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Coronary artery calcium score quantification using a deep-learning algorithm.

Authors
  • Wang, W1
  • Wang, H1
  • Chen, Q2
  • Zhou, Z1
  • Wang, R1
  • Wang, H1
  • Zhang, N1
  • Chen, Y3
  • Sun, Z4
  • Xu, L5
  • 1 Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China. , (China)
  • 2 ShuKun (Beijing) Network Technology Co., Limited, Shanghai, China. , (China)
  • 3 Department of Cardiology, Chinese PLA General Hospital, Beijing, China. , (China)
  • 4 Department of Medical Radiation Sciences, Curtin University, Perth, Western Australia, 6845, Australia. , (Australia)
  • 5 Department of Radiology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China. Electronic address: [email protected] , (China)
Type
Published Article
Journal
Clinical radiology
Publication Date
Nov 09, 2019
Identifiers
DOI: 10.1016/j.crad.2019.10.012
PMID: 31718789
Source
Medline
Language
English
License
Unknown

Abstract

To investigate the impact of a deep-learning algorithm on the quantification of coronary artery calcium score (CACS) and the stratification of cardiac risk. Computed tomography data of 530 patients who underwent CACS scan were included retrospectively. The scoring (including Agatston, mass, and volume scores) was done manually. The deep-learning method was trained using data from 300 patients to calculate CACS based on the manual calculation. The automated method was validated on a set of data from 90 patients and subsequently tested on a new set of data from 140 patients against manual CACS. For the data from 140 patients that were used to analyse the accuracy of deep-learning algorithm, the total CACS obtained manually and by using the deep-learning algorithm was recorded. Agatston score categories and cardiac risk categorisation of the two methods were compared. No significant differences were found between the manually derived and deep-learning Agatston, mass, and volume scores. The Agatston score categories and cardiac risk stratification displayed excellent agreement between the two methods, with kappa = 0.77 (95% confidence interval [CI]=0.73-0.81); however, a 13% reclassification rate was observed. Deep-learning algorithm can provide reliable Agatston, mass, and volume scores and enables cardiac risk stratification. Copyright © 2019. Published by Elsevier Ltd.

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