Informations générales (source: ClinicalTrials.gov)
Development of a Deep Learning System for Identification of Neuro-Ophthalmological Conditions on Color Fundus Photographs in Emergency Department (DEEP-VISION)
Observational
Fondation Ophtalmologique Adolphe de Rothschild (Voir sur ClinicalTrials)
septembre 2024
octobre 2027
17 octobre 2024
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.
Etablissements
| Les établissements d'Île-de-France dont les données sont issues de ClinicalTrials.gov Origine et niveau de fiabilité des données | |||||
|---|---|---|---|---|---|
| HOPITAL FONDATION A. DE ROTHSCHILD | Yavchitz | Contact (sur clinicalTrials) | |||
Critères
Tous
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
- 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
- Patient under legal protection
- Pregnant or breast-feeding women