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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 :
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| Deep learning algorithm applied on retrospectively collected color fundus photographs | Other | Deep learning algorithm applied on retrospectively collected color fundus photographs |
| Measure | Description | Time Frame |
|---|---|---|
| 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. | baseline |
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Inclusion Criteria:
Exclusion Criteria:
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Color fundus photographs taken within 30 days from the date of onset
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| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| Hopital Fondation Adolphe de Rothschild | Paris | France | 75019 | France |
| PubMed Identifier | Type | Citation | Retractions |
|---|---|---|---|
| 40576025 | Derived | 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. |
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| ID | Term |
|---|---|
| D009901 | Optic Nerve Diseases |
| D018917 | Optic Neuropathy, Ischemic |
| D009902 | Optic Neuritis |
| D010211 | Papilledema |
| D009896 | Optic Atrophy |
| D001932 | Brain Neoplasms |
| ID | Term |
|---|---|
| D003389 | Cranial Nerve Diseases |
| D009422 | Nervous System Diseases |
| D005128 | Eye Diseases |
| D014652 | Vascular Diseases |
| D002318 | Cardiovascular Diseases |
| D016543 | Central Nervous System Neoplasms |
| D009423 | Nervous System Neoplasms |
| D009371 | Neoplasms by Site |
| D009369 | Neoplasms |
| D001927 | Brain Diseases |
| D002493 | Central Nervous System Diseases |
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