Evaluation of NeoRetina Artificial Intelligence Algorithm for the Screening of Diabetic Retinopathy at the CHUM (DR-NeoRetina)
Primary Purpose
Diabetic Retinopathy, Diabetic Macular Edema, Diabetic Maculopathy
Status
Not yet recruiting
Phase
Not Applicable
Locations
Study Type
Interventional
Intervention
Screening of DR and DME with artificial intelligence using NeoRetina
Routine ophthalmological evaluation of DR and DME
Manual grading of DR and DME by CHUM ophthalmologists based on retinal photographies acquired by Diagnos
Sponsored by
About this trial
This is an interventional diagnostic trial for Diabetic Retinopathy focused on measuring Diabetes, Type 1 Diabetes, Type 2 Diabetes, Diabetic Retinopathy (DR), Diabetic Macular Edema (DME), Ophthalmological Evaluation, Screening of Diabetic Retinopathy, Eye Disease, Eye Complications of Diabetes, Diabetes Mellitus, Artificial Intelligence
Eligibility Criteria
Inclusion Criteria:
- Patients of 18 years old and older;
- Ability to provide informed consent;
- Diagnostic for diabetes : 3a) Type 1 diabetes of a lest 5 years of evolution; or 3b) Type 2 diabetes;
- 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:
- Patients less than 18 years old;
- Inability to provide informed consent;
- Patient who already had a treatment (surgery, laser, injection, etc.) for any retinal condition : Age-related macular degeneration (AMD), retinal vascular occlusion (RVO); etc.
Sites / Locations
Arms of the Study
Arm 1
Arm Type
Experimental
Arm Label
Diabetic Retinopathy (DR)
Arm Description
Screening of DR with artificial intelligence (NeoRetina algorithm) and diagnostic evaluation with a standard of care ophthalmological examination.
Outcomes
Primary Outcome Measures
Artificial Intelligence - Absence or Presence of Diabetic Retinopathy (DR)
Analysis of retinal images by artificial intelligence (NeoRetina) to determine the absence or the presence of diabetic retinopathy (DR)
R0 : No DR
R+ : Presence of DR
Eye Examination - Absence or Presence of Diabetic Retinopathy (DR)
Eye examination done by an ophthalmologist to determine the absence or the presence of diabetic retinopathy (DR) (blind assessment)
R0 : No DR
R+ : Presence of DR
Manual Analysis of Retinal Images - Absence or Presence of Diabetic Retinopathy (DR)
Manual analysis of retinal images acquired by Diagnos by an ophthalmologist of the CHUM to determine the absence or the presence of diabetic retinopathy (DR) (blind assessment)
R0 : No DR
R+ : Presence of DR
Artificial Intelligence - Severity of Diabetic Retinopathy (DR)
Analysis of retinal images by artificial intelligence (NeoRetina) to grade the severity of diabetic retinopathy (DR)
R1 - Mild NPDR: Mild Nonproliferative Diabetic Retinopathy
R2 - Moderate NPDR: Moderate Nonproliferative Diabetic Retinopathy
R3 - Severe NPDR : Severe Nonproliferative Diabetic Retinopathy
R4 - PDR : Proliferative Diabetic Retinopathy
Eye Examination - Severity of Diabetic Retinopathy (DR)
Eye examination done by an ophthalmologist to grade the severity of diabetic retinopathy (DR) (blind assessment)
R1 - Mild NPDR: Mild Nonproliferative Diabetic Retinopathy
R2 - Moderate NPDR: Moderate Nonproliferative Diabetic Retinopathy
R3 - Severe NPDR : Severe Nonproliferative Diabetic Retinopathy
R4 - PDR : Proliferative Diabetic Retinopathy
Manual Analysis of Retinal Images - Severity of Diabetic Retinopathy (DR)
Manual revision of retinal images acquired by Diagnos by an ophthalmologist of the CHUM to grade the severity of diabetic retinopathy (DR) (blind assessment)
R1 - Mild NPDR: Mild Nonproliferative Diabetic Retinopathy
R2 - Moderate NPDR: Moderate Nonproliferative Diabetic Retinopathy
R3 - Severe NPDR : Severe Nonproliferative Diabetic Retinopathy
R4 - PDR : Proliferative Diabetic Retinopathy
Artificial Intelligence - Absence or Presence of Diabetic Macular Edema (DME)
