GlaucomaNCT06213896
Detecting Eye Diseases Via Hybrid Deep Learning Algorithms From Fundus Images
An observational study of Glaucoma and Age-Related Macular Degeneration, run by Ural Telekomunikasyon Sanayi Ticaret Anonim Sirketi at 1 site in Turkey (Türkiye).
Trial summary
Eye health is of great importance for quality of life. Some eye diseases can progress and cause permanent damage up to vision loss if they are not treated early. Therefore, it is of great importance to have regular eye examinations and to detect possible eye diseases before they progress. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Glaucoma, Age-Related Macular Degeneration, Retinal Vein Occlusion
- What is being tested?
- Mydriatic Agent, Color Fundus Photography and EyeCheckup v2.0
- Type of treatment
- Medical device, Surgery or procedure
- How many people
- 1,528 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 1 Mar 2023to 18 Apr 2024
- 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 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Primary care clinic invitation to volunteer, Eighteen years of age or older Have been referred to an ophthalmologist for eye examination to screen for eye disease
Who may be able to join
- Must understand the study and sign informed consent. No history of retinal vascular disease, cataracts or any other disease that may affect the appearance of the retina or optic disc (refractive error and ocular surface disease are allowed).
- No history of intraocular surgery or ocular laser treatment for any retinal disease, other than cataract surgery.
- 18 years and over
Who cannot join
- Not understand the study or informed consent, Media opacity or other defect that would prevent taking a fundus photograph with the feature to be evaluated (which could not be taken with a non-mydriatic fundus camera in 6 attempts or was rejected 6 times by the EyeCheckup quality algorithm due to quality), Has intraocular surgery other than cataracts or has had laser treatment on the retina, Contraindicated for imaging with the fundus imaging systems used in the study, Under 18 years
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Screening And Detecting Eye Diseases With Hybrid Deep Learning Algorithms From Fundus Images And Validation Of Automated Artificial Intelligence Algorithm
Eye health is of great importance for quality of life. Some eye diseases can progress and cause permanent damage up to vision loss if they are not treated early. Therefore, it is of great importance to have regular eye examinations and to detect possible eye diseases before they progress. Healthy people should also undergo eye screening once a year, and those with any complaints regarding eye health should be examined.
With the advancing technology, Artificial Intelligence (AI) has begun to play a significant role in the healthcare sector. Retinal diseases, serious health problems resulting from damage to the back part of the eye's retina, include conditions such as retinopathy, macular degeneration, and glaucoma. Artificial intelligence, with its visual recognition and analysis capabilities, holds great potential in the early diagnosis of retinal diseases.
Read more
AI-based diagnosis of retinal diseases typically involves the use of specialized algorithms that analyze retinal images. These algorithms identify abnormal features in the eye, providing doctors with a quick and accurate diagnosis.
EyeCheckup v2.0 will diagnose glaucoma suspicion, severe glaucoma suspicion, age-related macular degeneration diagnosis, RVO diagnosis, diabetic retinopathy diagnosis and stage, presence/absence of DME suspicion and other retinal diseases from fundus images. This study is designed to assess the safety and efficacy of EyeCheckup v2.0.
The study is a single center study to determine the sensitivity and specificity of EyeCheckup to retinal and optic disc diseases. EyeCheckup v2.0 is an automated software device that is designed to analyze ocular fundus digital color photographs taken in frontline primary care settings in order to quickly screen.
Read the detailed description
According to the World Health Organization's worldwide report published in 2020, at least 2.2 billion people worldwide currently have visual impairment, and at least 1 billion of them have a visual impairment that can be prevented or has not yet been addressed. The world faces significant eye health challenges, including inequalities in the coverage and quality of eye care prevention, treatment, and rehabilitation services, a lack of trained eye care providers, and poor integration of eye care services into health systems, among others.
It is known that more than 80% of all visual disorders can be prevented or treated. An eye fundus examination must be performed by a retina specialist to make a correct diagnosis, but people only consult an ophthalmologist when they feel any discomfort. While typically symptoms progress so much that once a disease occurs, resulting in expensive treatments and surgeries, often the damage is irreversible, resulting in visual impairment or even permanent vision loss.
