A deep learning-based calculation system for plaque stenosis severity on common carotid artery of ultrasound images

Liu, Mengmeng1; Gao, Wenjing1; Song, Di1; Dong, Yinghui1; Hong, Shaofu1; Cui, Chen2,3; Shi, Siyuan2,3; Wu, Kai2,3; Chen, Jiayi2,3; Xu, Jinfeng1; Dong, Fajin1

2024-04-01

10.1177/17085381241246312

VASCULAR   影响因子和分区

1708-5381

1708-539X

Objectives Assessment of plaque stenosis severity allows better management of carotid source of stroke. Our objective is to create a deep learning (DL) model to segment carotid intima-media thickness and plaque and further automatically calculate plaque stenosis severity on common carotid artery (CCA) transverse section ultrasound images.Methods Three hundred and ninety images from 376 individuals were used to train (235/390, 60%), validate (39/390, 10%), and test (116/390, 30%) on a newly proposed CANet model. We also evaluated the model on an external test set of 115 individuals with 122 images acquired from another hospital. Comparative studies were conducted between our CANet model with four state-of-the-art DL models and two experienced sonographers to re-evaluate the present model's performance.Results On the internal test set, our CANet model outperformed the four comparative models with Dice values of 95.22% versus 90.15%, 87.48%, 90.22%, and 91.56% on lumen-intima (LI) borders and 96.27% versus 91.40%, 88.94%, 91.19%, and 92.88% on media-adventitia (MA) borders. On the external test set, our model still produced excellent results with a Dice value of 92.41%. Good consistency of stenosis severity calculation was observed between CANet model and experienced sonographers, with Intraclass Correlation Coefficient (ICC) of 0.927 and 0.702, Pearson's Correlation Coefficient of 0.928 and 0.704 on internal and external test set, respectively.Conclusions Our CANet model achieved excellent performance in the segmentation of carotid IMT and plaques as well as automated calculation of stenosis severity.

Carotid stenosis severity deep learning ultrasound

SCI

英语

第一 ; 通讯

National Key Research and Development Program of China[2022YFC3602400] ; Clinical Scientist Training Program of Shenzhen People's Hospital[SYWGSCGZH202202]

Cardiovascular System & Cardiology

Peripheral Vascular Disease

WOS:001207418800001

SAGE PUBLICATIONS LTD

Web of Science

1.Jinan Univ, Southern Univ Sci & Technol,Clin Coll 2, Shenzhen Peoples Hosp,Affiliated Hosp 1, Dept Ultrasound, 1017 Rd Dongmen North,St Cuizhu, Shenzhen 518020, Peoples R China
2.Illuminate LLC, Shenzhen, Peoples R China
3.Microport Prophecy, Shanghai, Peoples R China

Liu, Mengmeng,Gao, Wenjing,Song, Di,et al. A deep learning-based calculation system for plaque stenosis severity on common carotid artery of ultrasound images[J]. VASCULAR,2024.

Liu, Mengmeng.,Gao, Wenjing.,Song, Di.,Dong, Yinghui.,Hong, Shaofu.,...&Dong, Fajin.(2024).A deep learning-based calculation system for plaque stenosis severity on common carotid artery of ultrasound images.VASCULAR.

Liu, Mengmeng,et al."A deep learning-based calculation system for plaque stenosis severity on common carotid artery of ultrasound images".VASCULAR (2024).