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Artificial Intelligence System for Assessing Image Quality of Fundus Images and Its Effects on Diagnosis

NCT04289064 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 300

Last updated 2020-02-28

No results posted yet for this study

Summary

Fundus images are widely used in ophthalmology for the detection of diabetic retinopathy, glaucoma and other diseases. In real-world practice, the quality of fundus images can be unacceptable, which can undermine diagnostic accuracy and efficiency. Here, the researchers established and validated an artificial intelligence system to achieve automatic quality assessment of fundus images upon capture. This system can also provide guidance to photographers according to the reasons for low quality.

Conditions

Interventions

DEVICE

Taking a fundus image

The participant only needs to take a fundus image as usual.

Sponsors & Collaborators

  • Sun Yat-sen University

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2020-02-01
Primary Completion
2020-07-01
Completion
2020-07-01

Countries

  • China

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 NCT04289064 on ClinicalTrials.gov