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

NCT05230576 | Completed

• 1 other identifier: [2020]02-255-01
• Study Type: observational
• Target: 2,000
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Carotid Atherosclerosis

Outcome Measures

Primary Outcomes (5)

• AI assists junior radiologists to read images, and primary physicians read images independently

  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

  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

  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.

  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.

  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

Study Arms (2)

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

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

Interventions

Deep learning training cohort DIAGNOSTIC_TEST

train the deep learning model

Deep learning training cohort

Deep learning validation cohort DIAGNOSTIC_TEST

evaluate the model

Deep learning validation cohort

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Probability Sample

Study Population

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

You may qualify if:

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

You may not qualify if:

• (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.

Sponsors & Collaborators

• Jia Liu: lead

Study Sites (1)

The Third Affiliated Hospital of Sun Yat-sen University

Guangzhou, Guangdong, China

Location

Related Publications (7)

• Abbott AL, Paraskevas KI, Kakkos SK, Golledge J, Eckstein HH, Diaz-Sandoval LJ, Cao L, Fu Q, Wijeratne T, Leung TW, Montero-Baker M, Lee BC, Pircher S, Bosch M, Dennekamp M, Ringleb P. Systematic Review of Guidelines for the Management of Asymptomatic and Symptomatic Carotid Stenosis. Stroke. 2015 Nov;46(11):3288-301. doi: 10.1161/STROKEAHA.115.003390. Epub 2015 Oct 8.

  PMID: 26451020 BACKGROUND

• Nighoghossian N, Derex L, Douek P. The vulnerable carotid artery plaque: current imaging methods and new perspectives. Stroke. 2005 Dec;36(12):2764-72. doi: 10.1161/01.STR.0000190895.51934.43. Epub 2005 Nov 10.

  PMID: 16282537 BACKGROUND

• Saba L, Saam T, Jager HR, Yuan C, Hatsukami TS, Saloner D, Wasserman BA, Bonati LH, Wintermark M. Imaging biomarkers of vulnerable carotid plaques for stroke risk prediction and their potential clinical implications. Lancet Neurol. 2019 Jun;18(6):559-572. doi: 10.1016/S1474-4422(19)30035-3. Epub 2019 Apr 4.

  PMID: 30954372 BACKGROUND

• Rafailidis V, Charitanti A, Tegos T, Destanis E, Chryssogonidis I. Contrast-enhanced ultrasound of the carotid system: a review of the current literature. J Ultrasound. 2017 Feb 9;20(2):97-109. doi: 10.1007/s40477-017-0239-4. eCollection 2017 Jun.

  PMID: 28592999 BACKGROUND

• Deyama J, Nakamura T, Takishima I, Fujioka D, Kawabata K, Obata JE, Watanabe K, Watanabe Y, Saito Y, Mishina H, Kugiyama K. Contrast-enhanced ultrasound imaging of carotid plaque neovascularization is useful for identifying high-risk patients with coronary artery disease. Circ J. 2013;77(6):1499-507. doi: 10.1253/circj.cj-12-1529. Epub 2013 Mar 22.

  PMID: 23519900 BACKGROUND

• Varetto G, Gibello L, Castagno C, Quaglino S, Ripepi M, Benintende E, Gattuso A, Garneri P, Zan S, Capaldi G, Bertoldo U, Rispoli P. Use of Contrast-Enhanced Ultrasound in Carotid Atherosclerotic Disease: Limits and Perspectives. Biomed Res Int. 2015;2015:293163. doi: 10.1155/2015/293163. Epub 2015 Jun 21.

  PMID: 26180793 BACKGROUND

• Staub D, Patel MB, Tibrewala A, Ludden D, Johnson M, Espinosa P, Coll B, Jaeger KA, Feinstein SB. Vasa vasorum and plaque neovascularization on contrast-enhanced carotid ultrasound imaging correlates with cardiovascular disease and past cardiovascular events. Stroke. 2010 Jan;41(1):41-7. doi: 10.1161/STROKEAHA.109.560342. Epub 2009 Nov 12.

  PMID: 19910551 BACKGROUND

MeSH Terms

Conditions

Carotid Artery Diseases

Condition Hierarchy (Ancestors)

Cerebrovascular Disorders; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Vascular Diseases; Cardiovascular Diseases

Study Officials

• Jia Liu

  Third Affiliated Hospital, Sun Yat-Sen University

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR INVESTIGATOR
• PI Title: Attending Physician, Department of Ultrasound, The Third Affiliated Hospital of Sun Yat-sen University

Study Record Dates

• First Submitted: January 27, 2022
• First Posted: February 9, 2022
• Study Start: May 1, 2020
• Primary Completion: June 1, 2023
• Study Completion: June 1, 2023
• Last Updated: September 14, 2023

Record last verified: 2023-09

Data Sharing

• IPD Sharing: Will not share

Locations

CN (1)