Detection of coronary lesions in Kawasaki disease by Scaled-YOLOv4 with HarDNet backbone
IntroductionKawasaki disease (KD) may increase the risk of myocardial infarction or sudden death. In children, delayed KD diagnosis and treatment can increase coronary lesions (CLs) incidence by 25% and mortality by approximately 1%. This study focuses on the use of deep learning algorithm-based KD...
Main Authors: | , , , , , , , , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Frontiers Media S.A.
2023-01-01
|
Series: | Frontiers in Cardiovascular Medicine |
Subjects: | |
Online Access: | https://www.frontiersin.org/articles/10.3389/fcvm.2022.1000374/full |
_version_ | 1797946997533048832 |
---|---|
author | Ho-Chang Kuo Shih-Hsin Chen Yi-Hui Chen Yi-Hui Chen Yu-Chi Lin Chih-Yung Chang Yun-Cheng Wu Tzai-Der Wang Ling-Sai Chang I-Hsin Tai I-Hsin Tai Kai-Sheng Hsieh |
author_facet | Ho-Chang Kuo Shih-Hsin Chen Yi-Hui Chen Yi-Hui Chen Yu-Chi Lin Chih-Yung Chang Yun-Cheng Wu Tzai-Der Wang Ling-Sai Chang I-Hsin Tai I-Hsin Tai Kai-Sheng Hsieh |
author_sort | Ho-Chang Kuo |
collection | DOAJ |
description | IntroductionKawasaki disease (KD) may increase the risk of myocardial infarction or sudden death. In children, delayed KD diagnosis and treatment can increase coronary lesions (CLs) incidence by 25% and mortality by approximately 1%. This study focuses on the use of deep learning algorithm-based KD detection from cardiac ultrasound images.MethodsSpecifically, object detection for the identification of coronary artery dilatation and brightness of left and right coronary artery is proposed and different AI algorithms were compared. In infants and young children, a dilated coronary artery is only 1-2 mm in diameter than a normal one, and its ultrasound images demonstrate a large amount of noise background-this can be a considerable challenge for image recognition. This study proposes a framework, named Scaled-YOLOv4-HarDNet, integrating the recent Scaled-YOLOv4 but with the CSPDarkNet backbone replaced by the CSPHarDNet framework.ResultsThe experimental result demonstrated that the mean average precision (mAP) of Scaled-YOLOv4-HarDNet was 72.63%, higher than that of Scaled YOLOv4 and YOLOv5 (70.05% and 69.79% respectively). In addition, it could detect small objects significantly better than Scaled-YOLOv4 and YOLOv5.ConclusionsScaled-YOLOv4-HarDNet may aid physicians in detecting KD and determining the treatment approach. Because relatively few artificial intelligence solutions about images for KD detection have been reported thus far, this paper is expected to make a substantial academic and clinical contribution. |
first_indexed | 2024-04-10T21:20:55Z |
format | Article |
id | doaj.art-9b4b894d1cb94442920c0040744629cf |
institution | Directory Open Access Journal |
issn | 2297-055X |
language | English |
last_indexed | 2024-04-10T21:20:55Z |
publishDate | 2023-01-01 |
publisher | Frontiers Media S.A. |
record_format | Article |
series | Frontiers in Cardiovascular Medicine |
spelling | doaj.art-9b4b894d1cb94442920c0040744629cf2023-01-20T05:56:33ZengFrontiers Media S.A.Frontiers in Cardiovascular Medicine2297-055X2023-01-01910.3389/fcvm.2022.10003741000374Detection of coronary lesions in Kawasaki disease by Scaled-YOLOv4 with HarDNet backboneHo-Chang Kuo0Shih-Hsin Chen1Yi-Hui Chen2Yi-Hui Chen3Yu-Chi Lin4Chih-Yung Chang5Yun-Cheng Wu6Tzai-Der Wang7Ling-Sai Chang8I-Hsin Tai9I-Hsin Tai10Kai-Sheng Hsieh11Department of Pediatrics, Kawasaki Disease Center, Kaohsiung Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Kaohsiung, TaiwanDepartment of Computer Science and Information Engineering, Tamkang University, New Taipei City, TaiwanDepartment of Pediatrics, Kawasaki Disease Center, Kaohsiung Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Kaohsiung, TaiwanDepartment of Information Management, Chang Gung University, Kaohsiung, TaiwanDepartment of Pediatrics, Kawasaki Disease Center, Kaohsiung Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Kaohsiung, TaiwanDepartment of Computer Science and Information Engineering, Tamkang University, New Taipei City, TaiwanDepartment of Computer Science and Information Engineering, Tamkang University, New Taipei City, TaiwanDepartment of E-Sport Technology Management, Cheng Shiu University, Kaohsiung, TaiwanDepartment of Pediatrics, Kawasaki Disease Center, Kaohsiung Chang Gung Memorial Hospital, College of