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Clinical Trial Details — Status: Recruiting

Administrative data

NCT number NCT04859634
Other study ID # UWFAIDS2019-China-06
Secondary ID
Status Recruiting
Phase
First received
Last updated
Start date November 1, 2020
Est. completion date December 25, 2022

Study information

Verified date April 2021
Source Sun Yat-sen University
Contact Haotian Lin, MD, PhD
Phone 8613802793086
Email haot.lin@hotmail.com
Is FDA regulated No
Health authority
Study type Observational

Clinical Trial Summary

This prospective multicenter study will evaluate the efficacy of a real-time artificial intelligence system for detecting multiple ocular fundus lesions by ultra-widefield fundus imaging in real-world settings.


Description:

The ocular fundus can show signs of both ocular diseases (e.g., lattice degeneration, retinal detachment and glaucoma) and systemic diseases (e.g., hypertension, diabetes and leukemia). The routine fundus examination is conducive for early detection of these diseases. However, manual conducting fundus examination needs an experienced retina ophthalmologist, and is time-consuming and labor-intensive, which is difficult for its routine implementation on large scale. This study will develop an artificial intelligence system integrating with ultra-widefield fundus imaging to automatically screen for multiple ocular fundus lesions in real time and evaluate its performance in different real-world settings. The efficacy of the system will compare to the final diagnoses of each participant made by experienced ophthalmologists.


Recruitment information / eligibility

Status Recruiting
Enrollment 2000
Est. completion date December 25, 2022
Est. primary completion date February 1, 2022
Accepts healthy volunteers Accepts Healthy Volunteers
Gender All
Age group N/A and older
Eligibility Inclusion Criteria: All the participants who agree to take ultra-widefield fundus images. Exclusion Criteria: 1. Patients who cannot cooperate with a photographer such as some paralytics, the patients with dementia and severe psychopaths. 2. Patients who do not agree to sign informed consent.

Study Design


Intervention

Device:
Taking an ultra-widefield fundus image
The participant only needs to take an ultra-widefield fundus image as usual.

Locations

Country Name City State
China Zhongshan Ophthalmic Center, Sun Yat-sen University Guangzhou Guangdong

Sponsors (7)

Lead Sponsor Collaborator
Sun Yat-sen University Beijing Tongren Hospital, Guangdong Provincial People's Hospital, IKang Physical Examination Center, Shenzhen Eye Hospital, Xudong Ophthalmic Hospital, Yangxi General Hospital People's Hospital

Country where clinical trial is conducted

China, 

Outcome

Type Measure Description Time frame Safety issue
Primary Accuracy Performance of artificial intelligence system for detecting multiple ocular fundus lesions based on ultra-widefield fundus imaging. 8 months
Secondary Sensitivity Performance of artificial intelligence system for detecting multiple ocular fundus lesions based on ultra-widefield fundus imaging. 8 months
Secondary Specificity Performance of artificial intelligence system for detecting multiple ocular fundus lesions based on ultra-widefield fundus imaging. 8 months
Secondary Cohen's kappa coefficient The comparison between the performacne of AI system and ophthalmologists of three degrees of expertise. 8 months
Secondary False-positive rate Features of Misclassification 8 months
Secondary False-negative rate Features of Misclassification 8 months
Secondary Data processing time of AI system Data processing time of AI system. 8 months
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