Analysis of retinal images by artificial intelligence (NeoRetina) to determine the absence or the presence of diabetic macular edema (DME)
M0 : No DME
M+ : Presence of DME
Eye Examination - Absence or Presence of Diabetic Macular Edema (DME)
Eye examination done by an ophthalmologist to determine the absence or the presence of diabetic macular edema (DME) (blind assessment)
M0 : No DME
M+ : Presence of DME
Manual Analysis of Retinal Images - Absence or Presence of Diabetic Macular Edema (DME)
Manual analysis of retinal images acquired by Diagnos by an ophthalmologist of the CHUM to determine the absence or the presence of diabetic macular edema (DME) (blind assessment)
M0 : No DME
M+ : Presence of DME
Artificial Intelligence - Severity of Diabetic Macular Edema (DME)
Analysis of retinal images by artificial intelligence (NeoRetina) to grade the severity of diabetic macular edema (DME)
M1 : Non Central DME
M2 : Central DME
Eye Examination - Severity of Diabetic Macular Edema (DME)
Eye examination done by an ophthalmologist to grade the severity of diabetic macular edema (DME) (blind assessment)
M1 : Non Central DME
M2 : Central DME
Manual Analysis of Retinal Images - Severity of Diabetic Macular Edema (DME)
Manual analysis of retinal images acquired by Diagnos by an ophthalmologist of the CHUM to grade the severity of diabetic macular edema (DME) (blind assessment)
M1 : Non Central DME
M2 : Central DME
Secondary Outcome Measures
Performance of NeoRetina Algorithm - Diabetic Retinopathy (DR)
The performance of NeoRetina algorithm for the detection and the grading of diabetic retinopathy (DR) will be evaluated.
The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) and area under the receiver operating characteristic curve (AUC, 95% CI) will be calculated.
The levels of agreement will be determined by kappa analyses.
Performance of NeoRetina Algorithm - Diabetic Macular Edema (DME)
The performance of NeoRetina algorithm for the detection and the grading of diabetic macular edema (DME) will be evaluated.
The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) and area under the receiver operating characteristic curve (AUC, 95% CI) will be calculated.
The levels of agreement will be determined by kappa analyses.
Full Information
NCT ID
NCT04699864
First Posted
December 16, 2020
Last Updated
June 28, 2023
Sponsor
Centre hospitalier de l'Université de Montréal (CHUM)
Collaborators
DIAGNOS Inc.
1. Study Identification
Unique Protocol Identification Number
NCT04699864
Brief Title
Evaluation of NeoRetina Artificial Intelligence Algorithm for the Screening of Diabetic Retinopathy at the CHUM
Acronym
DR-NeoRetina
Official Title
The Use of Artificial Intelligence in the Early Detection and the Follow-Up of Diabetic Retinopathy of Diabetic Patients Followed at the CHUM: Evaluation of NeoRetina Automated Algorithm (DIAGNOS Inc.)
Study Type
Interventional
2. Study Status
Record Verification Date
June 2023
Overall Recruitment Status
Not yet recruiting
Study Start Date
September 2023 (Anticipated)
Primary Completion Date
September 2024 (Anticipated)
Study Completion Date
December 2025 (Anticipated)
3. Sponsor/Collaborators
Responsible Party, by Official Title
Sponsor
Name of the Sponsor
Centre hospitalier de l'Université de Montréal (CHUM)
Collaborators
DIAGNOS Inc.
4. Oversight
Studies a U.S. FDA-regulated Drug Product
No
Studies a U.S. FDA-regulated Device Product
No
Data Monitoring Committee
No
5. Study Description
Brief Summary
This prospective study aims to validate if NeoRetina, an artificial intelligence algorithm developped by DIAGNOS Inc. and trained to automatically detect the presence of diabetic retinopathy (DR) by the analysis of macula centered eye fundus photographies, can detect this disease and grade its severity.
