Improving Radiographic Fracture Recognition Performance and Efficiency Using Artificial Intelligence.
Journal
Radiology
ISSN: 1527-1315
Titre abrégé: Radiology
Pays: United States
ID NLM: 0401260
Informations de publication
Date de publication:
03 2022
03 2022
Historique:
pubmed:
22
12
2021
medline:
5
3
2022
entrez:
21
12
2021
Statut:
ppublish
Résumé
Background Missed fractures are a common cause of diagnostic discrepancy between initial radiographic interpretation and the final read by board-certified radiologists. Purpose To assess the effect of assistance by artificial intelligence (AI) on diagnostic performances of physicians for fractures on radiographs. Materials and Methods This retrospective diagnostic study used the multi-reader, multi-case methodology based on an external multicenter data set of 480 examinations with at least 60 examinations per body region (foot and ankle, knee and leg, hip and pelvis, hand and wrist, elbow and arm, shoulder and clavicle, rib cage, and thoracolumbar spine) between July 2020 and January 2021. Fracture prevalence was set at 50%. The ground truth was determined by two musculoskeletal radiologists, with discrepancies solved by a third. Twenty-four readers (radiologists, orthopedists, emergency physicians, physician assistants, rheumatologists, family physicians) were presented the whole validation data set (
Identifiants
pubmed: 34931859
doi: 10.1148/radiol.210937
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
627-636Commentaires et corrections
Type : CommentIn