Medicine, Chang Gung University, Kaohsiung, TaiwanDepartment of Medicine, College of Medicine, China Medical University, Taichung, TaiwanDepartment of Pediatric Cardiology, China Medical University Children's Hospital, China Medical University, Taichung, TaiwanCenter of Structure and Congenital Heart Disease/Ultrasound and Department of Cardiology, Children's Hospital, China Medical University, Taichung, TaiwanIntroductionKawasaki disease (KD) may increase the risk of myocardial infarction or sudden death. In children, delayed KD diagnosis and treatment can increase coronary lesions (CLs) incidence by 25% and mortality by approximately 1%. This study focuses on the use of deep learning algorithm-based KD detection from cardiac ultrasound images.MethodsSpecifically, object detection for the identification of coronary artery dilatation and brightness of left and right coronary artery is proposed and different AI algorithms were compared. In infants and young children, a dilated coronary artery is only 1-2 mm in diameter than a normal one, and its ultrasound images demonstrate a large amount of noise background-this can be a considerable challenge for image recognition. This study proposes a framework, named Scaled-YOLOv4-HarDNet, integrating the recent Scaled-YOLOv4 but with the CSPDarkNet backbone replaced by the CSPHarDNet framework.ResultsThe experimental result demonstrated that the mean average precision (mAP) of Scaled-YOLOv4-HarDNet was 72.63%, higher than that of Scaled YOLOv4 and YOLOv5 (70.05% and 69.79% respectively). In addition, it could detect small objects significantly better than Scaled-YOLOv4 and YOLOv5.ConclusionsScaled-YOLOv4-HarDNet may aid physicians in detecting KD and determining the treatment approach. Because relatively few artificial intelligence solutions about images for KD detection have been reported thus far, this paper is expected to make a substantial academic and clinical contribution.https://www.frontiersin.org/articles/10.3389/fcvm.2022.1000374/fullKawasaki diseaseechocardiographydeep learningobject detectionScaled-YOLOv4HarDNet |
spellingShingle | Ho-Chang Kuo Shih-Hsin Chen Yi-Hui Chen Yi-Hui Chen Yu-Chi Lin Chih-Yung Chang Yun-Cheng Wu Tzai-Der Wang Ling-Sai Chang I-Hsin Tai I-Hsin Tai Kai-Sheng Hsieh Detection of coronary lesions in Kawasaki disease by Scaled-YOLOv4 with HarDNet backbone Frontiers in Cardiovascular Medicine Kawasaki disease echocardiography deep learning object detection Scaled-YOLOv4 HarDNet |
title | Detection of coronary lesions in Kawasaki disease by Scaled-YOLOv4 with HarDNet backbone |
title_full | Detection of coronary lesions in Kawasaki disease by Scaled-YOLOv4 with HarDNet backbone |
title_fullStr | Detection of coronary lesions in Kawasaki disease by Scaled-YOLOv4 with HarDNet backbone |
title_full_unstemmed | Detection of coronary lesions in Kawasaki disease by Scaled-YOLOv4 with HarDNet backbone |
title_short | Detection of coronary lesions in Kawasaki disease by Scaled-YOLOv4 with HarDNet backbone |
title_sort | detection of coronary lesions in kawasaki disease by scaled yolov4 with hardnet backbone |
topic | Kawasaki disease echocardiography deep learning object detection Scaled-YOLOv4 HarDNet |
url | https://www.frontiersin.org/articles/10.3389/fcvm.2022.1000374/full |
work_keys_str_mv | AT hochangkuo detectionofcoronarylesionsinkawasakidiseasebyscaledyolov4withhardnetbackbone AT shihhsinchen detectionofcoronarylesionsinkawasakidiseasebyscaledyolov4withhardnetbackbone AT yihuichen detectionofcoronarylesionsinkawasakidiseasebyscaledyolov4withhardnetbackbone AT yihuichen detectionofcoronarylesionsinkawasakidiseasebyscaledyolov4withhardnetbackbone AT yuchilin detectionofcoronarylesionsinkawasakidiseasebyscaledyolov4withhardnetbackbone AT chihyungchang detectionofcoronarylesionsinkawasakidiseasebyscaledyolov4withhardnetbackbone AT yunchengwu detectionofcoronarylesionsinkawasakidiseasebyscaledyolov4withhardnetbackbone AT tzaiderwang detectionofcoronarylesionsinkawasakidiseasebyscaledyolov4withhardnetbackbone AT lingsaichang detectionofcoronarylesionsinkawasakidiseasebyscaledyolov4withhardnetbackbone AT ihsintai detectionofcoronarylesionsinkawasakidiseasebyscaledyolov4withhardnetbackbone AT ihsintai detectionofcoronarylesionsinkawasakidiseasebyscaledyolov4withhardnetbackbone AT kaishenghsieh detectionofcoronarylesionsinkawasakidiseasebyscaledyolov4withhardnetbackbone |