A Big Data-based Cohort Study for Cataract Patients

NCT ID: NCT05491798

Last Updated: 2024-01-05

Study Results

Results pending

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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Recruitment Status

RECRUITING

Total Enrollment

1000 participants

Study Classification

OBSERVATIONAL

Study Start Date

2020-07-01

Study Completion Date

2025-06-30

Brief Summary

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Cataract is an important cause of blindness and visual impairment worldwide. At present, the only effective treatment method is surgery. The visual function of most patients can be significantly improved after surgery, but there are still 5-20% of patients whose visual function cannot be improved after surgery. Previous studies have found that the surgical complications and postoperative visual function of cataract patients are closely related to the condition of the fundus, but the current fundus camera cannot perform clear fundus imaging of cataract patients, and the existing potential visual inspections, such as retinal visual inspection, are also inaccurate. Predict postoperative visual acuity. Therefore, there is an urgent need for a reliable postoperative effect prediction system for cataract patients to provide reference for both ophthalmologists and patients.

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.

Detailed Description

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Conditions

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Cataract Retina Disorder

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Eligibility Criteria

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Inclusion Criteria

* Candidates for cataract surgery (phacoemulsification and intraocular lens implantation) within a week.

Exclusion Criteria

* Unwilling or unable to receive fundus photography
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Sun Yat-sen University

OTHER

Sponsor Role lead

Responsible Party

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Haotian Lin

Clinical Professor

Responsibility Role PRINCIPAL_INVESTIGATOR

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

Site Status RECRUITING

Countries

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China

Central Contacts

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Haotian Lin, M.D., Ph.D.

Role: CONTACT

8613802793086

Facility Contacts

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Haotian Lin, M.D., Ph.D

Role: primary

8613802793086

Lixue Liu, M.D

Role: backup

+86-15602382879

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.

Reference Type DERIVED
PMID: 38839251 (View on PubMed)

Other Identifiers

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CCPMOH2021-China-1

Identifier Type: -

Identifier Source: org_study_id

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