NCT03973762

Brief Summary

To evaluate the safety and performance of an innovative artificial intelligence based Computer-Aided Diagnosis(CAD) system for diabetic retinography, Retinal images of patients with diabetes mellitus or diabetic retinopathy(DR) were collected retrospectively. All images were graded by a retinal specialists expert panel and the CAD device using the International Clinical Diabetic Retinopathy severity scale criteria. Investigator responsible for DR grading by CAD system is blinded to the DR grading results from the expert panel. Finally, DR grading results of the CAD system and experts were compared using sensitivity and specificity.

Trial Health

87
On Track

Trial Health Score

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

Enrollment
1,081

participants targeted

Target at P75+ for not_applicable

Timeline
Completed

Started May 2019

Geographic Reach
1 country

1 active site

Status
completed

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

First Submitted

Initial submission to the registry

May 27, 2019

Completed
4 days until next milestone

Study Start

First participant enrolled

May 31, 2019

Completed
4 days until next milestone

First Posted

Study publicly available on registry

June 4, 2019

Completed
1.1 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

July 15, 2020

Completed
1 month until next milestone

Study Completion

Last participant's last visit for all outcomes

August 15, 2020

Completed
Last Updated

November 3, 2020

Status Verified

November 1, 2020

Enrollment Period

1.1 years

First QC Date

May 27, 2019

Last Update Submit

November 2, 2020

Conditions

Diabetic Retinopathy

Keywords

Diabetic Retinography, CAD

Outcome Measures

Primary Outcomes (1)

  • Se and Sp under investigation target 3

    1.investigation target 3: Negative: DR grading of 0 or 1; Positive: DR grading of 2 or higher; After completion of DR grading by expert panel and CAD system, results of the CAD system were compared to the results of human grading, which is considered the gold standard, using measures as sensitivity(Se) and specificity(Sp).

    through study completion，an average of four months

Secondary Outcomes (1)

  • Se and Sp under investigation target 1/2/4/5

    through study completion，an average of four months

Study Arms (2)

DR Grading with CAD

EXPERIMENTAL

DR Grading with CAD

Device: DR Grading with CAD

DR Grading by expert panel

OTHER

DR Grading by expert panel

Other: DR Grading by expert panel

Interventions

DR Grading with CADDEVICE

A CAD system is used to make DR grading.

DR Grading with CAD
DR Grading by expert panelOTHER

DR Grading by expert panel

DR Grading by expert panel

Eligibility Criteria

Sexall
Healthy VolunteersNo
Age GroupsChild (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

  • Clinical history of diabetes mellitus or diabetic retinopathy;
  • Fully Gradable Images;
  • around 45° field which covers optic disc and macula;
  • complete patient identification information;

You may not qualify if:

  • incomplete patient identification information

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Peking Union Medical College Hospital

Beijing, Beijing Municipality, 100005, China

Location

Related Publications (3)

  • Xu Y, Wang L, He J, Bi Y, Li M, Wang T, Wang L, Jiang Y, Dai M, Lu J, Xu M, Li Y, Hu N, Li J, Mi S, Chen CS, Li G, Mu Y, Zhao J, Kong L, Chen J, Lai S, Wang W, Zhao W, Ning G; 2010 China Noncommunicable Disease Surveillance Group. Prevalence and control of diabetes in Chinese adults. JAMA. 2013 Sep 4;310(9):948-59. doi: 10.1001/jama.2013.168118.

    PMID: 24002281BACKGROUND

  • Williams GA, Scott IU, Haller JA, Maguire AM, Marcus D, McDonald HR. Single-field fundus photography for diabetic retinopathy screening: a report by the American Academy of Ophthalmology. Ophthalmology. 2004 May;111(5):1055-62. doi: 10.1016/j.ophtha.2004.02.004.

    PMID: 15121388BACKGROUND

  • Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, Venugopalan S, Widner K, Madams T, Cuadros J, Kim R, Raman R, Nelson PC, Mega JL, Webster DR. Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. JAMA. 2016 Dec 13;316(22):2402-2410. doi: 10.1001/jama.2016.17216.

    PMID: 27898976BACKGROUND

MeSH Terms

Conditions

Diabetic Retinopathy

Interventions

Diagnosis, Computer-Assisted

Condition Hierarchy (Ancestors)

Retinal DiseasesEye DiseasesDiabetic AngiopathiesVascular DiseasesCardiovascular DiseasesDiabetes ComplicationsDiabetes MellitusEndocrine System Diseases

Intervention Hierarchy (Ancestors)

Diagnosis

Study Officials

  • Chen Youxin, Professor

    Peking Union Medical College Hospital

    PRINCIPAL INVESTIGATOR

Study Design

Study Type
interventional
Phase
not applicable
Allocation
NON RANDOMIZED
Masking
SINGLE
Who Masked
INVESTIGATOR
Masking Details
Investigator responsible for CAD system operation is masked to the expert panel grading result.
Purpose
DIAGNOSTIC
Intervention Model
PARALLEL
Model Details: This trial aims to evaluate the diagnostic performance of a CAD system for retinal images； And DR grading by clinicians is used as the golden standard.
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Professor

Study Record Dates

First Submitted

May 27, 2019

First Posted

June 4, 2019

Study Start

May 31, 2019

Primary Completion

July 15, 2020

Study Completion

August 15, 2020

Last Updated

November 3, 2020

Record last verified: 2020-11

Data Sharing

IPD Sharing
Will not share

Locations

CN(1)