a multi-reader, multi-case methodology with a multicenter dataset of 480 examinations.
  • Inclusion of diverse body regions: foot/ankle, knee/leg, hip/pelvis, hand/wrist, elbow/arm, shoulder/clavicle, rib cage, and spine.
  • Twenty-four physicians interpreted cases with and without AI assistance, with a 1-month washout period.

Main Results:

  • AI assistance significantly increased sensitivity per patient by 10.4% (75.2% with AI vs. 64.8% without AI; P < .001).
  • Specificity per patient showed noninferiority with AI aid (+5.0%; P = .001), reaching 95.6% compared to 90.6% without AI.
  • AI reduced average reading time by 6.3 seconds per examination (P = .046).

Conclusions:

  • AI assistance demonstrably enhances the sensitivity of fracture detection for physicians interpreting radiographs.
  • AI support also shows potential for improving specificity without prolonging interpretation time.
  • The findings suggest AI is a valuable tool for improving diagnostic accuracy in fracture detection.