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Predicting Diabetic Retinopathy From Risk Factor Data and Digital Retinal Images

NCT03694145 · Status: ACTIVE_NOT_RECRUITING · Type: OBSERVATIONAL · Enrollment: 300

Last updated 2025-02-14

No results posted yet for this study

Summary

The objective of this study is to compare the results of a deep learning approach to diabetic retinopathy assessment with results from (1) an in-person examination with an ophthalmologist, and (2) the assessments of optometrists involved in a teleretinal screening program.

Conditions

Interventions

OTHER

In-Person Eye Examination

Dilated in-person eye examination by a board-certified ophthalmologist or retinal fellow.

Sponsors & Collaborators

  • National Library of Medicine (NLM)

    collaborator NIH

  • Los Angeles County Department of Public Health

    collaborator OTHER_GOV

  • University of California, Los Angeles

    collaborator OTHER

  • Charles Drew University of Medicine and Science

    lead OTHER

Principal Investigators

  • Omolola Ogunyemi, PhD · Charles Drew University of Medicine and Science

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2018-10-25
Primary Completion
2025-05-31
Completion
2025-07-31

Countries

  • United States

Study Locations

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Entities

Diseases
Companies

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 NCT03694145 on ClinicalTrials.gov