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Intravascular ultrasound-based deep learning for plaque characterization in coronary artery disease

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Abstract
Background and aims: Although plaque characterization by intravascular ultrasound (IVUS) is important for risk stratification, frame-by-frame analysis of a whole vascular segment is time-consuming. The aim was to develop IVUS-based algorithms for classifying attenuation and calcified plaques. Methods: IVUS image sets of 598 coronary arteries from 598 patients were randomized into training and test sets with 5:1 ratio. Each IVUS frame at a 0.4-mm interval was circumferentially labeled as one of three classes: attenuated plaque, calcified plaque, or plaque without attenuation or calcification. The model was trained on multi-class classification with 5-fold cross validation. By converting from Cartesian to polar coordinate images, the class corresponding to each array from 0 to 360? was plotted. Results: At the angle-level, Dice similarity coefficients for identifying calcification vs. attenuation vs. none by using ensemble model were 0.79, 0.74 and 0.99, respectively. Also, the maximal accuracy was 98% to classify those groups in the test set. At the frame-level, the model identified the presence of attenuation with 80% sensitivity, 96% specificity, and 93% overall accuracy, and the presence of calcium with 86% sensitivity, 97% specificity, and 96% overall accuracy. In the per-vessel analysis, the attenuation and calcification burden index closely correlated with human measurements (r = 0.89 and r = 0.95, respectively), as did the maximal attenuation and calcification burden index over 4 mm (r = 0.82 and r = 0.91, respectively). The inference times were 0.05 s per frame and 7.8 s per vessel. Conclusions: Our deep learning algorithms for plaque characterization may assist clinicians in recognizing highrisk coronary lesions.
Author(s)
강도윤강세훈강수진김영학김원장민현석박덕우박성욱박승정안정민이승환이준구이철환이필형조형주
Issued Date
2021
Type
Article
Keyword
딥러닝동맥경화반조직분석혈관내초음파
DOI
10.1016/j.atherosclerosis.2021.03.037
URI
https://oak.ulsan.ac.kr/handle/2021.oak/7042
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_proquest_miscellaneous_2511238087&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Intravascular%20ultrasound-based%20deep%20learning%20for%20plaque%20characterization%20in%20coronary%20artery%20disease&offset=0&pcAvailability=true
Publisher
ATHEROSCLEROSIS
Location
미국
Language
영어
ISSN
0021-9150
Citation Volume
324
Citation Number
0
Citation Start Page
69
Citation End Page
75
Appears in Collections:
Medicine > Medicine
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