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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RECRUITING
1000 participants
OBSERVATIONAL
2020-07-01
2025-06-30
Brief Summary
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This study intends to collect patient medical record information and traditional/ultra-wide fundus photos and other multi-modal data. Firstly, this study will use artificial intelligence technology to enhance fundus photos of cataract patients to obtain clearer fundus photos. Then this study will use both medical record information and traditional/ultra-wide fundus photographs to predict postoperative vision and visual function of cataract patients.
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Detailed Description
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Conditions
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Study Design
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COHORT
PROSPECTIVE
Eligibility Criteria
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Inclusion Criteria
Exclusion Criteria
18 Years
ALL
No
Sponsors
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Sun Yat-sen University
OTHER
Responsible Party
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Haotian Lin
Clinical Professor
Principal Investigators
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Haotian Lin, M.D., Ph.D.
Role: PRINCIPAL_INVESTIGATOR
Zhongshan Ophthalmic Center, Sun Yat-sen University
Locations
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Zhognshan Ophthalmic Center, Sun Yat-sen University
Guangzhou, Guangdong, China
Countries
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Central Contacts
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Facility Contacts
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References
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Liu L, Hong J, Wu Y, Liu S, Wang K, Li M, Zhao L, Liu Z, Li L, Cui T, Tsui CK, Xu F, Hu W, Yun D, Chen X, Shang Y, Bi S, Wei X, Lai Y, Lin D, Fu Z, Deng Y, Cai K, Xie Y, Cao Z, Wang D, Zhang X, Dongye M, Lin H, Wu X. Digital ray: enhancing cataractous fundus images using style transfer generative adversarial networks to improve retinopathy detection. Br J Ophthalmol. 2024 Sep 20;108(10):1423-1429. doi: 10.1136/bjo-2024-325403.
Other Identifiers
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CCPMOH2021-China-1
Identifier Type: -
Identifier Source: org_study_id
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