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
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Study Type
Study Type
Enrollment (Anticipated)
Enrollment
Contacts and Locations
Study Locations
-
-
Guangdong
-
Guangzhou, Guangdong, China, 510060
- Zhongshan Ophthalmic Center, Sun Yat-sen University
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Child
- Adult
- Older Adult
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
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?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / 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.
|
The participant only needs to take a fundus image as usual.
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Performance of artificial intelligence system for distinguish between good image quality and poor image quality
Time Frame: 3 months
|
Area under the receiver operating characteristic curves, sensitivity, specificity, positive and negative predictive values, accuracy
|
3 months
|
Secondary Outcome Measures
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
Time Frame: 3 months
|
Cohen's kappa coefficient, P value and other related statistic results
|
3 months
|
Collaborators and Investigators
Sponsor
Sponsor
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Anticipated)
Primary Completion
Study Completion (Anticipated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Additional Relevant MeSH Terms
Other Study ID Numbers
Other Study ID Numbers
- IMAQUA2020-China-01
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.