# Automating Carotid Intima-Media Thickness Video Interpretation with Convolutional Neural Networks

## TL;DR

This paper introduces an automated system using convolutional neural networks to interpret carotid ultrasound videos for cardiovascular risk assessment, significantly improving efficiency and accuracy over existing methods.

## Contribution

The study presents a novel unified CNN-based framework tailored for three key operations in CIMT video interpretation, enabling automation and enhancing clinical applicability.

## Key findings

- System outperforms state-of-the-art methods
- Significant reduction in interpretation time
- Improved accuracy in CIMT measurement

## Abstract

Cardiovascular disease (CVD) is the leading cause of mortality yet largely preventable, but the key to prevention is to identify at-risk individuals before adverse events. For predicting individual CVD risk, carotid intima-media thickness (CIMT), a noninvasive ultrasound method, has proven to be valuable, offering several advantages over CT coronary artery calcium score. However, each CIMT examination includes several ultrasound videos, and interpreting each of these CIMT videos involves three operations: (1) select three end-diastolic ultrasound frames (EUF) in the video, (2) localize a region of interest (ROI) in each selected frame, and (3) trace the lumen-intima interface and the media-adventitia interface in each ROI to measure CIMT. These operations are tedious, laborious, and time consuming, a serious limitation that hinders the widespread utilization of CIMT in clinical practice. To overcome this limitation, this paper presents a new system to automate CIMT video interpretation. Our extensive experiments demonstrate that the suggested system significantly outperforms the state-of-the-art methods. The superior performance is attributable to our unified framework based on convolutional neural networks (CNNs) coupled with our informative image representation and effective post-processing of the CNN outputs, which are uniquely designed for each of the above three operations.