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

NCT ID: NCT04289064

Last Updated: 2020-02-28

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

300 participants

Study Classification

OBSERVATIONAL

Study Start Date

2020-02-01

Study Completion Date

2020-07-01

Brief Summary

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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.

Detailed Description

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Conditions

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Retinal Diseases Artificial Intelligence

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

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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.

Taking a fundus image

Intervention Type DEVICE

The participant only needs to take a fundus image as usual.

Interventions

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Taking a fundus image

The participant only needs to take a fundus image as usual.

Intervention Type DEVICE

Eligibility Criteria

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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.
Eligible Sex

ALL

Accepts Healthy Volunteers

Yes

Sponsors

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Sun Yat-sen University

OTHER

Sponsor Role lead

Responsible Party

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Haotian Lin

Clinical Professor

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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Zhongshan Ophthalmic Center, Sun Yat-sen University

Guangzhou, Guangdong, China

Site Status

Countries

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China

Other Identifiers

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IMAQUA2020-China-01

Identifier Type: -

Identifier Source: org_study_id