Artificial IntelligenceNCT04678375
Artificial Intelligence for Detecting Retinal Diseases
An observational study of Artificial Intelligence and Retinal Diseases, run by Beijing Tongren Hospital at 1 site in China.
Trial summary
The objective of this study is to apply an artificial intelligence algorithm to diagnose multi retinal diseases from fundus photography. The effectiveness and accuracy of this algorithm was evaluated by sensitivity, specificity, positive predictive value, negative predictive value, and area under curve. Read more about this study
- Who is it for?
- Ages 18 to 80 yearsHealthy volunteers can take part
- Conditions the study is for
- Artificial Intelligence, Retinal Diseases
- What is being tested?
- Retinal diseases diagnosed by artificial intelligence algorithm
- How many people
- 1,000,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Jun 2018to 1 Oct 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 18 to 80 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: The study population is derived from an anonymous database that contains health examination results of the general population.
Who may be able to join
- fundus photography around 45° field which covers optic disc and macula
- complete identification information
Who cannot join
- insufficient information for diagnosis.
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Classification of Retinal Diseases by Artificial Intelligence
The objective of this study is to apply an artificial intelligence algorithm to diagnose multi retinal diseases from fundus photography. The effectiveness and accuracy of this algorithm was evaluated by sensitivity, specificity, positive predictive value, negative predictive value, and area under curve.
Read the detailed description
The objective of this study is to apply an artificial intelligence algorithm to diagnose referral diabetes retinopathy, referral age-related macular degeneration, referral possible glaucoma, pathological myopia, retinal vein occlusion, macular hole, macular epiretinal membrane, hypertensive retinopathy, myelinated fibers, retinitis pigmentosa and other retinal lesions from fundus photography. The effectiveness and accuracy of this algorithm was evaluated by sensitivity, specificity, positive predictive value, negative predictive value, area under curve, and F1 score.
- Conditions
- Artificial Intelligence, Retinal Diseases
- Being tested
- Retinal diseases diagnosed by artificial intelligence algorithm
- Medical subject headings
- Retinal Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Retinal diseases diagnosed by artificial intelligence algorithmDiagnostic test
An artificial intelligence algorithm was applied to diagnose referral diabetes retinopathy, referral age-related macular degeneration, referral possible glaucoma, pathological myopia, retinal vein occlusion, macular hole, macular epiretinal membrane, hypertensive retinopathy, myelinated fibers, retinitis pigmentosa and other retinal lesions from fundus photography.
| Study group | Role | What they receive |
|---|---|---|
| Retinal diseases diagnosed by artificial intelligence algorithm | Retinal diseases diagnosed by artificial intelligence algorithm Receives: Diagnostic Test: Retinal diseases diagnosed by artificial intelligence algorithm |
What is being measured
The primary outcome is the main question the study is designed to answer.
Area under curve
We used the receiver operating characteristic (ROC) curve and area under curve to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.
Measured: 1 week
Sensitivity and specificity
We used sensitivity and specificity to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.
Measured: 1 week
Positive predictive value, negative predictive value
We used positive predictive value and negative predictive value to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.
Measured: 1 week
F1 score
We used F1 score to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.
Measured: 1 week
Secondary outcomes (1)
Systemic biomarkers and diseases
Using medical records as the gold standard, we test the accuracy of this artificial intelligence algorism recognition and classification of systemic biomarkers and diseases: age, sex, blood pressure, blood hemoglobin, cardiovascular diseases, thyroid function and kidney function.
Measured: 1 week
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted21 Dec 2020
- Study start1 Jun 2018
- Primary completion30 Jun 2020
- Study completion1 Oct 2020
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Artificial IntelligenceStudies of the same condition, open and under way.
- 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
- Retrospective
- Enrolment
- 1,000,000 participants (actual)
Sponsor and investigators
- Lead sponsor
- Beijing Tongren Hospital
- Sponsor type
- University, hospital or other
- Collaborators
- Beijing Tulip Partner Technology Co., Ltd, China
- Responsible party
- Sponsor
- Investigators
- Wenbin Wei, Study chair, Beijing Tongren Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04678375
- Sponsor's protocol ID
- AI in retinal diseases
- Status last verified
- Jun 2018
- First submitted
- 16 Dec 2020
- Last updated
- 15 Apr 2021
- Results posted
- 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 15 Apr 2021. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Classification of Retinal Diseases by Artificial Intelligence
- ClinicalTrials.gov ID
- NCT04678375
- Acronym / sponsor ID
- AI in retinal diseases
- Conditions
- Artificial Intelligence, Retinal Diseases
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 1,000,000 participants (actual)
- Lead sponsor
- Beijing Tongren HospitalUniversity, hospital or other
- Collaborators
- Beijing Tulip Partner Technology Co., Ltd, China
- Study start
- 1 Jun 2018 (actual)
- Primary completion
- 30 Jun 2020 (actual)
- Study completion
- 1 Oct 2020 (actual)
- First posted
- 21 Dec 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.
The objective of this study is to apply an artificial intelligence algorithm to diagnose multi retinal diseases from fundus photography. The effectiveness and accuracy of this algorithm was evaluated by sensitivity, specificity, positive predictive value, negative predictive value, and area under curve.
Detailed description
The objective of this study is to apply an artificial intelligence algorithm to diagnose referral diabetes retinopathy, referral age-related macular degeneration, referral possible glaucoma, pathological myopia, retinal vein occlusion, macular hole, macular epiretinal membrane, hypertensive retinopathy, myelinated fibers, retinitis pigmentosa and other retinal lesions from fundus photography. The effectiveness and accuracy of this algorithm was evaluated by sensitivity, specificity, positive predictive value, negative predictive value, area under curve, and F1 score.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Retinal diseases diagnosed by artificial intelligence algorithm | – | Diagnostic Test: Retinal diseases diagnosed by artificial intelligence algorithmRetinal diseases diagnosed by artificial intelligence algorithm |
Who may qualify
- Ages
- 18 to 80 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- The study population is derived from an anonymous database that contains health examination results of the general population.
Key inclusion criteria (2 listed)
- fundus photography around 45° field which covers optic disc and macula
- complete identification information
Key exclusion criteria (1 listed)
- insufficient information for diagnosis.
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 |
|---|---|---|
| Retinal diseases diagnosed by artificial intelligence algorithmAn artificial intelligence algorithm was applied to diagnose referral diabetes retinopathy, referral age-related macular degeneration, referral possible glaucoma, pathological myopia, retinal vein occlusion, macular hole, macular epiretinal membrane, hypertensive retinopathy, myelinated fibers, retinitis pigmentosa and other retinal lesions from fundus photography. | Diagnostic test | Retinal diseases diagnosed by artificial intelligence algorithm |
Primary outcome
Area under curve
Time frame: 1 week
We used the receiver operating characteristic (ROC) curve and area under curve to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.
Sensitivity and specificity
Time frame: 1 week
We used sensitivity and specificity to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.
Positive predictive value, negative predictive value
Time frame: 1 week
We used positive predictive value and negative predictive value to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.
F1 score
Time frame: 1 week
We used F1 score to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.
1 secondary outcome
Systemic biomarkers and diseases
Time frame: 1 week
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 Apr 2021.
- Individual participant dataSharing planned: No
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Healthy Volunteer
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.
Wenbin Wei study chair
Beijing Tongren Hospital
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 Artificial Intelligence and Retinal Diseases, run by Beijing Tongren Hospital 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 2 inclusion and 1 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: finished, as of the sponsor's last update on 15 Apr 2021. 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?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
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?
Area under curve (time frame: 1 week). The study lists 4 primary outcomes in all. 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 15 Apr 2021.