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Development and Validation of a Deep Learning System for Multiple Ocular Fundus Diseases Using Retinal Images

NCT04213430 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 300000

Last updated 2019-12-30

No results posted yet for this study

Summary

Retinal images can reflect both fundus and systemic conditions (diabetes and cardiovascular disease) and firstly to be used for medical artificial intelligence (AI) algorithm training due to its advantages of clinical significance and easy to obtain. Here, the investigators developed a single network model that can mine the characteristics among multiple fundus diseases, which was trained by plenty of fundus images with one or several disease labels (if they have) in each of them. The model performance was compared with those of both native and international ophthalmologists. The model was further tested by datasets with different camera types and validated by three external datasets prospectively collected from the clinical sites where the model would be applied.

Conditions

  • Ophthalmological Disorder

Interventions

OTHER

diagnostic

Training dataset was used to train the deep learning model, which was validated and tested by other two datasets.

Sponsors & Collaborators

  • Sun Yat-sen University

    lead OTHER

Eligibility

Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2014-01-31
Primary Completion
2020-02-29
Completion
2020-05-31

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 NCT04213430 on ClinicalTrials.gov