Eye DiseasesDEEP-VISIONNCT06643338
Development of a Deep Learning System for Identification of Neuro-Ophthalmological Conditions on Color Fundus Photographs in Emergency Department
An observational study of Eye Diseases, run by Fondation Ophtalmologique Adolphe de Rothschild at 1 site in France.
Trial summary
In recent years, artificial intelligence (AI) has been widely integrated into the medical field, contributing in particular to improved patient diagnosis. The BONSAI study, Brain and Optic Nerve Study with AI, in which our team is participating, has successfully demonstrated the ability of AI to identify individual… Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Eye Diseases
- How many people
- 1,000 participants
- Where
- 1 siteFrance
- Study dates
- 9 Sep 2024to Oct 2027
- 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 18 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Adult patients presenting to the emergency department of the Fondation Adolphe de Rothschild hospital with no signs of infection and/or ocular allergy (runny eye, red eye).
Who may be able to join
- Patient aged 18 and over
- Presenting to the emergency department of the Fondation Adolphe de Rothschild hospital
- Express consent to participate in the study
- Member or beneficiary of a social security scheme
Who cannot join
- Patient under legal protection
- Pregnant or breast-feeding women
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
France1 site, 1 recruiting
- Recruiting
Hôpital Fondation Adolphe de Rothschild
Paris, 75019
Yavchitz, 33 1 48 03 64 54, ayavchitz@for.paris
About this study
In recent years, artificial intelligence (AI) has been widely integrated into the medical field, contributing in particular to improved patient diagnosis. The BONSAI study, Brain and Optic Nerve Study with AI, in which our team is participating, has successfully demonstrated the ability of AI to identify individual neuro-ophthalmological or neurological pathologies affecting the optic nerves and/or brain, from a simple fundus image.
While this is a promising advance, it remains limited in current clinical practice. Our major challenge is to be able to identify a wider range of optic nerve and/or brain pathologies simultaneously in the same analysis, so as to improve patient management, especially for those referred to emergency departments. Indeed, in the absence of a precise diagnosis, complications can be irreversible and life-threatening.
Among the most alarming clinical signs in the emergency department is papilledema of stasis, which, accompanied by acute headaches, may indicate the presence of intracranial hypertension, inflammatory or ischemic pathology. The latter may be a manifestation of Horton's disease. Our team has developed an AI algorithm to diagnose retinal and optic nerve abnormalities based on retinophotographs taken under ideal conditions during scheduled consultations, and not on images of patients presenting to the emergency department. In hospitals without ophthalmology emergency departments, it is essential that emergency physicians (emergency physicians, general practitioners, neurologists) are able to assess the fundus in the absence of an ophthalmology specialist. This assessment, although part of the general examination, often presents challenges for non-ophthalmologists. The aim of our study is to improve the performance of our AI algorithm so that it can discriminate between different retinal and optic nerve pathologies in the emergency department. We therefore plan to build a database of fundus images by prospectively including patients presenting to the ophthalmology and neurology emergency departments of the Fondation Adolphe de Rothschild Hospital. The performance of the algorithm developed will be evaluated according to standard criteria of sensitivity, specificity, area under the curve (AUC) and accuracy.
- Conditions
- Eye Diseases
- Medical subject headings
- Eye Diseases
What is being measured
The primary outcome is the main question the study is designed to answer.
Proportion of correct predictions among all positive predictions out of all total predictions of the algorithm (positive + negative)
The gold standard will be the diagnosis made by a senior ophthalmologist on the basis of the patient's medical records consulted at D30 after the emergency visit
Measured: Day 30
Secondary outcomes (3)
Sensitivity of the algorithm for each eye disease
The gold standard will be the diagnosis made by a senior ophthalmologist on the basis of the patient's medical records consulted at D30 after the emergency visit
Measured: Day 30
Specificity of the algorithm for each eye disease
The gold standard will be the diagnosis made by a senior ophthalmologist on the basis of the patient's medical records consulted at D30 after the emergency visit
Measured: Day 30
Area under the curve (AUC) for each eye disease
The gold standard will be the diagnosis made by a senior ophthalmologist on the basis of the patient's medical records consulted at D30 after the emergency visit
Measured: Day 30
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted16 Oct 2024
- Study start9 Sep 2024
- Primary completionOct 2027 (estimated)
- Study completionOct 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Eye DiseasesStudies of the same condition, open and under way.