Artificial intelligence is used to study and develop theories and methods that can help simulate and extend human intelligence, which have been used in many fields of research such as automatic diagnosis and medicine. In recent years, the intersection of artificial intelligence (AI) technology and modern medicine has made effective and rapid disease screening possible. EyeCheckup is an automated software device designed to analyze digital color photographs of the ocular fundus to quickly screen for retinal and optic disc diseases.
The main aim of the research is to evaluate the performance of the automatic screening algorithm to detect steerable retinal and optic disc diseases based on color fundus images and to determine its sensitivity and specificity towards possible diseases. For the clinical validation of the system, the images will be evaluated by ophthalmologists and the results will be compared with the artificial intelligence algorithm.
After exclusions, this study will enroll up to 1528 subjects that meet the eligibility criteria. Participants who meet the eligibility criteria will be recruited after obtaining written informed consent from primary health care providers. Subjects will undergo fundus photography per, Food and Drug Administration (FDA) cleared, ophthalmic cameras. Images will be taken according to a specific EyeCheckup imaging protocol provided to the ophthalmic camera operator and then analyzed by the EyeCheckup v2.0 device.
Methods and tools to be used in the research:
- Fundus photo capturing with non-mydriatic cameras: Optic disc-centered and fovea-centered fundus images will be taken with Canon CR-2 AF, Topcon TRC-NW400 and Optomed Aurora Non-mydriatic fundus cameras. For volunteers whose non-mydriatic images cannot be obtained, pupil dilation will be achieved by instilling tropicamide drops, and then images will be taken. Canon CR-2 AF, Topcon TRC-NW400 and Optomed Aurora Non-mydriatic fundus cameras, from which retina images will be taken, are CE marked and FDA approved.
Tests to be done:
- Fundus images obtained with three different cameras from each volunteer included in the study will be analyzed separately for both the right eye and the left eye by the EyeCheckup artificial intelligence algorithm on a camera-based basis. ii. Evaluation of Canon CR-2 AF images by retina and glaucoma specialists for clinical validation of the system and comparison of the results,
- Conditions
- Glaucoma, Age-Related Macular Degeneration, Retinal Vein Occlusion, Diabetic Retinopathy, Diabetic Macular Edema, Eye Diseases
- Being tested
- Mydriatic Agent, Color Fundus Photography, EyeCheckup v2.0
- Keywords
- Artificial Intelligence, Eye Diseases
- Medical subject headings
- Glaucoma, Macular Degeneration, Retinal Vein Occlusion, Diabetic Retinopathy, Eye Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Mydriatic AgentDrug
Also called Tropicamide drops
Subjects will be administered mydriatic medication to dilate their pupils.
Color Fundus PhotographyProcedure or surgery
Subjects will undergo fundus photography before and after administration of mydriatic agent.
EyeCheckup v2.0Device
Screening for existence of eye diseases
What is being measured
The primary outcome is the main question the study is designed to answer.
