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

• Sponsor: Sun Yat-sen University (Other)
• Overall Status: Unknown status
• CT.gov ID: NCT04289064
• Collaborator: (none)
• Enrollment: 300
• Location: 1
• Anticipated Duration (Months): 5
• Patients Per Site Per Month: 60.5

Study Details

Study Description

Brief Summary

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.

Condition or Disease	Intervention/Treatment	Phase
Retinal Diseases; Artificial Intelligence	Device: Taking a fundus image

Study Design

• Study Type: Observational
• Anticipated Enrollment: 300 participants
• Observational Model: Cohort
• Time Perspective: Prospective
• Official Title: Artificial Intelligence System for Assessing Image Quality of Fundus Images and Its Effects on Diagnosis: A Clinical Trial
• Actual Study Start Date: Feb 1, 2020
• Anticipated Primary Completion Date: Jul 1, 2020
• Anticipated Study Completion Date: Jul 1, 2020

Arms and Interventions

Arm	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.

Outcome Measures

Primary Outcome Measures

1. Performance of artificial intelligence system for distinguish between good image quality and poor image quality [3 months]

   Area under the receiver operating characteristic curves, sensitivity, specificity, positive and negative predictive values, accuracy

Secondary Outcome Measures

1. The comparison of the performance for previous artificial intelligence diagnostic system with fundus images of different image quality [3 months]

   Cohen's kappa coefficient, P value and other related statistic results

Eligibility Criteria

Criteria

• Ages Eligible for Study: N/A and Older
• Sexes Eligible for Study: All
• Accepts Healthy Volunteers: Yes

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.
• 1. Patients who do not agree to sign informed consent.

Contacts and Locations

Locations

	Site	City	State	Country	Postal Code
1	Zhongshan Ophthalmic Center, Sun Yat-sen University	Guangzhou	Guangdong	China	510060

Sponsors and Collaborators

• Sun Yat-sen University

Investigators

None specified.

Study Documents (Full-Text)

None provided.

More Information

Publications

None provided.

• Responsible Party: Haotian Lin, Clinical Professor, Sun Yat-sen University
• ClinicalTrials.gov Identifier: NCT04289064
• Other Study ID Numbers: IMAQUA2020-China-01
• First Posted: Feb 28, 2020
• Last Update Posted: Feb 28, 2020
• Last Verified: Feb 1, 2020

• Individual Participant Data (IPD) Sharing Statement: No
• Plan to Share IPD: No
• Studies a U.S. FDA-regulated Drug Product: No
• Studies a U.S. FDA-regulated Device Product: No
• Additional relevant MeSH terms: Retinal Diseases

Study Results

No Results Posted as of Feb 28, 2020