A Big Data-based Cohort Study for Cataract Patients

NCT05491798 | Unknown DMC

• 1 other identifier: CCPMOH2021-China-1
• Study Type: observational
• Target: 1,000
• Locations: 1 country
• Sites: 1

Brief Summary

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.

Conditions

Cataract; Retina Disorder

Keywords

Cataract; Postoperative prediction; Image enhancement; Retina disorder

Outcome Measures

Primary Outcomes (2)

• Change of best corrected visual acuity

  Change of best corrected visual acuity from baseline to 1 week after surgery

  Baseline and 1 week after surgery

• Accuracy for detection of retinal disorders

  Accuracy for detection of retinal disorders using enhanced fundus images

  1 week after surgery

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

All candidates for cataract surgery (phacoemulsification and intraocular lens implantation) within a week.

You may qualify if:

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

You may not qualify if:

• Unwilling or unable to receive fundus photography

Sponsors & Collaborators

• Sun Yat-sen University: lead

Study Sites (1)

Zhognshan Ophthalmic Center, Sun Yat-sen University

Guangzhou, Guangdong, 510060, China

RECRUITING

Related Publications (1)

• 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.

  PMID: 38839251 DERIVED

MeSH Terms

Conditions

Cataract; Retinal Diseases

Condition Hierarchy (Ancestors)

Lens Diseases; Eye Diseases

Study Officials

• Haotian Lin, M.D., Ph.D.

  Zhongshan Ophthalmic Center, Sun Yat-sen University

  PRINCIPAL INVESTIGATOR

Central Study Contacts

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

CONTACT

8613802793086; linht5@mail.sysu.edu.cn

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Target Duration: 1 Week
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Clinical Professor

Study Record Dates

• First Submitted: August 7, 2021
• First Posted: August 8, 2022
• Study Start: July 1, 2020
• Primary Completion: December 1, 2024
• Study Completion: June 1, 2025
• Last Updated: January 5, 2024

Record last verified: 2024-01

Locations

CN (1)