To determine the accuracy of diagnosis with artificial intelligence algorithm
Comparison of the compatibility of the diagnosis of the artificial intelligence algorithm with the diagnoses of retina and glaucoma specialists
Measured: through study completion, an average of 1 year
Secondary outcomes (1)
To determine the sensitivity and specificity of EyeCheckup v2.0 to detect retinal and optic disc diseases
- To determine the sensitivity of EyeCheckup v2.0 to detect Glaucoma, RVO, diabetic retinopathy, suspected DME, ARMD, other retinal disease (True positive rate of the algorithm)
- To determine the specificity of EyeCheckup v2.0 to detect Glaucoma, RVO, diabetic retinopathy, suspected DME, ARMD, other retinal disease (True negative rate of the algorithm)
- To determine the specificity of EyeCheckup v2.0 to detect Refere/Nonrefere (True negative rate of the algorithm)
- To determine the sensitivity of EyeCheckup v2.0 to detect Refere/Nonrefere (True positive rate of the algorithm)
Measured: through study completion, an average of 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Jan 2024
- Study start1 Mar 2023
- Primary completion18 Apr 2024
- Study completion18 Apr 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for GlaucomaStudies 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
- Case only
- Time perspective
- Prospective
- Enrolment
- 1,528 participants (actual)
Sponsor and investigators
- Lead sponsor
- Ural Telekomunikasyon Sanayi Ticaret Anonim Sirketi
- Sponsor type
- Industry (pharma, biotech, devices)
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06213896
- Sponsor's protocol ID
- EC-2023-TR
- Status last verified
- Jan 2024
- First submitted
- 9 Jan 2024
- Last updated
- 14 May 2024
- Results posted
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 14 May 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Screening And Detecting Eye Diseases With Hybrid Deep Learning Algorithms From Fundus Images And Validation Of Automated Artificial Intelligence Algorithm
- ClinicalTrials.gov ID
- NCT06213896
- Acronym / sponsor ID
- EC-2023-TR
- Conditions
- Glaucoma, Age-Related Macular Degeneration, Retinal Vein Occlusion, Diabetic Retinopathy, Diabetic Macular Edema, Eye Diseases
- Keywords
- Artificial Intelligence, Eye Diseases
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 1,528 participants (actual)
- Lead sponsor
- Ural Telekomunikasyon Sanayi Ticaret Anonim SirketiIndustry (pharma, biotech, devices)
- Study start
- 1 Mar 2023 (actual)
- Primary completion
- 18 Apr 2024 (actual)
- Study completion
- 18 Apr 2024 (actual)
- First posted
- 19 Jan 2024 (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.
Eye health is of great importance for quality of life. Some eye diseases can progress and cause permanent damage up to vision loss if they are not treated early. Therefore, it is of great importance to have regular eye examinations and to detect possible eye diseases before they progress. Healthy people should also undergo eye screening once a year, and those with any complaints regarding eye health should be examined.
With the advancing technology, Artificial Intelligence (AI) has begun to play a significant role in the healthcare sector. Retinal diseases, serious health problems resulting from damage to the back part of the eye's retina, include conditions such as retinopathy, macular degeneration, and glaucoma. Artificial intelligence, with its visual recognition and analysis capabilities, holds great potential in the early diagnosis of retinal diseases.
AI-based diagnosis of retinal diseases typically involves the use of specialized algorithms that analyze retinal images. These algorithms identify abnormal features in the eye, providing doctors with a quick and accurate diagnosis.
EyeCheckup v2.0 will diagnose glaucoma suspicion, severe glaucoma suspicion, age-related macular degeneration diagnosis, RVO diagnosis, diabetic retinopathy diagnosis and stage, presence/absence of DME suspicion and other retinal diseases from fundus images. This study is designed to assess the safety and efficacy of EyeCheckup v2.0.
The study is a single center study to determine the sensitivity and specificity of EyeCheckup to retinal and optic disc diseases. EyeCheckup v2.0 is an automated software device that is designed to analyze ocular fundus digital color photographs taken in frontline primary care settings in order to quickly screen.
Detailed description
According to the World Health Organization's worldwide report published in 2020, at least 2.2 billion people worldwide currently have visual impairment, and at least 1 billion of them have a visual impairment that can be prevented or has not yet been addressed. The world faces significant eye health challenges, including inequalities in the coverage and quality of eye care prevention, treatment, and rehabilitation services, a lack of trained eye care providers, and poor integration of eye care services into health systems, among others.
It is known that more than 80% of all visual disorders can be prevented or treated. An eye fundus examination must be performed by a retina specialist to make a correct diagnosis, but people only consult an ophthalmologist when they feel any discomfort. While typically symptoms progress so much that once a disease occurs, resulting in expensive treatments and surgeries, often the damage is irreversible, resulting in visual impairment or even permanent vision loss.
Artificial intelligence is used to study and develop theories and methods that can help simulate and extend human intelligence, which have been used in many fields of research such as automatic diagnosis and medicine. In recent years, the intersection of artificial intelligence (AI) technology and modern medicine has made effective and rapid disease screening possible. EyeCheckup is an automated software device designed to analyze digital color photographs of the ocular fundus to quickly screen for retinal and optic disc diseases.
