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A Multi-center Study on the Artificial Intelligence Enabled Diabetic Retinopathy Screening Based on Fundus Images

NCT03602989 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 1000

Last updated 2019-05-20

No results posted yet for this study

Summary

Early detection and intervention of diabetic retinopathy (DR) is critical in preventing DR-related vision loss among type 1 (T1DM) and type 2 diabetic mellitus (T2DM) patients, currently estimated at over 100 million in China alone. Yet the healthcare resources, particularly retinal specialists, are in short supply and unevenly distributed. In order to help address this enormous mismatch and implement population-based screening, an artificial intelligence (AI) enabled, cloud based software is developed by training a custom-built convolutional neural network.

This study is designed to evaluate the safety and efficacy of such device in detecting referable diabetic retinopathy (moderate non-proliferative DR or worse).

Conditions

Interventions

DEVICE

AI-enabled Diabetic Retinopathy Screening Software

Color fundus images of both eyes are captured on site before being uploaded to and analyzed by the cloud-based Artificial Intelligence software

Sponsors & Collaborators

  • Zhongshan Ophthalmic Center, Sun Yat-sen University

    collaborator OTHER

  • Peking University People's Hospital

    collaborator OTHER

  • The Eye Hospital of Wenzhou Medical University

    collaborator OTHER

  • Shenzhen SiBright Co., Ltd.

    lead INDUSTRY

Principal Investigators

  • Xiaofeng Lin, M.D. · Zhongshan Ophthalmic Center, Sun Yat-sen University

Eligibility

Min Age
18 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2018-07-05
Primary Completion
2018-11-30
Completion
2019-08-31

Countries

  • China

Study Locations

More Related Trials

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 NCT03602989 on ClinicalTrials.gov