He Lan, Shen E, Yang Zekun, Zhang Ying, Wang Yudong, Chen Weidao, Wang Yitong, He Yongming
Department of Ultrasound Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030.
Department of Ultrasound Medicine, Songjiang Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, 201600.
Zhongguo Yi Liao Qi Xie Za Zhi. 2024 Jul 30;48(4):361-366. doi: 10.12455/j.issn.1671-7104.240009.
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.
本研究旨在开发一个用于确定超声图像中颈动脉斑块存在情况的数据集，该数据集由来自1165名参与者的1761张超声图像组成。一种将双线性卷积神经网络与残差神经网络相结合的深度学习架构，即单输入BCNN-ResNet模型，被用于辅助临床医生使用颈动脉超声图像诊断斑块。经过训练、内部验证和外部验证后，该模型在内部验证中的ROC AUC为0.99（95%置信区间：0.91至0.84），在外部验证中的ROC AUC为0.95（95%置信区间：0.96至0.94），超过了ResNet-34网络模型，后者在内部验证中的AUC为0.98（95%置信区间：0.99至0.95），在外部验证中的AUC为0.94（95%置信区间：0.95至0.92）。因此，单输入BCNN-ResNet网络模型显示出卓越的诊断能力，并为颈动脉斑块的自动检测提供了一种创新解决方案。
