Improving Radiographic Fracture Recognition Performance and Efficiency Using Artificial Intelligence

Autor: Ali Guermazi, Chadi Tannoury, Andrew J. Kompel, Akira M. Murakami, Alexis Ducarouge, André Gillibert, Xinning Li, Antoine Tournier, Youmna Lahoud, Mohamed Jarraya, Elise Lacave, Hamza Rahimi, Aloïs Pourchot, Robert L. Parisien, Alexander C. Merritt, Douglas Comeau, Nor-Eddine Regnard, Daichi Hayashi
Rok vydání: 2021
Předmět:
Zdroj: Radiology
ISSN: 1527-1315
Popis: 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 (
Databáze: OpenAIRE