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Tytuł pozycji:

Convolutional neural network performance compared to radiologists in detecting intracranial hemorrhage from brain computed tomography: A systematic review and meta-analysis.

Tytuł:
Convolutional neural network performance compared to radiologists in detecting intracranial hemorrhage from brain computed tomography: A systematic review and meta-analysis.
Autorzy:
Daugaard Jørgensen M; Faculty of Health and Medical Sciences, Copenhagen University, Copenhagen, Denmark.
Antulov R; Department of Radiology and Nuclear Medicine, Hospital of South West Jutland, University Hospital of Southern Denmark, Esbjerg, Denmark; Department of Regional Health Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark.
Hess S; Department of Radiology and Nuclear Medicine, Hospital of South West Jutland, University Hospital of Southern Denmark, Esbjerg, Denmark; Department of Regional Health Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark.
Lysdahlgaard S; Department of Radiology and Nuclear Medicine, Hospital of South West Jutland, University Hospital of Southern Denmark, Esbjerg, Denmark; Department of Regional Health Research, Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark. Electronic address: .
Źródło:
European journal of radiology [Eur J Radiol] 2022 Jan; Vol. 146, pp. 110073. Date of Electronic Publication: 2021 Nov 24.
Typ publikacji:
Journal Article; Meta-Analysis; Systematic Review
Język:
English
Imprint Name(s):
Publication: Limerick : Elsevier Science Ireland Ltd
Original Publication: Stuttgart ; New York : Thieme, [c1981-
MeSH Terms:
Neural Networks, Computer*
Radiologists*
Humans ; Intracranial Hemorrhages/diagnostic imaging ; Retrospective Studies ; Sensitivity and Specificity ; Tomography, X-Ray Computed
Contributed Indexing:
Keywords: Artificial Intelligence; Computed tomography; Intracranial hemorrhage; Meta-analysis; Systematic review
Entry Date(s):
Date Created: 20211130 Date Completed: 20220103 Latest Revision: 20220103
Update Code:
20240104
DOI:
10.1016/j.ejrad.2021.110073
PMID:
34847397
Czasopismo naukowe
Purpose: To compare the diagnostic accuracy of convolutional neural networks (CNN) with radiologists as the reference standard in the diagnosis of intracranial hemorrhages (ICH) with non contrast computed tomography of the cerebrum (NCTC).
Methods: PubMed, Embase, Scopus, and Web of Science were searched for the period from 1 January 2012 to 20 July 2020; eligible studies included patients with and without ICH as the target condition undergoing NCTC, studies had deep learning algorithms based on CNNs and radiologists reports as the minimum reference standard. Pooled sensitivities, specificities and a summary receiver operating characteristics curve (SROC) were employed for meta-analysis.
Results: 5,119 records were identified through database searching. Title-screening left 47 studies for full-text assessment and 6 studies for meta-analysis. Comparing the CNN performance to reference standards in the retrospective studies found a pooled sensitivity of 96.00% (95% CI: 93.00% to 97.00%), pooled specificity of 97.00% (95% CI: 90.00% to 99.00%) and SROC of 98.00% (95% CI: 97.00% to 99.00%), and combining retrospective and studies with external datasets found a pooled sensitivity of 95.00% (95% CI: 91.00% to 97.00%), pooled specificity of 96.00% (95% CI: 91.00% to 98.00%) and a pooled SROC of 98.00% (95% CI: 97.00% to 99.00%).
Conclusion: This review found the diagnostic performance of CNNs to be equivalent to that of radiologists for retrospective studies. Out-of-sample external validation studies pooled with retrospective studies found CNN performance to be slightly worse. There is a critical need for studies with a robust reference standard and external data-set validation.
(Copyright © 2021 The Author(s). Published by Elsevier B.V. All rights reserved.)

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