The main aim of the research is to evaluate the performance of the automatic screening algorithm to detect steerable retinal and optic disc diseases based on color fundus images and to determine its sensitivity and specificity towards possible diseases. For the clinical validation of the system, the images will be evaluated by ophthalmologists and the results will be compared with the artificial intelligence algorithm.
After exclusions, this study will enroll up to 1528 subjects that meet the eligibility criteria. Participants who meet the eligibility criteria will be recruited after obtaining written informed consent from primary health care providers. Subjects will undergo fundus photography per, Food and Drug Administration (FDA) cleared, ophthalmic cameras. Images will be taken according to a specific EyeCheckup imaging protocol provided to the ophthalmic camera operator and then analyzed by the EyeCheckup v2.0 device.
Methods and tools to be used in the research:
- Fundus photo capturing with non-mydriatic cameras: Optic disc-centered and fovea-centered fundus images will be taken with Canon CR-2 AF, Topcon TRC-NW400 and Optomed Aurora Non-mydriatic fundus cameras. For volunteers whose non-mydriatic images cannot be obtained, pupil dilation will be achieved by instilling tropicamide drops, and then images will be taken. Canon CR-2 AF, Topcon TRC-NW400 and Optomed Aurora Non-mydriatic fundus cameras, from which retina images will be taken, are CE marked and FDA approved.
Tests to be done:
- Fundus images obtained with three different cameras from each volunteer included in the study will be analyzed separately for both the right eye and the left eye by the EyeCheckup artificial intelligence algorithm on a camera-based basis. ii. Evaluation of Canon CR-2 AF images by retina and glaucoma specialists for clinical validation of the system and comparison of the results,
Study design
- Observational model
- Case only
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Primary care clinic invitation to volunteer, Eighteen years of age or older Have been referred to an ophthalmologist for eye examination to screen for eye disease
Key inclusion criteria (3 listed)
- Must understand the study and sign informed consent. No history of retinal vascular disease, cataracts or any other disease that may affect the appearance of the retina or optic disc (refractive error and ocular surface disease are allowed).
- No history of intraocular surgery or ocular laser treatment for any retinal disease, other than cataract surgery.
- 18 years and over
Key exclusion criteria (1 listed)
- Not understand the study or informed consent, Media opacity or other defect that would prevent taking a fundus photograph with the feature to be evaluated (which could not be taken with a non-mydriatic fundus camera in 6 attempts or was rejected 6 times by the EyeCheckup quality algorithm due to quality), Has intraocular surgery other than cataracts or has had laser treatment on the retina, Contraindicated for imaging with the fundus imaging systems used in the study, Under 18 years
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 |
|---|---|---|
| Mydriatic AgentAlso: Tropicamide dropsSubjects will be administered mydriatic medication to dilate their pupils. | Drug | – |
| Color Fundus PhotographySubjects will undergo fundus photography before and after administration of mydriatic agent. | Procedure or surgery | – |
| EyeCheckup v2.0Screening for existence of eye diseases | Device | – |
Primary outcome
To determine the accuracy of diagnosis with artificial intelligence algorithm
Time frame: through study completion, an average of 1 year
Comparison of the compatibility of the diagnosis of the artificial intelligence algorithm with the diagnoses of retina and glaucoma specialists
1 secondary outcome
To determine the sensitivity and specificity of EyeCheckup v2.0 to detect retinal and optic disc diseases
Time frame: through study completion, an average of 1 year
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 14 May 2024.
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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 Glaucoma and Age-Related Macular Degeneration, run by Ural Telekomunikasyon Sanayi Ticaret Anonim Sirketi at 1 site in Turkey (Türkiye).
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 3 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 14 May 2024. 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 Turkey (Türkiye). 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?
To determine the accuracy of diagnosis with artificial intelligence algorithm (time frame: through study completion, an average of 1 year). 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 14 May 2024; AzTrials last refreshed its copy on 30 Oct 2026.