Artificial Intelligence System for Assessing Image Quality of Fundus Images and Its Effects on Diagnosis

Artificial Intelligence System for Assessing Image Quality of Fundus Images and Its Effects on Diagnosis: A Clinical Trial

Fundus images are widely used in ophthalmology for the detection of diabetic retinopathy, glaucoma and other diseases. In real-world practice, the quality of fundus images can be unacceptable, which can undermine diagnostic accuracy and efficiency. Here, the researchers established and validated an artificial intelligence system to achieve automatic quality assessment of fundus images upon capture. This system can also provide guidance to photographers according to the reasons for low quality.

Study Overview

• Status: Unknown
• Conditions: Retinal Diseases; Artificial Intelligence
• Intervention / Treatment: Device: Taking a fundus image

• Study Type: Observational
• Enrollment (Anticipated): 300

Contacts and Locations

Study Locations

• China
  • Guangdong
    • Guangzhou, Guangdong, China, 510060
      • Zhongshan Ophthalmic Center, Sun Yat-sen University

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

Inclusion Criteria:

• Patients should be aware of the contents and signed for the informed consent.

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.

Description

Inclusion Criteria:

• Patients should be aware of the contents and signed for the informed consent.

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

1

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Fundus image quality assessment; Device: an artificial intelligence system for quality assessment of fundus images. These patients are enrolled in primary healthcare units or the AI clinic at Zhongshan Ophthalmic Center.	Device: Taking a fundus image; The participant only needs to take a fundus image as usual.

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Performance of artificial intelligence system for distinguish between good image quality and poor image quality	Area under the receiver operating characteristic curves, sensitivity, specificity, positive and negative predictive values, accuracy	3 months

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
The comparison of the performance for previous artificial intelligence diagnostic system with fundus images of different image quality	Cohen's kappa coefficient, P value and other related statistic results	3 months

Collaborators and Investigators

• Sponsor: Sun Yat-sen University

Study record dates

Study Major Dates

• Study Start (Actual): February 1, 2020
• Primary Completion (Anticipated): July 1, 2020
• Study Completion (Anticipated): July 1, 2020

Study Registration Dates

• First Submitted: February 26, 2020
• First Submitted That Met QC Criteria: February 26, 2020
• First Posted (Actual): February 28, 2020

Study Record Updates

• Last Update Posted (Actual): February 28, 2020
• Last Update Submitted That Met QC Criteria: February 26, 2020
• Last Verified: February 1, 2020

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Eye Diseases; Retinal Diseases
• Other Study ID Numbers: IMAQUA2020-China-01

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

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