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

NCT05230576 · Status: COMPLETED · Type: OBSERVATIONAL · Enrollment: 2000

Last updated 2023-09-14

No results posted yet for this study

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

Interventions

DIAGNOSTIC_TEST

Deep learning training cohort

train the deep learning model

DIAGNOSTIC_TEST

Deep learning validation cohort

evaluate the model

Sponsors & Collaborators

  • Jia Liu

    lead OTHER

Principal Investigators

  • Jia Liu · Third Affiliated Hospital, Sun Yat-Sen University

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2020-05-01
Primary Completion
2023-06-01
Completion
2023-06-01

Countries

  • China

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05230576 on ClinicalTrials.gov