- More trials run by Fondation Ophtalmologique Adolphe de RothschildThe 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
- 1,000 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Fondation Ophtalmologique Adolphe de Rothschild
- Sponsor type
- Research network
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06643338
- Sponsor's protocol ID
- DMA_2024_6
- Acronym
- DEEP-VISION
- Status last verified
- Oct 2024
- First submitted
- 9 Aug 2024
- Last updated
- 16 Oct 2024
- 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 16 Oct 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development of a Deep Learning System for Identification of Neuro-Ophthalmological Conditions on Color Fundus Photographs in Emergency Department
- ClinicalTrials.gov ID
- NCT06643338
- Acronym / sponsor ID
- DEEP-VISION / DMA_2024_6
- Conditions
- Eye Diseases
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Fondation Ophtalmologique Adolphe de RothschildResearch network
- Study start
- 9 Sep 2024 (actual)
- Primary completion
- Oct 2027 (estimated)
- Study completion
- Oct 2027 (estimated)
- First posted
- 16 Oct 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.
In recent years, artificial intelligence (AI) has been widely integrated into the medical field, contributing in particular to improved patient diagnosis. The BONSAI study, Brain and Optic Nerve Study with AI, in which our team is participating, has successfully demonstrated the ability of AI to identify individual neuro-ophthalmological or neurological pathologies affecting the optic nerves and/or brain, from a simple fundus image.
While this is a promising advance, it remains limited in current clinical practice. Our major challenge is to be able to identify a wider range of optic nerve and/or brain pathologies simultaneously in the same analysis, so as to improve patient management, especially for those referred to emergency departments. Indeed, in the absence of a precise diagnosis, complications can be irreversible and life-threatening.
Among the most alarming clinical signs in the emergency department is papilledema of stasis, which, accompanied by acute headaches, may indicate the presence of intracranial hypertension, inflammatory or ischemic pathology. The latter may be a manifestation of Horton's disease. Our team has developed an AI algorithm to diagnose retinal and optic nerve abnormalities based on retinophotographs taken under ideal conditions during scheduled consultations, and not on images of patients presenting to the emergency department. In hospitals without ophthalmology emergency departments, it is essential that emergency physicians (emergency physicians, general practitioners, neurologists) are able to assess the fundus in the absence of an ophthalmology specialist. This assessment, although part of the general examination, often presents challenges for non-ophthalmologists. The aim of our study is to improve the performance of our AI algorithm so that it can discriminate between different retinal and optic nerve pathologies in the emergency department. We therefore plan to build a database of fundus images by prospectively including patients presenting to the ophthalmology and neurology emergency departments of the Fondation Adolphe de Rothschild Hospital. The performance of the algorithm developed will be evaluated according to standard criteria of sensitivity, specificity, area under the curve (AUC) and accuracy.
Study design
- Observational model
- Cohort
- Time perspective
- Prospective
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Adult patients presenting to the emergency department of the Fondation Adolphe de Rothschild hospital with no signs of infection and/or ocular allergy (runny eye, red eye).
Key inclusion criteria (4 listed)
- Patient aged 18 and over
- Presenting to the emergency department of the Fondation Adolphe de Rothschild hospital
- Express consent to participate in the study
- Member or beneficiary of a social security scheme
Key exclusion criteria (2 listed)
- Patient under legal protection
- Pregnant or breast-feeding women
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Proportion of correct predictions among all positive predictions out of all total predictions of the algorithm (positive + negative)
Time frame: Day 30
The gold standard will be the diagnosis made by a senior ophthalmologist on the basis of the patient's medical records consulted at D30 after the emergency visit
3 secondary outcomes
Sensitivity of the algorithm for each eye disease
Time frame: Day 30
Specificity of the algorithm for each eye disease
Time frame: Day 30
Area under the curve (AUC) for each eye disease
Time frame: Day 30
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 16 Oct 2024.
- 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 Eye Diseases, run by Fondation Ophtalmologique Adolphe de Rothschild at 1 site in France.
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 4 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: looking for participants now, as of the sponsor's last update on 16 Oct 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 France. 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 Amelie Yavchitz, Dr (33 148036454, ayavchitz@for.paris), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Proportion of correct predictions among all positive predictions out of all total predictions of the algorithm (positive + negative) (time frame: Day 30). 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 16 Oct 2024.