Clinical evaluation of AI software for rib fracture detection and its impact on junior radiologist performance.

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Abstract

The detection of rib fractures (RFs) on computed tomography (CT) images is time-consuming and susceptible to missed diagnosis. An automated artificial intelligence (AI) detection system may be helpful to improve the diagnostic efficiency for junior radiologists.

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Author and article information

Journal

Journal ID (iso-abbrev): Acta Radiol

Title: Acta radiologica (Stockholm, Sweden : 1987)

Publisher: SAGE Publications

ISSN (Electronic): 1600-0455

ISSN (Print): 0284-1851

Publication date (Electronic): Nov 2022

Volume: 63

Issue: 11

Affiliations

[1 ] Department of Radiology, 26447Peking University First Hospital, Beijing, PR China.

[2 ] Shanghai United Imaging Intelligence Co., Ltd, Shanghai, PR China.

[3 ] Department of Radiology, 74639Beijing Chaoyang Hospital, Capital Medical University, Beijing, PR China.

[4 ] Department of Radiology, 74569China-Japan Union Hospital of Jilin University, Changchun, PR China.

[5 ] Department of Radiology, Shanghai Ninth People's Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, PR China.

Article

DOI: 10.1177/02841851211043839

PubMed ID: 34617809

SO-VID: 3e909fd0-3bb8-4372-9e5c-a495bd9813b7

History

Keywords: artificial intelligence,multidetector computed tomography,thoracic injuries,Rib fracture

Data availability:

Keywords: artificial intelligence, multidetector computed tomography, thoracic injuries, Rib fracture

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