Clinical trial NCT06643338: Development of a Deep Learning System for Identification of Neuro-Ophthalmological Conditions on Color Fundus Photographs in Emergency Department
Official title: Development of a Deep Learning System for Identification of Neuro-Ophthalmological Conditions on Color Fundus Photographs in Emergency Department
Study type: OBSERVATIONAL
Overall status: RECRUITING
Conditions: Eye Diseases
Lead sponsor: Fondation Ophtalmologique Adolphe de Rothschild
Sponsor class: NETWORK
First posted: 2024-10-16
Enrollment: 1000
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Adult patients presenting to the emergency department of the Fondation Adolphe de Rothschild hospital with no signs of infection and/or ocular allergy (runny eye, red eye).

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.


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

Primary outcomes:
measure: Proportion of correct predictions among all positive predictions out of all total predictions of the algorithm (positive + negative)
timeFrame: Day 30
Secondary outcomes:
measure: Sensitivity of the algorithm for each eye disease
timeFrame: Day 30
measure: Specificity of the algorithm for each eye disease
timeFrame: Day 30
measure: Area under the curve (AUC) for each eye disease
timeFrame: Day 30
Other outcomes:
Locations:
city: Paris
country: France
facility: Hôpital Fondation Adolphe de Rothschild
Study officials:
References: