Ignite Creation Date:
2024-10-26 @ 3:42 PM
Last Modification Date:
2024-10-26 @ 3:42 PM
Study NCT ID:
NCT06643338
Status:
RECRUITING
Last Update Posted:
None
First Post:
2024-08-09
Brief Title:
Development of a Deep Learning System for Identification of Neuro-Ophthalmological Conditions on Color Fundus Photographs in Emergency Department
Sponsor:
None
Organization:
None
Study Overview
Official Title:
Development of a Deep Learning System for Identification of Neuro-Ophthalmological Conditions on Color Fundus Photographs in Emergency Department
Status:
RECRUITING
Status Verified Date:
2024-10
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
DEEP-VISION
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 andor 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 andor 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 Hortons 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
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 andor 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 Hortons 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
Detailed Description:
None
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None