Real-time Artificial Intelligence System for Detecting Multiple Ocular Fundus Lesions by Ultra-widefield Fundus Imaging

• Conditions: Diagnostic Screening Programs; Artificial Intelligence; Diagnostic Imaging; Abnormality of the Fundus

• Registration Number: NCT04859634
• Lead Sponsor: Sun Yat-sen University

Brief 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.

Detailed 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.

Study Timeline

• Study Start: 2020-11-01
• Status Verified: 2021-04
• Study First Submitted: 2021-04-15
• Study First Submitted QC: 2021-04-23
• Last Update Submitted: 2021-04-23
• Study First Posted: 2021-04-26
• Last Update Posted: 2021-04-26
• Primary Completion: 2022-02-01
• Study Completion: 2022-12-25

Recruitment & Eligibility

• Status: UNKNOWN
• Sex: All
• Target Recruitment: 2000

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

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Accuracy	8 months	Performance of artificial intelligence system for detecting multiple ocular fundus lesions based on ultra-widefield fundus imaging.

Secondary Outcome Measures

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

Trial Locations

Locations (1)

Zhongshan Ophthalmic Center, Sun Yat-sen University; 🇨🇳 Guangzhou, Guangdong, China