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

Artificial Intelligence Clinical Trial

Official title:

Real-time Artificial Intelligence System for Detecting Multiple Ocular Fundus Lesions by Ultra-widefield Fundus Imaging: A Prospective Multicenter Study

Clinical Trial Details — Status: Recruiting

Administrative data

• NCT number: NCT04859634
• Other study ID #: UWFAIDS2019-China-06
• Status: Recruiting
• 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
• 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.

Related Conditions & MeSH terms

• Artificial Intelligence
• Congenital Abnormalities
• Diagnostic Imaging

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