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A Methodological Approach to Non-invasive Assessments of Vascular Function and Morphology

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[Deep Learning-Based Artificial Intelligence Model for Automatic Carotid Plaque Identification].

Lan He¹, E Shen², Zekun Yang³

¹Department of Ultrasound Medicine, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200030.

Zhongguo Yi Liao Qi Xie Za Zhi = Chinese Journal of Medical Instrumentation

|August 18, 2024

Summary

This summary is machine-generated.

A new deep learning model, single-input BCNN-ResNet, accurately detects carotid artery plaques in ultrasound images. This AI tool aids doctors in diagnosis, showing high performance in internal and external validation studies.

Keywords:

carotid ultrasound deep learning single-input BCNN-ResNet network model

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Published on: June 22, 2010

Area of Science:

Medical Imaging
Artificial Intelligence
Cardiovascular Disease

Context:

Carotid artery plaques are a significant risk factor for stroke.
Accurate detection of these plaques is crucial for timely intervention.
Current diagnostic methods can be time-consuming and subjective.

Purpose:

To develop and validate a deep learning model for automated carotid artery plaque detection.
To create a comprehensive dataset of carotid ultrasound images for training AI models.
To compare the performance of the proposed BCNN-ResNet model against existing deep learning architectures.

Summary:

A dataset of 1761 carotid ultrasound images from 1165 participants was curated.
The single-input BCNN-ResNet model was developed by combining bilinear convolutional neural networks and residual neural networks.
The model achieved an ROC AUC of 0.99 in internal validation and 0.95 in external validation, outperforming ResNet-34.

Impact:

The BCNN-ResNet model demonstrates high diagnostic accuracy for carotid artery plaques.
This AI solution offers a potential tool to assist clinicians in plaque diagnosis.
The developed dataset and model contribute to advancing automated medical image analysis in cardiology.