Artificial IntelligenceNCT05981950
Real-world of AI in Diagnosing 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 in real-world settings. The effectiveness and accuracy of this algorithm are evaluated by sensitivity, specificity, positive predictive value, negative predictive value, and area under curve. Read more about this study
- Who is it for?
- Ages 1 year to 100 years
- Conditions the study is for
- Artificial Intelligence, Retinal Diseases
- What is being tested?
- artificial intelligence algorithm
- How many people
- 100,000 participants
- Where
- 1 siteChina
- Study dates
- 1 Aug 2023to 1 Aug 2029
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 1 year to 100 years
- All sexes
- Child, 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. Contact the study team or a site near you to ask about taking part.
China1 site, 1 recruiting
- Recruiting
Wen-Bin Wei
Beijing, Beijing Municipality, 100730
Wen-Bin Wei, MD, 58269516, weiwenbintr@163.com
Wen-Bin Wei, MD
About this study
Official title: Real-world Application of Using Artificial Intelligence in Diagnosing Retinal Diseases
The objective of this study is to apply an artificial intelligence algorithm to diagnose multi-retinal diseases in real-world settings. The effectiveness and accuracy of this algorithm are 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. tic 45-degree fundus cameras, trained operators took binocular fundus photography on participants. Operators were then asked to identify gradable images and unload for algorithm diagnosis. The effectiveness and accuracy of this algorithm are evaluated by sensitivity, specificity, positive predictive value, negative predictive value, area under curve, and F1 score.
- Conditions
- Artificial Intelligence, Retinal Diseases
- Being tested
- 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.
artificial intelligence algorithmDiagnostic test
Retinal diseases diagnosed by artificial intelligence algorithm
| Study group | Role | What they receive |
|---|---|---|
| Retinal diseases diagnosed by artificial intelligence algorithm | 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. Receives: Diagnostic Test: 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 month
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 month
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 month
F1 score
We used F1 score to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.
Measured: 1 month
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted8 Aug 2023
- Study start1 Aug 2023
- Primary completion1 Aug 2028 (estimated)
- Study completion1 Aug 2029 (estimated)
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.
- More trials run by Beijing Tongren HospitalThe 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
- 100,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Beijing Tongren Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Wenbin Wei, Beijing Tongren Hospital
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05981950
- Sponsor's protocol ID
- Real-world RAIDS
- Status last verified
- Aug 2023
- First submitted
- 1 Aug 2023
- Last updated
- 8 Aug 2023
- 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 8 Aug 2023. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Real-world Application of Using Artificial Intelligence in Diagnosing Retinal Diseases
- ClinicalTrials.gov ID
- NCT05981950
- Acronym / sponsor ID
- Real-world RAIDS
- Conditions
- Artificial Intelligence, Retinal Diseases
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 100,000 participants (estimated)
- Lead sponsor
- Beijing Tongren HospitalUniversity, hospital or other
- Study start
- 1 Aug 2023 (actual)
- Primary completion
- 1 Aug 2028 (estimated)
- Study completion
- 1 Aug 2029 (estimated)
- First posted
- 8 Aug 2023 (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 in real-world settings. The effectiveness and accuracy of this algorithm are 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. tic 45-degree fundus cameras, trained operators took binocular fundus photography on participants. Operators were then asked to identify gradable images and unload for algorithm diagnosis. The effectiveness and accuracy of this algorithm are evaluated by sensitivity, specificity, positive predictive value, negative predictive value, area under curve, and F1 score.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Retinal diseases diagnosed by artificial intelligence algorithm | – | Diagnostic Test: 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. |
Who may qualify
- Ages
- 1 year to 100 years
- Sex
- All
- Age groups
- Child, 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 |
|---|---|---|
| artificial intelligence algorithmRetinal diseases diagnosed by artificial intelligence algorithm | Diagnostic test | Retinal diseases diagnosed by artificial intelligence algorithm |
Primary outcome
Area under curve
Time frame: 1 month
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 month
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 month
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 month
We used F1 score to examine the ability of this artificial intelligence algorism recognition and classification of retinal diseases.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Aug 2023.
- 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.
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: looking for participants now, as of the sponsor's last update on 8 Aug 2023. 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Wenbin Wei, MD (58269516, weiwenbintr@163.cim), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Area under curve (time frame: 1 month). 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 8 Aug 2023.