BoneView
Evaluation of the use of artificial intelligence in the detection of appendicular skeletal fractures in adult patients consulting in an emergency department
Evelyne Dubreucq Guerif, Sophie Agut, Alexandra Rousseau, Rudy Bompard and Hélène Goulet
Data
- Consecutive data of limb x-rays from the emergency department over 1 year
- 4475 patients in total
Design
- Retrospective study
- Only examining radiographs with discrepancies between the AI results and the ED physician’s diagnosis
- Emergency physicians re-evaluated the discrepant radiographs with AI assistance
Ground truth:
- Concordance between AI and report
- In the event of discrepancies, adjudication by a radiologist
Results
- 603 discordant cases
- 282 = confirmed fractures
- 220 were not identified by the emergency physician and 62 were correctly identified
Proportion of fractures missed by the ED physician without AI VS Proportion of fractures missed by the ED physician with AI
78.0% VS 45.3%
- Diagnostic error rate decreased by 41.9% with AI assistance
Read more study
BoneView
Early budget impact analysis of AI to support the review of radiographic examinations for suspected fractures in NHS emergency departments (ED)
Lucy Gregory, Trishal Boodhna, Mathew Storey, Susan Shelmerdine, Alex Novak, David Lowe, Hugh Harvey
BoneView
Improving fracture detection with artificial intelligence
Ana Capelastegui Alber, Roque Oca Pernas, Gotzon Iglesias Hidalgo, Jose Alejandro Larena, Miguel Angel Ulibarrena Saiz
BoneView
AI-powered solution for bone trauma