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Deep Learning of Retinal Photographs and Atherosclerotic Cardiovascular Disease

NCT04749927 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 2400

Last updated 2021-02-11

No results posted yet for this study

Summary

The research team has developed a deep learning algorithm that predicts anthropometric factors from fundus photographs and an algorithm that predicts cardiovascular disease risk. Fundus photographs are taken for various cardiovascular diseases (myocardial infarction, heart failure, hypertension with target organ damage, high-risk dyslipidemia, diabetic patients, and low-risk hypertension patients), and a deep learning algorithm for predicting developed anthropometric factors will be validated. Fundus photographs will also be taken twice in the first year, and additional fundus photographs will be taken two years later. Major cardiovascular events will be followed up for 5 years to verify the deep learning algorithm predicting cardiovascular disease risk prospectively.

Conditions

Sponsors & Collaborators

  • Yonsei University

    lead OTHER

Principal Investigators

  • Sungha Park · Severance Hospital

Eligibility

Min Age
20 Years
Max Age
79 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2020-10-11
Primary Completion
2029-10-10
Completion
2029-10-10

Countries

  • South Korea

Study Locations

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Entities

Diseases

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 NCT04749927 on ClinicalTrials.gov