Building Research With Artificial Intelligence in Neuro-Ophthalmology

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

COMPLETED

Total Enrollment

693 participants

Study Classification

OBSERVATIONAL

Study Start Date

2023-10-01

Study Completion Date

2024-02-01

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

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. Evaluate the performance of a diagnostic classification algorithm trained on retinal images.
2. Assess the ability to detect multiple pathologies from a single retinal image.
3. Support the development of advanced computer vision tools for medical diagnostics.

Related Clinical Trials

Explore similar clinical trials based on study characteristics and research focus.

Development of a Deep Learning System for Identification of Neuro-Ophthalmological Conditions on Color Fundus Photographs in Emergency Department

NCT06643338

Eye Diseases

RECRUITING

Artificial Intelligence for Detecting Retinal Diseases

NCT04678375

Artificial Intelligence Retinal Diseases

COMPLETED

AI Classifies Multi-Retinal Diseases

NCT04592068

Deep Learning Retinal Diseases

UNKNOWN

High Resolution, High-speed Multimodal Ophthalmic Imaging

NCT04129021

Retinitis Pigmentosa Maculopathy, Age Related Macular Dystrophy +12 more

RECRUITING NA

Real-world of AI in Diagnosing Retinal Diseases

NCT05981950

Artificial Intelligence Retinal Diseases

RECRUITING

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Optic Neuropathy Optic Neuropathy, Ischemic Optic Neuritis Optic Nerve Diseases Papilledema Optic Atrophy Brain Tumors Retinal Photograph Artificial Intelligence (AI) Machine Learning Deep Learning

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Observational Model Type

COHORT

Study Time Perspective

RETROSPECTIVE

Interventions

Learn about the drugs, procedures, or behavioral strategies being tested and how they are applied within this trial.

Deep learning algorithm applied on retrospectively collected color fundus photographs

Intervention Type OTHER

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

Inclusion Criteria

* 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.

Exclusion Criteria

* 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.

Eligible Sex

ALL

Accepts Healthy Volunteers

No