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

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Author(s)
Suyon ChangHwiyoung KimYoung Joo SuhDong Min ChoiHyunghu KimDong Kyu KimJin Young KimJin Young YooByoung Wook Choi
Keimyung Author(s)
Kim, Jin Young
Department
Dept. of Radiology (영상의학)
Journal Title
Eur J Radiol
Issued Date
2021
Volume
137
Keyword
Aortic valve stenosisCalciumComputed tomographyDeep learningAutomated severity scoring of aortic valve calcium
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.
Keimyung Author(s)(Kor)
김진영
Publisher
School of Medicine (의과대학)
Citation
Suyon Chang et al. (2021). Development of a deep learning-based algorithm for the automatic detection and quantification of aortic valve calcium. Eur J Radiol, 137, 109582. doi: 10.1016/j.ejrad.2021.109582
Type
Article
ISSN
1872-7727
Source
https://www.clinicalkey.com/#!/content/playContent/1-s2.0-S0720048X21000620
DOI
10.1016/j.ejrad.2021.109582
URI
https://kumel.medlib.dsmc.or.kr/handle/2015.oak/43599
Appears in Collections:
1. School of Medicine (의과대학) > Dept. of Radiology (영상의학)
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