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

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

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.

Study Overview

• Status: Recruiting
• Conditions: Artificial Intelligence; Diagnostic Imaging; Abnormality of the Fundus; Diagnostic Screening Programs
• Intervention / Treatment: Device: Taking an ultra-widefield fundus image

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 Type: Observational
• Enrollment (Anticipated): 2000

Contacts and Locations

• Study Contact: Name: Haotian Lin, MD, PhD; Phone Number: 8613802793086; Email: haot.lin@hotmail.com
• Study Contact Backup: Name: Zhongwen Li, MD; Phone Number: 8618138726682; Email: cuitx3@mail2.sysu.edu.cn

Study Locations

• China
  • Guangdong
    • Guangzhou, Guangdong, China, 510060
      • Recruiting
      • Zhongshan Ophthalmic Center, Sun Yat-sen University
      • Contact: Haotian Lin, M.D., Ph.D; Phone Number: 8613802793086; Email: haot.lin@hotmail.com
      • Contact: Xiaohang Wu, M.D., Ph.D; Phone Number: 8615913177657; Email: wuxiaohang_zoc@qq.com

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Child; Adult; Older Adult
• Accepts Healthy Volunteers: Yes
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

All the participants who agree to take ultra-widefield fundus images.

Description

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 Plan

How is the study designed?

Number of groups / cohorts

7

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Zhongshan Ophthalmic Center; The participant only needs to take an ultra-widefield fundus image as usual.	Device: Taking an ultra-widefield fundus image; The participant only needs to take an ultra-widefield fundus image as usual.
Shenzhen Ophthalmic Center; The participant only needs to take an ultra-widefield fundus image as usual.	Device: Taking an ultra-widefield fundus image; The participant only needs to take an ultra-widefield fundus image as usual.
Beijin Tongren Hospital; The participant only needs to take an ultra-widefield fundus image as usual.	Device: Taking an ultra-widefield fundus image; The participant only needs to take an ultra-widefield fundus image as usual.
Xudong Ophthalmic Center; The participant only needs to take an ultra-widefield fundus image as usual.	Device: Taking an ultra-widefield fundus image; The participant only needs to take an ultra-widefield fundus image as usual.
IKang Physical Examination Center; The participant only needs to take an ultra-widefield fundus image as usual.	Device: Taking an ultra-widefield fundus image; The participant only needs to take an ultra-widefield fundus image as usual.
Yangxi General Hospital People's Hospital; The participant only needs to take an ultra-widefield fundus image as usual.	Device: Taking an ultra-widefield fundus image; The participant only needs to take an ultra-widefield fundus image as usual.
Guangdong Provincial People's Hospital; The participant only needs to take an ultra-widefield fundus image as usual.	Device: Taking an ultra-widefield fundus image; The participant only needs to take an ultra-widefield fundus image as usual.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Accuracy	Performance of artificial intelligence system for detecting multiple ocular fundus lesions based on ultra-widefield fundus imaging.	8 months

Secondary Outcome Measures

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

Collaborators and Investigators

• Sponsor: Sun Yat-sen University
• Collaborators: Beijing Tongren Hospital; Guangdong Provincial People's Hospital; Shenzhen Eye Hospital; Xudong Ophthalmic Hospital; IKang Physical Examination Center; Yangxi General Hospital People's Hospital

Study record dates

Study Major Dates

• Study Start (Actual): November 1, 2020
• Primary Completion (Anticipated): February 1, 2022
• Study Completion (Anticipated): December 25, 2022

Study Registration Dates

• First Submitted: April 15, 2021
• First Submitted That Met QC Criteria: April 23, 2021
• First Posted (Actual): April 26, 2021

Study Record Updates

• Last Update Posted (Actual): April 26, 2021
• Last Update Submitted That Met QC Criteria: April 23, 2021
• Last Verified: April 1, 2021

More Information

Terms related to this study

• Keywords: Artificial Intelligence; Deep learning; Ultra-widefield Fundus Imaging; Ocular Fundus Lesions; Diagnostic Screening Programs
• Additional Relevant MeSH Terms: Congenital Abnormalities
• Other Study ID Numbers: UWFAIDS2019-China-06

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No