NCT03903042

Brief Summary

This study aims to compare the effect of Aurora handheld fundus camera with traditional desktop fundus camera in the fundus photography screening of diabetic patients, and to evaluate the effect of artificial intelligence algorithm in the diagnosis of diabetic retinopathy.

Trial Health

43
At Risk

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Trial has exceeded expected completion date
Enrollment
300

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Nov 2018

Shorter than P25 for all trials

Geographic Reach
1 country

1 active site

Status
unknown

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Start

First participant enrolled

November 1, 2018

Completed
5 months until next milestone

First Submitted

Initial submission to the registry

April 3, 2019

Completed
1 day until next milestone

First Posted

Study publicly available on registry

April 4, 2019

Completed
27 days until next milestone

Primary Completion

Last participant's last visit for primary outcome

May 1, 2019

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

May 1, 2019

Completed
Last Updated

April 4, 2019

Status Verified

April 1, 2019

Enrollment Period

6 months

First QC Date

April 3, 2019

Last Update Submit

April 3, 2019

Conditions

Diabetic Retinopathy

Keywords

Aurora fundus cameraArtificial intelligenceDiabetic retinopathy

Outcome Measures

Primary Outcomes (1)

  • Image Quality of Aurora camera

    Score of Image Quality

    within 3 months

Secondary Outcomes (6)

  • Outcome of gold standard

    within 3 months

  • Image of Aurora camera

    1 month

  • Image of traditional camera (Center 1: Canon)

    1 month

  • Image of traditional camera (Center 2: Zeiss)

    1 month

  • Image of traditional camera (Center 3: Topcon)

    1 month

  • +1 more secondary outcomes

Study Arms (3)

Group 1

Center 1: Traditional camera(Canon) vs Aurora camera

Group 2

Center 2: Traditional camera(Zeiss) vs Aurora camera

Group 3

Center 3: Traditional camera(Topcon) vs Aurora camera

Eligibility Criteria

Age18 Years+
Sexall
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodNon-Probability Sample
Study Population

Patients who were diagnosed with diabetes, more than 18 years of age, male or female Chinese patients.

You may qualify if:

  • Participants are more than 18 years of age, male or female Chinese patients;
  • Diagnosed with diabetes;
  • Prior written informed consent should be obtained

You may not qualify if:

  • Patients with invisible fundus caused by any cause;
  • Patients or his/her licensor unwill to sign an informed consent or follow this protocol;
  • Pregnant women

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine

Shanghai, Shanghai Municipality, 200080, China

Location

Related Publications (6)

  • Jin G, Xiao W, Ding X, Xu X, An L, Congdon N, Zhao J, He M. Prevalence of and Risk Factors for Diabetic Retinopathy in a Rural Chinese Population: The Yangxi Eye Study. Invest Ophthalmol Vis Sci. 2018 Oct 1;59(12):5067-5073. doi: 10.1167/iovs.18-24280.

    PMID: 30357401BACKGROUND

  • Zheng X, Zhang L. A study of retinopathy analysis in type 2 diabetes patients in Chinese population. Pak J Pharm Sci. 2018 Sep;31(5(Supplementary)):2041-2046.

    PMID: 30393210BACKGROUND

  • Hendrick AM, Gibson MV, Kulshreshtha A. Diabetic Retinopathy. Prim Care. 2015 Sep;42(3):451-64. doi: 10.1016/j.pop.2015.05.005.

    PMID: 26319349BACKGROUND

  • Wong TY, Bressler NM. Artificial Intelligence With Deep Learning Technology Looks Into Diabetic Retinopathy Screening. JAMA. 2016 Dec 13;316(22):2366-2367. doi: 10.1001/jama.2016.17563. No abstract available.

    PMID: 27898977BACKGROUND

  • Abramoff MD, Niemeijer M, Russell SR. Automated detection of diabetic retinopathy: barriers to translation into clinical practice. Expert Rev Med Devices. 2010 Mar;7(2):287-96. doi: 10.1586/erd.09.76.

    PMID: 20214432BACKGROUND

  • Li Z, Keel S, Liu C, He Y, Meng W, Scheetz J, Lee PY, Shaw J, Ting D, Wong TY, Taylor H, Chang R, He M. An Automated Grading System for Detection of Vision-Threatening Referable Diabetic Retinopathy on the Basis of Color Fundus Photographs. Diabetes Care. 2018 Dec;41(12):2509-2516. doi: 10.2337/dc18-0147. Epub 2018 Oct 1.

    PMID: 30275284BACKGROUND

MeSH Terms

Conditions

Diabetic Retinopathy

Condition Hierarchy (Ancestors)

Retinal DiseasesEye DiseasesDiabetic AngiopathiesVascular DiseasesCardiovascular DiseasesDiabetes ComplicationsDiabetes MellitusEndocrine System Diseases

Study Officials

  • Fenghua Wang

    Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine

    PRINCIPAL INVESTIGATOR

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
PROSPECTIVE
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Doctoral Investigator

Study Record Dates

First Submitted

April 3, 2019

First Posted

April 4, 2019

Study Start

November 1, 2018

Primary Completion

May 1, 2019

Study Completion

May 1, 2019

Last Updated

April 4, 2019

Record last verified: 2019-04

Data Sharing

IPD Sharing
Will share

Clinical Study Report will be published

Shared Documents
CSR
Time Frame
starting 6 months after publication
Access Criteria
Dr Fenghua Wang will review requests and criteria.

Locations

CN(1)