Retinal DiseasesNCT04289064
Artificial Intelligence System for Assessing Image Quality of Fundus Images and Its Effects on Diagnosis
An observational study of Retinal Diseases and Artificial Intelligence, run by Sun Yat-sen University at 1 site in China.
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. Read more about this study
- Who is it for?
- Any ageHealthy volunteers can take part
- Conditions the study is for
- Retinal Diseases, Artificial Intelligence
- What is being tested?
- Taking a fundus image
- Type of treatment
- Medical device
- How many people
- 300 participants
- Where
- 1 siteChina
- Study dates
- 1 Feb 2020to 1 Jul 2020
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- Healthy volunteers accepted
- Child, Adult, Older adult
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.
Who may be able to join
- Patients should be aware of the contents and signed for the informed consent.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
China1 site
Zhongshan Ophthalmic Center, Sun Yat-sen University
Guangzhou, Guangdong, 510060
About this study
Official title: Artificial Intelligence System for Assessing Image Quality of Fundus Images and Its Effects on Diagnosis: A Clinical Trial
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.
- Conditions
- Retinal Diseases, Artificial Intelligence
- Being tested
- Taking a fundus image
- Medical subject headings
- Retinal Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Taking a fundus imageDevice
The participant only needs to take a fundus image as usual.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Device: Taking a fundus image |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: 3 months
Secondary outcomes (1)
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
Measured: 3 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Feb 2020
- Study start1 Feb 2020
- Primary completion1 Jul 2020 (estimated)
- Study completion1 Jul 2020 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Retinal DiseasesStudies of the same condition, open and under way.
- More trials run by Sun Yat-sen UniversityThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 300 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Sun Yat-sen University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Haotian Lin, Sun Yat-sen University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04289064
- Sponsor's protocol ID
- IMAQUA2020-China-01
- Status last verified
- Feb 2020
- First submitted
- 26 Feb 2020
- Last updated
- 28 Feb 2020
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 28 Feb 2020. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence System for Assessing Image Quality of Fundus Images and Its Effects on Diagnosis: A Clinical Trial
- ClinicalTrials.gov ID
- NCT04289064
- Acronym / sponsor ID
- IMAQUA2020-China-01
- Conditions
- Retinal Diseases, Artificial Intelligence
- Study type
- Observational
- Recruitment status
- Unknown status
- Enrolment
- 300 participants (estimated)
- Lead sponsor
- Sun Yat-sen UniversityUniversity, hospital or other
- Study start
- 1 Feb 2020 (actual)
- Primary completion
- 1 Jul 2020 (estimated)
- Study completion
- 1 Jul 2020 (estimated)
- First posted
- 28 Feb 2020 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Fundus image quality assessment | – | Device: Taking a fundus imageDevice: 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. |
Who may qualify
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child, Adult, Older adult
- 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.
Key inclusion criteria (1 listed)
- Patients should be aware of the contents and signed for the informed consent.
Key exclusion criteria (2 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Taking a fundus imageThe participant only needs to take a fundus image as usual. | Device | Fundus image quality assessment |
Primary outcome
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
1 secondary outcome
The comparison of the performance for previous artificial intelligence diagnostic system with fundus images of different image quality
Time frame: 3 months
Results and safety
Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 28 Feb 2020.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Retinal Diseases and Artificial Intelligence, run by Sun Yat-sen University at 1 site in China.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: status not confirmed, as of the sponsor's last update on 28 Feb 2020. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in China. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Performance of artificial intelligence system for distinguish between good image quality and poor image quality (time frame: 3 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 28 Feb 2020.