Real-world Diagnostic Effectiveness of Artificial Intelligence Algorithm in Diabetic Retinopathy Screening
NCT ID: NCT03911323
Last Updated: 2019-04-11
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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UNKNOWN
1000 participants
OBSERVATIONAL
2018-10-01
2020-10-01
Brief Summary
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This study is designed to evaluate the clinical efficacy of such an algorithm in detecting referable diabetic retinopathy.
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Detailed Description
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The subjects enrolled in this study are patients with T1DM or T2DM. Qualified color fundus images and seven-field stereoscopic photography images of each eyes of the subject are taken. The fundus images are graded for RDR by the algorithm under test, and seven-field stereoscopic photography images of the same eye are graded by ophthalmologist, which serving as the gold standard to compare the algorithm performance against.
The trial plans to enroll 1000 subjects. With a 95% confidence interval, the sensitivity is expected to be at least 87% whereas the specificity at 89% or above.
The quality of fundus images are assessed according to the National DR Screening Imaging and Grading Guideline published by Chinese Ophthalmological Society and Chinese Medical Doctor Association in 2017.
The grading of RDR is based on the National DR Clinical Diagnosis and Treatment Guideline published by Chinese Ophthalmological Society in 2014.
A brief overview of the clinical protocol is as follows:
Candidate recruiting phase: recruiting qualified participants; Clinical phase: imaging and diagnosing by AI and ophthalmologist ; Statistical analysis phase: comparing two outputs; Closing phase: final report and archiving
Conditions
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Study Design
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CASE_ONLY
PROSPECTIVE
Interventions
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diabetic retinopathy
Patients with diabetes enrolled will undergo nonmydriatic fundus imaging and seven-field stereoscopic photography. The images will be run on an artificial intelligence (AI) algorithm. The diagnosis of the AI algorithm will be compared to the diagnosis of seven-field stereoscopic photography by ophthalmologist. Sensitivity and specificity will be calculated to evaluate the performance of AI algorithm.
Eligibility Criteria
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Inclusion Criteria
2. Age 18 or older, no limitations on gender identity
3. Patients with type 1 or type 2 diabetes.
Exclusion Criteria
2. Subjects diagnosed with macular edema, severe non-proliferative retinopathy, proliferative retinopathy, radioactive retinopathy or retinal vein obstruction.
3. Pregnant woman, subjects with mydriatic allergy, unclear refractive medium, family history of glaucoma, or diagnosed as narrow angle
4. Subjects with a history of laser therapy, retinal surgery or anti-vascular endothelial growth factor injection
5. Subjects currently participating in another ophthalmic research, receiving ophthalmic research products.
6. Subject who is photo-sensitivity or taking medication that causes photosensitivity
7. Subjects received photodynamic therapy recently
18 Years
ALL
No
Sponsors
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Shenzhen Second People's Hospital
OTHER
Responsible Party
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Principal Investigators
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Lisha Mou, PhD
Role: PRINCIPAL_INVESTIGATOR
Shenzhen Second People's Hospital
Locations
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Shenzhen second peoples's hospital
Shenzhen, Guangdong, China
Countries
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Central Contacts
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Facility Contacts
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Other Identifiers
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20190401
Identifier Type: -
Identifier Source: org_study_id
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