Validation of the Utility of Ophthalmology Intelligent Diagnostic System
NCT ID: NCT03499145
Last Updated: 2019-10-21
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
615 participants
OBSERVATIONAL
2018-04-01
2019-08-31
Brief Summary
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Detailed Description
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Conditions
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Study Design
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COHORT
PROSPECTIVE
Study Groups
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Eligible patients for AI test.
Device: ophthalmology diagnostic system. An artificial intelligence to make comprehensive evaluation and treatment decision of ocular diseases.
Ophthalmology diagnostic system.
An artificial intelligence to make comprehensive evaluation and treatment decision of ocular diseases.
Interventions
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Ophthalmology diagnostic system.
An artificial intelligence to make comprehensive evaluation and treatment decision of ocular diseases.
Eligibility Criteria
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Inclusion Criteria
ALL
Yes
Sponsors
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Ministry of Health, China
OTHER_GOV
Xidian University
OTHER
Sun Yat-sen University
OTHER
Responsible Party
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Haotian Lin
Clinical Professor
Locations
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Zhongshan Ophthalmic Center, Sun Yat-sen University
Guangzhou, Guangdong, China
Countries
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References
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Li W, Yang Y, Zhang K, Long E, He L, Zhang L, Zhu Y, Chen C, Liu Z, Wu X, Yun D, Lv J, Liu Y, Liu X, Lin H. Dense anatomical annotation of slit-lamp images improves the performance of deep learning for the diagnosis of ophthalmic disorders. Nat Biomed Eng. 2020 Aug;4(8):767-777. doi: 10.1038/s41551-020-0577-y. Epub 2020 Jun 22.
Other Identifiers
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CCPMOH2018-China-2
Identifier Type: -
Identifier Source: org_study_id
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