Optic NeuropathyBRAINNCT06390579
Building Research With Artificial Intelligence in Neuro-Ophthalmology
An observational study of Optic Neuropathy and Optic Neuropathy, Ischemic, run by Fondation Ophtalmologique Adolphe de Rothschild at 1 site in France.
Trial summary
The research team, recognized as a world leader in Artificial Intelligence for neuro-ophthalmology, has shown that it is possible to diagnose certain neuro-ophthalmologic or neurologic disorders from a single retinal fundus image (Milea et al, New England Journal of Medicine, 2020). Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Optic Neuropathy, Optic Neuropathy, Ischemic, Optic Neuritis
- What is being tested?
- Deep learning algorithm applied on retrospectively collected color fundus photographs
- How many people
- 693 participants
- Where
- 1 siteFrance
- Study dates
- 1 Oct 2023to 1 Feb 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 Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Study population: Color fundus photographs taken within 30 days from the date of onset
Who may be able to join
- Patients with well-defined neuro-ophthalmologic or neurologic conditions, including different forms of optic neuropathies and various neurodegenerative diseases.
- Patients with a robust reference diagnosis confirmed by clinical experts.
- Patients with available retinal fundus images collected during routine care.
Who cannot join
- Patients without a confirmed diagnosis or unclear clinical classification.
- Patients without retinal fundus images or with images that are completely unreadable.
- Patients whose data cannot be anonymized according to legal and institutional protocols.
Trial locations and contacts
1 site in 1 country.
About this study
The research team, recognized as a world leader in Artificial Intelligence for neuro-ophthalmology, has shown that it is possible to diagnose certain neuro-ophthalmologic or neurologic disorders from a single retinal fundus image (Milea et al, New England Journal of Medicine, 2020). However, clinical practice requires identifying a broader spectrum of diseases (inflammatory, ischemic, hereditary, neurodegenerative) within the same analysis.
The main objective is to develop, through a new algorithm capable of classifying multiple disorders from a smaller set of conventional retinal images.
Read more
This project meets a significant public health need: the global shortage of neuro-ophthalmologists. It aims to provide healthcare professionals with a rapid triage tool to detect serious and treatable conditions, enabling timely intervention.
The study will include patients with clearly defined neuro-ophthalmologic or neurologic conditions, confirmed diagnoses, and retinal imaging. Clinical, paraclinical, and imaging data collected during standard care will be used, with strict anonymization according to legal and institutional requirements.
Specific Objectives :
- Evaluate the performance of a diagnostic classification algorithm trained on retinal images.
- Assess the ability to detect multiple pathologies from a single retinal image.
- Support the development of advanced computer vision tools for medical diagnostics.
- Conditions
- Optic Neuropathy, Optic Neuropathy, Ischemic, Optic Neuritis, Optic Nerve Diseases, Papilledema, Optic Atrophy
- Being tested
- Deep learning algorithm applied on retrospectively collected color fundus photographs
- Keywords
- fundus imaging, automated diagnosis, multi-class classification, clinical decision support
- Medical subject headings
- Optic Nerve Diseases, Optic Neuropathy, Ischemic, Optic Neuritis, Papilledema, Optic Atrophy, Brain Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Deep learning algorithm applied on retrospectively collected color fundus photographsOther
Deep learning algorithm applied on retrospectively collected color fundus photographs
What is being measured
The primary outcome is the main question the study is designed to answer.
Diagnostic performance of the Artificial Intelligence algorithm in detecting multiple neuro-ophthalmologic and neurologic conditions from retinal imaging.
Evaluation of the algorithm's sensitivity, specificity, and area under the receiver operating caracteristics curve for classifying multiple neuro-ophthalmologic and neurologic pathologies using retinal fundus photography and Optical Coherence Tomography images, compared with expert-established reference diagnoses.
Measured: baseline
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted30 Apr 2024
- Study start1 Oct 2023
- Primary completion1 Feb 2024
- Study completion1 Feb 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Optic NeuropathyStudies 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
- Retrospective
- Enrolment
- 693 participants (actual)
Sponsor and investigators
- Lead sponsor
- Fondation Ophtalmologique Adolphe de Rothschild
- Sponsor type
- Research network
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06390579
- Sponsor's protocol ID
- CE_20230926_8_DMA_BRAIN
- Acronym
- BRAIN
- Status last verified
- Sep 2025
- First submitted
- 25 Apr 2024
- Last updated
- 8 Sep 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Gungor A, Sarbout I, Gilbert AL, Hamann S, Lebranchu P, Hobeanu C, Gohier P, Vignal-Clermont C, Dumitrascu OM, Cohen SY, Lagreze WA, Feltgen N, van der Heide F, Lamirel C, Jonas JB, Obadia M, Racoceanu D, Milea D. Artificial Intelligence-Based Detection of Central Retinal Artery Occlusion Within 4.5 Hours on Standard Fundus Photographs. J Am Heart Assoc. 2025 Jul;14(13):e041441. doi: 10.1161/JAHA.124.041441. Epub 2025 Jun 27.
