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Development of a deep learning-based algorithm for the automatic detection and quantification of aortic valve calcium

Authors
 Suyon Chang  ;  Hwiyoung Kim  ;  Young Joo Suh  ;  Dong Min Choi  ;  Hyunghu Kim  ;  Dong Kyu Kim  ;  Jin Young Kim  ;  Jin Young Yoo  ;  Byoung Wook Choi 
Citation
 EUROPEAN JOURNAL OF RADIOLOGY, Vol.137 : 109582, 2021-04 
Journal Title
 EUROPEAN JOURNAL OF RADIOLOGY 
ISSN
 0720-048X 
Issue Date
2021-04
MeSH
Algorithms ; Aortic Valve / diagnostic imaging ; Aortic Valve Stenosis* ; Calcinosis* / diagnostic imaging ; Calcium ; Deep Learning* ; Humans ; Retrospective Studies ; Severity of Illness Inde
Keywords
Aortic valve stenosis ; Automated severity scoring of aortic valve calcium ; Calcium ; Computed tomography ; Deep learning
Abstract
Purpose: We aimed to develop a deep learning (DL)-based algorithm for automated quantification of aortic valve calcium (AVC) from non-enhanced electrocardiogram-gated cardiac CT scans and compare performance of DL-measured AVC volume and Agatston score with those of visual gradings by radiologist readers for classification of AVC severity. Method: A total of 589 CT examinations performed at a single center between March 2010 and August 2017 were retrospectively included. The DL algorithm was designed to segment AVC and to quantify AVC volume, and Agatston score was calculated using attenuation values. Manually measured AVC volume and Agatston score were used as ground truth. To validate AVC segmentation performance, the Dice coefficient was calculated. For observer performance testing, four radiologists determined AVC grade in two reading rounds. The diagnostic performance of DL-measured AVC volume and Agaston score for classifying severe AVC was compared with that of each reader's assessment. Results: After applying the DL algorithm, the Dice coefficient score was 0.807. In patients with AVC, accuracy of DL-measured AVC volume for AVC grading was 97.0 % with area under the curve (AUC) of 0.964 (95 % confidence interval [CI] 0.923-1) in the test set, which was better than the radiologist readers (accuracy 69.7 %-91.9 %, AUC 0.762-0.923) with manually measured AVC volume as ground truth. When manually measured AVC Agatston score was used as ground truth, accuracy of DL-measured AVC Agatston score for AVC grading was 92.9 % with AUC of 0.933 (95 % CI 0.885-0.981) in the test set, which was also better than the radiologist readers (accuracy 77.8-89.9 %, AUC 0.791-0.903). Conclusions: DL-based automated AVC quantification may be comparable with manual measurements. The diagnostic performance of the DL-measured AVC volume and Agatston score for classification of severe AVC outperforms radiologist readers.
Full Text
https://www.sciencedirect.com/science/article/pii/S0720048X21000620
DOI
10.1016/j.ejrad.2021.109582
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Radiology (영상의학교실) > 1. Journal Papers
Yonsei Authors
Kim, Hwiyoung(김휘영)
Suh, Young Joo(서영주) ORCID logo https://orcid.org/0000-0002-2078-5832
Choi, Byoung Wook(최병욱) ORCID logo https://orcid.org/0000-0002-8873-5444
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/182187
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