Artificial Intelligence System for Assessing Image Quality of Fundus Images and Its Effects on Diagnosis
1 other identifier
observational
300
1 country
1
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.
Trial Health
Trial Health Score
Automated assessment based on enrollment pace, timeline, and geographic reach
participants targeted
Target at P75+ for all trials
Started Feb 2020
Shorter than P25 for all trials
1 active site
Health score is calculated from publicly available data and should be used for screening purposes only.
Trial Relationships
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Study Timeline
Key milestones and dates
Study Start
First participant enrolled
February 1, 2020
CompletedFirst Submitted
Initial submission to the registry
February 26, 2020
CompletedFirst Posted
Study publicly available on registry
February 28, 2020
CompletedPrimary Completion
Last participant's last visit for primary outcome
July 1, 2020
CompletedStudy Completion
Last participant's last visit for all outcomes
July 1, 2020
CompletedFebruary 28, 2020
February 1, 2020
5 months
February 26, 2020
February 26, 2020
Conditions
Outcome Measures
Primary Outcomes (1)
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 Outcomes (1)
The comparison of the performance for previous artificial intelligence diagnostic system with fundus images of different image quality
3 months
Study Arms (1)
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.
Interventions
The participant only needs to take a fundus image as usual.
Eligibility Criteria
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.
You may qualify if:
- Patients should be aware of the contents and signed for the informed consent.
You may not qualify if:
- \. Patients who cannot cooperate with a photographer such as some paralytics, the patients with dementia and severe psychopaths.
- \. Patients who do not agree to sign informed consent.
Contact the study team to confirm eligibility.
Sponsors & Collaborators
Study Sites (1)
Zhongshan Ophthalmic Center, Sun Yat-sen University
Guangzhou, Guangdong, 510060, China
MeSH Terms
Conditions
Condition Hierarchy (Ancestors)
Study Design
- Study Type
- observational
- Observational Model
- COHORT
- Time Perspective
- PROSPECTIVE
- Sponsor Type
- OTHER
- Responsible Party
- PRINCIPAL INVESTIGATOR
- PI Title
- Clinical Professor
Study Record Dates
First Submitted
February 26, 2020
First Posted
February 28, 2020
Study Start
February 1, 2020
Primary Completion
July 1, 2020
Study Completion
July 1, 2020
Last Updated
February 28, 2020
Record last verified: 2020-02
Data Sharing
- IPD Sharing
- Will not share