PubMed 40576025
Study at a glance
From the registry record, last updated by the sponsor on 8 Sep 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Building Research With Artificial Intelligence in Neuro-Ophthalmology
- ClinicalTrials.gov ID
- NCT06390579
- Acronym / sponsor ID
- BRAIN / CE_20230926_8_DMA_BRAIN
- Conditions
- Optic Neuropathy, Optic Neuropathy, Ischemic, Optic Neuritis, Optic Nerve Diseases, Papilledema, Optic Atrophy
- Keywords
- fundus imaging, automated diagnosis, multi-class classification, clinical decision support
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 693 participants (actual)
- Lead sponsor
- Fondation Ophtalmologique Adolphe de RothschildResearch network
- Study start
- 1 Oct 2023 (actual)
- Primary completion
- 1 Feb 2024 (actual)
- Study completion
- 1 Feb 2024 (actual)
- First posted
- 30 Apr 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.
The research team, recognized as a world leader in Artificial Intelligence for neuro-ophthalmology, has shown that it is possible to diagnose certain neuro-ophthalmologic or neurologic disorders from a single retinal fundus image (Milea et al, New England Journal of Medicine, 2020). However, clinical practice requires identifying a broader spectrum of diseases (inflammatory, ischemic, hereditary, neurodegenerative) within the same analysis.
The main objective is to develop, through a new algorithm capable of classifying multiple disorders from a smaller set of conventional retinal images.
This project meets a significant public health need: the global shortage of neuro-ophthalmologists. It aims to provide healthcare professionals with a rapid triage tool to detect serious and treatable conditions, enabling timely intervention.
The study will include patients with clearly defined neuro-ophthalmologic or neurologic conditions, confirmed diagnoses, and retinal imaging. Clinical, paraclinical, and imaging data collected during standard care will be used, with strict anonymization according to legal and institutional requirements.
Specific Objectives :
- Evaluate the performance of a diagnostic classification algorithm trained on retinal images.
- Assess the ability to detect multiple pathologies from a single retinal image.
- Support the development of advanced computer vision tools for medical diagnostics.
Study design
- Observational model
- Cohort
- Time perspective
- Retrospective
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
- Study population
- Color fundus photographs taken within 30 days from the date of onset
Key inclusion criteria (3 listed)
- Patients with well-defined neuro-ophthalmologic or neurologic conditions, including different forms of optic neuropathies and various neurodegenerative diseases.
- Patients with a robust reference diagnosis confirmed by clinical experts.
- Patients with available retinal fundus images collected during routine care.
Key exclusion criteria (3 listed)
- Patients without a confirmed diagnosis or unclear clinical classification.
- Patients without retinal fundus images or with images that are completely unreadable.
- Patients whose data cannot be anonymized according to legal and institutional protocols.
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 |
|---|---|---|
| Deep learning algorithm applied on retrospectively collected color fundus photographsDeep learning algorithm applied on retrospectively collected color fundus photographs | Other | – |
Primary outcome
Diagnostic performance of the Artificial Intelligence algorithm in detecting multiple neuro-ophthalmologic and neurologic conditions from retinal imaging.
Time frame: baseline
Evaluation of the algorithm's sensitivity, specificity, and area under the receiver operating caracteristics curve for classifying multiple neuro-ophthalmologic and neurologic pathologies using retinal fundus photography and Optical Coherence Tomography images, compared with expert-established reference diagnoses.
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 8 Sep 2025.
- Derived from this study: PubMed 40576025Gungor A, Sarbout I, Gilbert AL, Hamann S, Lebranchu P, Hobeanu C, Gohier P, Vignal-Clermont C, Dumitrascu OM, Cohen SY, Lagreze WA, Feltgen N, van der Heide F, Lamirel C, Jonas JB, Obadia M, Racoceanu D, Milea D. Artificial Intelligence-Based Detection of Central Retinal Artery Occlusion Within 4.5 Hours on Standard Fundus Photographs. J Am Heart Assoc. 2025 Jul;14(13):e041441. doi: 10.1161/JAHA.124.041441. Epub 2025 Jun 27.
- 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 Optic Neuropathy and Optic Neuropathy, Ischemic, 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 3 inclusion and 3 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 8 Sep 2025. 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?
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?
Diagnostic performance of the Artificial Intelligence algorithm in detecting multiple neuro-ophthalmologic and neurologic conditions from retinal imaging. (time frame: baseline). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06390579, which also lists 1 linked publication.
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 Sep 2025.