Detailed Description
More than 880 000 Quebecers (more than 10% of the population) suffer from diabetes, which is the main cause of blindness in diabetic adults under 65 years of age, and around 40% of people with diabetes suffer from diabetic retinopathy (DR). The early detection of DR and a regular follow-up is thus crucial to prevent the progression of this disease.
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.
6. Conditions and Keywords
Primary Disease or Condition Being Studied in the Trial, or the Focus of the Study
Diabetic Retinopathy, Diabetic Macular Edema, Diabetic Maculopathy
Keywords
Diabetes, Type 1 Diabetes, Type 2 Diabetes, Diabetic Retinopathy (DR), Diabetic Macular Edema (DME), Ophthalmological Evaluation, Screening of Diabetic Retinopathy, Eye Disease, Eye Complications of Diabetes, Diabetes Mellitus, Artificial Intelligence
7. Study Design
Primary Purpose
Diagnostic
Study Phase
Not Applicable
Interventional Study Model
Single Group Assignment
Masking
None (Open Label)
Allocation
N/A
Enrollment
630 (Anticipated)
8. Arms, Groups, and Interventions
Arm Title
Diabetic Retinopathy (DR)
Arm Type
Experimental
Arm Description
Screening of DR with artificial intelligence (NeoRetina algorithm) and diagnostic evaluation with a standard of care ophthalmological examination.
Intervention Type
Diagnostic Test
Intervention Name(s)
Screening of DR and DME with artificial intelligence using NeoRetina
Intervention Description
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.
Intervention Type
Diagnostic Test
Intervention Name(s)
Routine ophthalmological evaluation of DR and DME
Intervention Description
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.
Intervention Type
Diagnostic Test
Intervention Name(s)
Manual grading of DR and DME by CHUM ophthalmologists based on retinal photographies acquired by Diagnos
Intervention Description
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.
Primary Outcome Measure Information:
Title
Artificial Intelligence - Absence or Presence of Diabetic Retinopathy (DR)
Description
Analysis of retinal images by artificial intelligence (NeoRetina) to determine the absence or the presence of diabetic retinopathy (DR)
R0 : No DR
R+ : Presence of DR
Time Frame
Baseline
Title
Eye Examination - Absence or Presence of Diabetic Retinopathy (DR)
Description
Eye examination done by an ophthalmologist to determine the absence or the presence of diabetic retinopathy (DR) (blind assessment)
R0 : No DR
R+ : Presence of DR
Time Frame
Baseline
Title
Manual Analysis of Retinal Images - Absence or Presence of Diabetic Retinopathy (DR)
Description
Manual analysis of retinal images acquired by Diagnos by an ophthalmologist of the CHUM to determine the absence or the presence of diabetic retinopathy (DR) (blind assessment)
R0 : No DR
R+ : Presence of DR
Time Frame
Baseline
Title
Artificial Intelligence - Severity of Diabetic Retinopathy (DR)
Description
Analysis of retinal images by artificial intelligence (NeoRetina) to grade the severity of diabetic retinopathy (DR)
R1 - Mild NPDR: Mild Nonproliferative Diabetic Retinopathy
R2 - Moderate NPDR: Moderate Nonproliferative Diabetic Retinopathy
R3 - Severe NPDR : Severe Nonproliferative Diabetic Retinopathy
R4 - PDR : Proliferative Diabetic Retinopathy
Time Frame
Baseline
Title
Eye Examination - Severity of Diabetic Retinopathy (DR)
Description
Eye examination done by an ophthalmologist to grade the severity of diabetic retinopathy (DR) (blind assessment)
R1 - Mild NPDR: Mild Nonproliferative Diabetic Retinopathy
R2 - Moderate NPDR: Moderate Nonproliferative Diabetic Retinopathy
R3 - Severe NPDR : Severe Nonproliferative Diabetic Retinopathy
R4 - PDR : Proliferative Diabetic Retinopathy
Time Frame
Baseline
Title
Manual Analysis of Retinal Images - Severity of Diabetic Retinopathy (DR)
Description
