Literature DB >> 31973944

Comparison of the performance between Frontier ASPECTS software and different levels of radiologists on assessing CT examinations of acute ischaemic stroke patients.

L Li1, Y Chen2, Y Bao3, X Jia3, Y Wang3, T Zuo3, F Zhu3.   

Abstract

AIM: To compare the performance of Frontier Alberta Stroke Program Early CT Score (ASPECTS) software with different levels of radiologists in assessing computed tomography (CT) examinations of patients with early acute ischaemic stroke (AIS), and to evaluate whether this software can improve the performance of rating by less experienced radiologists.
MATERIALS AND METHODS: Unenhanced brain CT examinations of 55 patients with acute middle cerebral artery ischaemia were scored separately by Frontier, two senior radiologists, and two junior radiologists retrospectively and blinded to any clinical information. Two junior radiologists then scored again with the assist of Frontier. The reference standard was defined as the ASPECTS on Follow-up unenhanced CT scored by another two non-blinded independent experts on a consensus basis. Statistical analysis was performed using intraclass correlation coefficient (ICC) analysis and Bland-Altman plots.
RESULTS: Frontier and senior radiologists in ASPECTS reading have excellent agreement with the reference standard (r=0.842 and 0.803, respectively), while only a good agreement was found between junior radiologists and reference standard (r=0.680). Bland-Altman analysis revealed the mean ASPECTS difference and SD difference of junior radiologists were larger (mean difference=1.35; SD=1.42) than that of Frontier and senior radiologists with reference standard (mean difference=0.16, 0.22; SD=1.24, 1.13, respectively). However, with the assist of Frontier, the agreement between junior radiologists and reference standard was improved from good (r=0.680) to excellent (r =0.852), and the mean ASPECTS difference and SD difference were reduced.
CONCLUSION: High agreement in ASPECTS rating between senior radiologists, Frontier, and expert consensus reading was found. Moreover, Frontier can improve the performance of less experienced radiologists to assess the ASPECTS of patients with AIS.
Copyright © 2020 The Royal College of Radiologists. Published by Elsevier Ltd. All rights reserved.

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Year:  2020        PMID: 31973944     DOI: 10.1016/j.crad.2019.12.010

Source DB:  PubMed          Journal:  Clin Radiol        ISSN: 0009-9260            Impact factor:   2.350


  1 in total

1.  Distinguishing nontuberculous mycobacteria from Mycobacterium tuberculosis lung disease from CT images using a deep learning framework.

Authors:  Li Wang; Wenlong Ding; Yan Mo; Dejun Shi; Shuo Zhang; Lingshan Zhong; Kai Wang; Jigang Wang; Chencui Huang; Shu Zhang; Zhaoxiang Ye; Jun Shen; Zhiheng Xing
Journal:  Eur J Nucl Med Mol Imaging       Date:  2021-06-16       Impact factor: 9.236

  1 in total

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