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

NCT ID: NCT06643338

Last Updated: 2024-10-16

Basic Information

• Recruitment Status: RECRUITING
• Total Enrollment: 1000 participants
• Study Classification: OBSERVATIONAL
• Study Start Date: 2024-09-09
• Study Completion Date: 2027-10-31

Brief 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 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

Study Design

• Observational Model Type: COHORT
• Study Time Perspective: PROSPECTIVE

Eligibility Criteria

Inclusion Criteria

• 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

Exclusion Criteria

• Patient under legal protection
• Pregnant or breast-feeding women

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Fondation Ophtalmologique Adolphe de Rothschild

NETWORK

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Locations

Hôpital Fondation Adolphe de Rothschild

Paris, , France

Site Status: RECRUITING

Countries

France

Central Contacts

Amelie Yavchitz, Dr

Role: CONTACT

ayavchitz@for.paris

33 148036454

Facility Contacts

Yavchitz

Role: primary

ayavchitz@for.paris

33 1 48 03 64 54

Other Identifiers

DMA_2024_6

Identifier Type: -

Identifier Source: org_study_id