Evaluation of NeoRetina Artificial Intelligence Algorithm for the Screening of Diabetic Retinopathy at the CHUM
NCT ID: NCT04699864
Last Updated: 2024-09-19
Study Results
The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.
Basic Information
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RECRUITING
NA
630 participants
INTERVENTIONAL
2024-06-10
2026-12-31
Brief Summary
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Detailed Description
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However, the public health care system in Quebec does not actually have the capacity to allow all people with diabetes to see an ophthalmologist within a short delay. Artificial intelligence might help in screening DR and in refering to eye doctors only patients who suffer from this eye disease.
The investigators of this study hypothesize that artificial intelligence (AI) is a useful technology for the screening of diabetic retinopathy (DR) that can detect the absence or the presence of DR with an efficiency and an accuracy similar to that of an ophthalmological evaluation.
The goal of this study is to compare the screening results of DR obtained with NeoRetina pure artificial intelligence algorithm (automated analysis of color photos of the retina) with the results of a routine ophthalmological evaluation done in a clinical context at the Centre hospitalier de l'Université de Montréal (CHUM).
The main objective of this study is to determine if artificial intelligence (AI) could be a useful technology for the early detection and the follow-up of diabetic retinopathy (DR).
The first specific objective is to determine the efficiency and the accuracy of NeoRetina (DIAGNOS Inc.) automated algorithm for the screening and the grading of the severity of diabetic retinopathy (DR) by the analysis of eye fundus images from diabetic patients compared to that of an eye examination done by an ophthalmologist in a clinical context.
The second specific objective is to evaluate if NeoRetina can determine, with efficiency and accuracy, the absence of diabetic retinopathy (DR), the presence of diabetic retinopathy (DR) and the severity of the disease.
Recruited diabetic participants will be screened for DR by AI with NeoRetina. Participants will also have a full eye examination (blind assessment) with an ophthalmologist of the CHUM in order to determine if they suffer from this eye complication of diabetes.
The results of the screening done by AI with NeoRetina will be compared to those of the ocular evaluation done by an ophthalmologist. Ophthalmologists from the CHUM will also revise the retinal images acquired by DIAGNOS (blind assessment) in order to determine if DR is present and will manually grade the severity of the disease.
Conditions
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Study Design
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NA
SINGLE_GROUP
DIAGNOSTIC
NONE
Study Groups
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Diabetic Retinopathy (DR)
Screening of DR with artificial intelligence (NeoRetina algorithm) and diagnostic evaluation with a standard of care ophthalmological examination.
Screening of DR and DME with artificial intelligence using NeoRetina
Macula-centered eye color fundus photos will be acquired by DIAGNOS team using a non-mydriatic digital camera (without pupil dilation). After a numerical treatment, retinal images will be analyzed by NeoRetina artificial intelligence (AI) algorithm in order to find eye lesions characteristics of diabetic retinopathy (DR) and diabetic macular edema (DME). The severity of DR and DME will be graded by NeoRetina according to the ''Early Treatment Diabetic Retinopathy Study'' (ETDRS) international classification standards.
Routine ophthalmological evaluation of DR and DME
Standard of care eye examination (blind assessment) will be performed by an ophthalmologist of the CHUM in order to find lesions characteristics of diabetic retinopathy (DR) and diabetic macular edema (DME). The severity of DR and DME will be graded by the doctor according to the ''Early Treatment Diabetic Retinopathy Study'' (ETDRS) international classification standards.
Manual grading of DR and DME by CHUM ophthalmologists based on retinal photographies acquired by Diagnos
Ophthalmologists of the CHUM will revise the macula-centered eye color photos acquired by DIAGNOS in order to find lesions characteristics of diabetic retinopathy (DR) and diabetic macular edema (DME). The severity of DR and DME will be graded (blind assessment) according to the ''Early Treatment Diabetic Retinopathy Study'' (ETDRS) international classification standards.
Interventions
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Screening of DR and DME with artificial intelligence using NeoRetina
Macula-centered eye color fundus photos will be acquired by DIAGNOS team using a non-mydriatic digital camera (without pupil dilation). After a numerical treatment, retinal images will be analyzed by NeoRetina artificial intelligence (AI) algorithm in order to find eye lesions characteristics of diabetic retinopathy (DR) and diabetic macular edema (DME). The severity of DR and DME will be graded by NeoRetina according to the ''Early Treatment Diabetic Retinopathy Study'' (ETDRS) international classification standards.
Routine ophthalmological evaluation of DR and DME
Standard of care eye examination (blind assessment) will be performed by an ophthalmologist of the CHUM in order to find lesions characteristics of diabetic retinopathy (DR) and diabetic macular edema (DME). The severity of DR and DME will be graded by the doctor according to the ''Early Treatment Diabetic Retinopathy Study'' (ETDRS) international classification standards.
Manual grading of DR and DME by CHUM ophthalmologists based on retinal photographies acquired by Diagnos
Ophthalmologists of the CHUM will revise the macula-centered eye color photos acquired by DIAGNOS in order to find lesions characteristics of diabetic retinopathy (DR) and diabetic macular edema (DME). The severity of DR and DME will be graded (blind assessment) according to the ''Early Treatment Diabetic Retinopathy Study'' (ETDRS) international classification standards.
Eligibility Criteria
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Inclusion Criteria
2. Ability to provide informed consent;
3. Diagnostic for diabetes : 3a) Type 1 diabetes of a lest 5 years of evolution; or 3b) Type 2 diabetes;
4. Diabetic patient followed and refered by a physician of the Centre hospitalier de l'Université de Montréal (CHUM) : 4a) followed by an endocrinologist of the CHUM; or 4b) hospitalized at the CHUM; or 4c) on the waiting list of the Ophthalmology Clinic of the CHUM for the evaluation of DR.
Exclusion Criteria
2. Inability to provide informed consent;
3. Patient who already had a treatment (surgery, laser, injection, etc.) for any retinal condition : Age-related macular degeneration (AMD), retinal vascular occlusion (RVO); etc.
18 Years
ALL
No
Sponsors
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DIAGNOS Inc.
UNKNOWN
Centre hospitalier de l'Université de Montréal (CHUM)
OTHER
Responsible Party
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Principal Investigators
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Karim Hammamji, MD
Role: PRINCIPAL_INVESTIGATOR
Centre hospitalier de l'Université de Montréal (CHUM)
Locations
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Centre hospitalier de l'Université de Montréal
Montreal, Quebec, Canada
Countries
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Central Contacts
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Facility Contacts
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References
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Unité d'évaluation des technologies et des modes d'intervention en santé (UETMIS). Centre hospitalier de l'Université de Montréal. Projet pilote : application de l'intelligence artificielle en ophtalmologie. Revue de la littérature et étude de terrain, phase I. Préparée par Imane Hammana et Alfons Pomp. Février 2020.
Shaban M, Ogur Z, Mahmoud A, Switala A, Shalaby A, Abu Khalifeh H, Ghazal M, Fraiwan L, Giridharan G, Sandhu H, El-Baz AS. A convolutional neural network for the screening and staging of diabetic retinopathy. PLoS One. 2020 Jun 22;15(6):e0233514. doi: 10.1371/journal.pone.0233514. eCollection 2020.
Related Links
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Diabetes Quebec - Understanding Diabetes : Myths and Statistics
DIAGNOS Inc.
Other Identifiers
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20.292
Identifier Type: -
Identifier Source: org_study_id
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