Intelligent Detection of Carotid Plaque and Its Stability Based on Deep Learning Dynamic Ultrasound Scanning

September 13, 2023 updated by: Jia Liu

Deep Learning Model Based on Routine Ultrasound Scanning Video to Help Doctors Improve the Diagnosis of Carotid Plaque

This study intends to build a model through deep learning that can automatically and accurately detect plaques, calculate the lumen stenosis rate and evaluate the stability of plaques based on the carotid transverse axis dynamic ultrasound images and contrast-enhanced ultrasound images, so as to comprehensively evaluate the possibility of carotid plaques. cardiovascular risk. The successful development of this study will automatically simulate and reproduce the whole process of carotid plaque assessment by clinical sonographers. Solve the problem of ultrasonic inspection equipment and experience dependence. It is expected to carry out large-scale population intelligent screening, providing new ideas for early prevention and treatment. Especially in medically underdeveloped remote areas and the lack of experienced sonographers, it has great practical value in clinical health care and can bring greater social and economic benefits.

Study Overview

Status

Completed

Conditions

Intervention / Treatment

Detailed Description

Background:

Carotid plaque is harmful to human health. According to estimates by the World Health Organization, 6.7 million cerebrovascular accidents and strokes occur each year, mainly related to the formation of carotid atherosclerotic plaques. On the one hand, carotid artery plaque can cause carotid artery stenosis or even occlusion, causing cerebral ischemia. Early detection and accurate assessment of carotid plaques are helpful for clinicians to take effective intervention measures, which can significantly reduce the disability rate and fatality rate of stroke.

Carotid CTA and MRA can provide relatively high-resolution and high-quality plaque images, but have cost and scanning limitations that limit their application in daily clinical practice. Ultrasonography has the advantages of non-invasiveness, convenience, low cost, and good repeatability. It is the preferred imaging method for plaque detection, stenosis and plaque stability. Contrast-enhanced ultrasonography (CEUS) can sensitively demonstrate intra-plaque microcirculation perfusion by injecting microbubble contrast agents, and is consistent with histopathological findings, and has been increasingly used clinically to evaluate plaque stability.

However, on the one hand, the limitation of ultrasound examination is that it needs to rely on the level of instruments and operators to improve the accuracy. On the other hand, with the growth of the population base and the aging of society, the traditional medical model has been unable to meet the annual increase in the number of patients. examination needs of patients. Therefore, it is of great significance to develop an integrated AI application platform that can automatically and accurately detect plaque based on ultrasound image data, and evaluate lumen stenosis and plaque stability.

Purpose:

This study intends to build a model based on deep learning to automatically and accurately detect plaque based on the carotid transverse axis dynamic ultrasound image, calculate the lumen stenosis rate, and perform stability assessment, so as to comprehensively evaluate the possible cardiovascular effects of carotid plaque. risk. It will realize the automatic simulation and reproduction of the whole process of assessment of cervical plaque by clinical ultrasound experts.

Study design:

Two-thirds of the enrolled patients and their corresponding carotid artery dynamic scan images and expert diagnosis results were randomly selected as the deep learning training cohort. The carotid artery dynamic scan images and expert diagnosis results of the remaining 1/3 patients were used as a validation cohort to evaluate the overall diagnostic accuracy of the deep learning model

Statistical Analysis:

The sensitivity, specificity, positive predictive value, and negative predictive value of deep learning for detecting plaque, estimating luminal stenosis rate, or predicting plaque stability were calculated by the area under the receiver operating characteristic (ROC) curve (AUROC) to evaluate. Statistical analysis was performed using SPSS 22.0 software.

Quality Control:

Develop standardized and standard carotid ultrasound examination methods and operating procedures, and develop unified image acquisition and storage standards. All operators are rigorously trained in carotid ultrasonography. Two operators with more than 5 years of experience in ultrasound operation were hired as quality control personnel to review all images and exclude unqualified images.

Ultrasound is safe and radiation-free. During the examination, the doctor and the patient were always in a state of communication, and the patient felt less nervous and fearful, with good tolerance and high compliance.

Ethics of the study:

This research will follow the ethical guidelines of the Declaration of Helsinki of the World Medical Congress and the relevant norms and regulations of clinical research. The study will begin after the approval of the ethics committee. Before the start of the study, the investigator should inform the subjects of all relevant contents of the clinical study in easy-to-understand language, and inform the patients that they have the right to withdraw from the study at any time. The study was started only after the patients signed the informed consent voluntarily.

Study Type

Observational

Enrollment (Actual)

2000

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • China
    • Guangdong
      • Guangzhou, Guangdong, China
        • The Third Affiliated Hospital of Sun Yat-Sen University

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years and older (Adult, Older Adult)

Accepts Healthy Volunteers

No

Sampling Method

Probability Sample

Study Population

Age≥18 years old, gender is not limited, with varying degrees of carotid atherosclerosis.

Description

Inclusion Criteria:

  • (1) Age≥18 years old, gender is not limited. (2) Patients who voluntarily participated in this study signed the informed consent.

Exclusion Criteria:

  • (1) Severe cerebrovascular disease, uncooperative patients, and those who cannot tolerate examination. (2) Wound dressings after neck surgery affects carotid artery ultrasonography. (3) The neck is short and thick, and the probe cannot be put down vertically.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Observational Models: Cohort
  • Time Perspectives: Prospective

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Deep learning training cohort
2/3 of the enrolled patients and their corresponding carotid artery dynamic scan images and expert diagnosis results were randomly selected as the training cohort for deep learning.
Diagnostic test: Deep learning training cohort
train the deep learning model
Deep learning validation cohort
The carotid artery dynamic scan images and expert diagnosis results of the remaining 1/3 patients were used as a validation cohort to evaluate the overall diagnostic accuracy of the deep learning model.
Diagnostic test: Deep learning validation cohort
evaluate the model

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
AI assists junior radiologists to read images, and primary physicians read images independently
Time Frame: through study completion, an average of 2 years
Taking the reading results of senior sonographers as the gold standard, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of AI-assisted reading and independent reading by junior physicians for carotid plaque-assisted diagnosis were tested. AUC is evaluated.
through study completion, an average of 2 years
Assessing the performance of AI model
Time Frame: through study completion, an average of 2 years
Taking the reading results of senior sonographers as the gold standard, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of AI independent reading. It was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC).
through study completion, an average of 2 years
AI estimates the lumen stenosis rate
Time Frame: through study completion, an average of 2 years
Taking the reading results of senior sonographers as the gold standard, AI can estimate the sensitivity, specificity, accuracy, positive predictive value and negative predictive value of lumen stenosis rate. It was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC).
through study completion, an average of 2 years
AI predicts plaque stability.
Time Frame: through study completion, an average of 2 years
Taking the reading results of senior sonographers as the gold standard, AI predicts the sensitivity, specificity, accuracy, positive predictive value and negative predictive value of plaque stability. It was evaluated by the area under the receiver operating characteristic (ROC) curve (AUC).
through study completion, an average of 2 years
Plaque detection by AI model on videos acquired by different types of equipment.
Time Frame: through study completion, an average of 2 years
Taking the reading results of senior sonographers as the gold standard, AI detects plaque sensitivity, specificity, accuracy, positive predictive value, and negative predictive value on different ultrasound equipment. Assessed by the area under the receiver operating characteristic (ROC) curve (AUC).
through study completion, an average of 2 years

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Jia Liu

Investigators

  • Principal Investigator: Jia Liu, Third Affiliated Hospital, Sun Yat-Sen University

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

May 1, 2020

Primary Completion (Actual)

June 1, 2023

Study Completion (Actual)

June 1, 2023

Study Registration Dates

First Submitted

January 27, 2022

First Submitted That Met QC Criteria

February 8, 2022

First Posted (Actual)

February 9, 2022

Study Record Updates

Last Update Posted (Actual)

September 14, 2023

Last Update Submitted That Met QC Criteria

September 13, 2023

Last Verified

September 1, 2023

More Information

Terms related to this study

Additional Relevant MeSH Terms

Other Study ID Numbers

  • [2020]02-255-01

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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