Building Research With Artificial Intelligence in Neuro-Ophthalmology

NCT06390579 | Completed

• 1 other identifier: CE_20230926_8_DMA_BRAIN
• Study Type: observational
• Target: 693
• Locations: 1 country
• Sites: 1

Brief 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). 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 :

1. 1.Evaluate the performance of a diagnostic classification algorithm trained on retinal images.
2. 2.Assess the ability to detect multiple pathologies from a single retinal image.
3. 3.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; Brain Tumors; Retinal Photograph; Artificial Intelligence (AI); Machine Learning; Deep Learning

Keywords

fundus imaging; automated diagnosis; multi-class classification; clinical decision support

Outcome Measures

Primary Outcomes (1)

• 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

Interventions

Deep learning algorithm applied on retrospectively collected color fundus photographs OTHER

Deep learning algorithm applied on retrospectively collected color fundus photographs

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Color fundus photographs taken within 30 days from the date of onset

You may qualify if:

• 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.

You may not qualify if:

• 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.

Sponsors & Collaborators

• Fondation Ophtalmologique Adolphe de Rothschild: lead

Study Sites (1)

Hopital Fondation Adolphe de Rothschild

Paris, France, 75019, France

Location

Related Publications (1)

• 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.

  PMID: 40576025 DERIVED

MeSH Terms

Conditions

Optic Nerve Diseases; Optic Neuropathy, Ischemic; Optic Neuritis; Papilledema; Optic Atrophy; Brain Neoplasms

Condition Hierarchy (Ancestors)

Cranial Nerve Diseases; Nervous System Diseases; Eye Diseases; Vascular Diseases; Cardiovascular Diseases; Central Nervous System Neoplasms; Nervous System Neoplasms; Neoplasms by Site; Neoplasms; Brain Diseases; Central Nervous System Diseases

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: RETROSPECTIVE
• Sponsor Type: NETWORK
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: April 25, 2024
• First Posted: April 30, 2024
• Study Start: October 1, 2023
• Primary Completion: February 1, 2024
• Study Completion: February 1, 2024
• Last Updated: September 8, 2025

Record last verified: 2025-09

Data Sharing

• IPD Sharing: Will not share

Locations

FR (1)