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

Artificial Intelligence Clinical Trial

Official title:

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

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT04289064
• Other study ID #: IMAQUA2020-China-01
• Status: Active, not recruiting
• Start date: February 1, 2020
• Est. completion date: July 1, 2020

Study information

• Verified date: February 2020
• Source: Sun Yat-sen University
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational

Clinical Trial 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.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 300
• Est. completion date: July 1, 2020
• Est. primary completion date: July 1, 2020
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: All
• Age group: N/A and older

Eligibility

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.

Related Conditions & MeSH terms

• Artificial Intelligence
• Retinal Diseases

Intervention

Device:
• Taking a fundus image
The participant only needs to take a fundus image as usual.

Locations

Country	Name	City	State
China	Zhongshan Ophthalmic Center, Sun Yat-sen University	Guangzhou	Guangdong

Sponsors (1)

Lead Sponsor	Collaborator
Sun Yat-sen University

Country where clinical trial is conducted

China, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	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	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