A Blinded, Self-control Trial to Evaluate an Artificial Intelligence Based CAD System for Diabetic Retinography
NCT ID: NCT03973762
Last Updated: 2020-11-03
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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COMPLETED
NA
1081 participants
INTERVENTIONAL
2019-05-31
2020-08-15
Brief Summary
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Detailed Description
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Inclusion Criterion:
Clinical history of diabetes mellitus or diabetic retinopathy; Fully Gradable Images; around 45° field which covers optic disc and macula; complete patient identification information;
Exclusion Criterion:
incomplete patient identification information;
2. DR grading by expert panel At first, retinal images are graded by three experts independently, then they met for a consensus meeting to discuss cases without initial agreement. If they can't achieve consensus, a final decision is made by the principal investigator. Experts give a grading of both DR and Diabetic Macular Edema (DME) for each image according to the International Clinical Diabetic Retinopathy severity scale criteria and hard exudates around optic disc.
3. Blinding and DR grading by CAD system Before DR grading by CAD system, a randomized identification(ID) is assigned to each retinal image, which ensures that investigator responsible for CAD system operation is masked to the expert panel grading result. Both DR and DME grading is generated by the CAD system and the results are exported.
4. Unblinding Finally, all data are unblinded and results of the CAD system are compared to the results of human grading, which is considered the gold standard, using measures as sensitivity and specificity;
Conditions
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Study Design
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NON_RANDOMIZED
PARALLEL
DIAGNOSTIC
SINGLE
Study Groups
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DR Grading with CAD
DR Grading with CAD
DR Grading with CAD
A CAD system is used to make DR grading.
DR Grading by expert panel
DR Grading by expert panel
DR Grading by expert panel
DR Grading by expert panel
Interventions
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DR Grading with CAD
A CAD system is used to make DR grading.
DR Grading by expert panel
DR Grading by expert panel
Eligibility Criteria
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Inclusion Criteria
* Fully Gradable Images;
* around 45° field which covers optic disc and macula;
* complete patient identification information;
Exclusion Criteria
ALL
No
Sponsors
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Peking University People's Hospital
OTHER
Beijing Tongren Hospital
OTHER
Chinese PLA General Hospital
OTHER
Peking Union Medical College Hospital
OTHER
Responsible Party
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Youxin Chen
Professor
Principal Investigators
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Chen Youxin, Professor
Role: PRINCIPAL_INVESTIGATOR
Peking Union Medical College Hospital
Locations
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Peking Union Medical College Hospital
Beijing, Beijing Municipality, China
Countries
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References
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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.
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.
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.
Other Identifiers
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GTR201-Clinical
Identifier Type: -
Identifier Source: org_study_id