Lan He, E Shen, Zekun Yang, Ying Zhang, Yudong Wang, Weidao Chen, Yitong Wang, Yongming He
{"title":"[Deep Learning-Based Artificial Intelligence Model for Automatic Carotid Plaque Identification].","authors":"Lan He, E Shen, Zekun Yang, Ying Zhang, Yudong Wang, Weidao Chen, Yitong Wang, Yongming He","doi":"10.12455/j.issn.1671-7104.240009","DOIUrl":null,"url":null,"abstract":"<p><p>This study aims at developing a dataset for determining the presence of carotid artery plaques in ultrasound images, composed of 1761 ultrasound images from 1165 participants. A deep learning architecture that combines bilinear convolutional neural networks with residual neural networks, known as the single-input BCNN-ResNet model, was utilized to aid clinical doctors in diagnosing plaques using carotid ultrasound images. Following training, internal validation, and external validation, the model yielded an ROC AUC of 0.99 (95% confidence interval: 0.91 to 0.84) in internal validation and 0.95 (95% confidence interval: 0.96 to 0.94) in external validation, surpassing the ResNet-34 network model, which achieved an AUC of 0.98 (95% confidence interval: 0.99 to 0.95) in internal validation and 0.94 (95% confidence interval: 0.95 to 0.92) in external validation. Consequently, the single-input BCNN-ResNet network model has shown remarkable diagnostic capabilities and offers an innovative solution for the automatic detection of carotid artery plaques.</p>","PeriodicalId":52535,"journal":{"name":"中国医疗器械杂志","volume":"48 4","pages":"361-366"},"PeriodicalIF":0.0000,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"中国医疗器械杂志","FirstCategoryId":"1087","ListUrlMain":"https://doi.org/10.12455/j.issn.1671-7104.240009","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"Medicine","Score":null,"Total":0}
引用次数: 0
Abstract
This study aims at developing a dataset for determining the presence of carotid artery plaques in ultrasound images, composed of 1761 ultrasound images from 1165 participants. A deep learning architecture that combines bilinear convolutional neural networks with residual neural networks, known as the single-input BCNN-ResNet model, was utilized to aid clinical doctors in diagnosing plaques using carotid ultrasound images. Following training, internal validation, and external validation, the model yielded an ROC AUC of 0.99 (95% confidence interval: 0.91 to 0.84) in internal validation and 0.95 (95% confidence interval: 0.96 to 0.94) in external validation, surpassing the ResNet-34 network model, which achieved an AUC of 0.98 (95% confidence interval: 0.99 to 0.95) in internal validation and 0.94 (95% confidence interval: 0.95 to 0.92) in external validation. Consequently, the single-input BCNN-ResNet network model has shown remarkable diagnostic capabilities and offers an innovative solution for the automatic detection of carotid artery plaques.
期刊介绍:
Chinese Journal of Medical Instrumentation mainly reports on the development, progress, research and development, production, clinical application, management, and maintenance of medical devices and biomedical engineering. Its aim is to promote the exchange of information on medical devices and biomedical engineering in China and turn the journal into a high-quality academic journal that leads academic directions and advocates academic debates.