Manual revision of retinal images acquired by Diagnos by an ophthalmologist of the CHUM to grade the severity of diabetic retinopathy (DR) (blind assessment)
R1 - Mild NPDR: Mild Nonproliferative Diabetic Retinopathy
R2 - Moderate NPDR: Moderate Nonproliferative Diabetic Retinopathy
R3 - Severe NPDR : Severe Nonproliferative Diabetic Retinopathy
R4 - PDR : Proliferative Diabetic Retinopathy
Time Frame
Baseline
Title
Artificial Intelligence - Absence or Presence of Diabetic Macular Edema (DME)
Description
Analysis of retinal images by artificial intelligence (NeoRetina) to determine the absence or the presence of diabetic macular edema (DME)
M0 : No DME
M+ : Presence of DME
Time Frame
Baseline
Title
Eye Examination - Absence or Presence of Diabetic Macular Edema (DME)
Description
Eye examination done by an ophthalmologist to determine the absence or the presence of diabetic macular edema (DME) (blind assessment)
M0 : No DME
M+ : Presence of DME
Time Frame
Baseline
Title
Manual Analysis of Retinal Images - Absence or Presence of Diabetic Macular Edema (DME)
Description
Manual analysis of retinal images acquired by Diagnos by an ophthalmologist of the CHUM to determine the absence or the presence of diabetic macular edema (DME) (blind assessment)
M0 : No DME
M+ : Presence of DME
Time Frame
Baseline
Title
Artificial Intelligence - Severity of Diabetic Macular Edema (DME)
Description
Analysis of retinal images by artificial intelligence (NeoRetina) to grade the severity of diabetic macular edema (DME)
M1 : Non Central DME
M2 : Central DME
Time Frame
Baseline
Title
Eye Examination - Severity of Diabetic Macular Edema (DME)
Description
Eye examination done by an ophthalmologist to grade the severity of diabetic macular edema (DME) (blind assessment)
M1 : Non Central DME
M2 : Central DME
Time Frame
Baseline
Title
Manual Analysis of Retinal Images - Severity of Diabetic Macular Edema (DME)
Description
Manual analysis of retinal images acquired by Diagnos by an ophthalmologist of the CHUM to grade the severity of diabetic macular edema (DME) (blind assessment)
M1 : Non Central DME
M2 : Central DME
Time Frame
Baseline
Secondary Outcome Measure Information:
Title
Performance of NeoRetina Algorithm - Diabetic Retinopathy (DR)
Description
The performance of NeoRetina algorithm for the detection and the grading of diabetic retinopathy (DR) will be evaluated.
The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) and area under the receiver operating characteristic curve (AUC, 95% CI) will be calculated.
The levels of agreement will be determined by kappa analyses.
Time Frame
3 years
Title
Performance of NeoRetina Algorithm - Diabetic Macular Edema (DME)
Description
The performance of NeoRetina algorithm for the detection and the grading of diabetic macular edema (DME) will be evaluated.
The sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) and area under the receiver operating characteristic curve (AUC, 95% CI) will be calculated.
The levels of agreement will be determined by kappa analyses.
Time Frame
3 years
10. Eligibility
Sex
All
Minimum Age & Unit of Time
18 Years
Accepts Healthy Volunteers
No
Eligibility Criteria
Inclusion Criteria:
Patients of 18 years old and older;
Ability to provide informed consent;
Diagnostic for diabetes : 3a) Type 1 diabetes of a lest 5 years of evolution; or 3b) Type 2 diabetes;
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:
Patients less than 18 years old;
Inability to provide informed consent;
Patient who already had a treatment (surgery, laser, injection, etc.) for any retinal condition : Age-related macular degeneration (AMD), retinal vascular occlusion (RVO); etc.
Central Contact Person:
First Name & Middle Initial & Last Name or Official Title & Degree
Marie-Catherine Tessier, MSc
Phone
514-890-8000
Ext
11550
Email
marie-catherine.tessier.chum@ssss.gouv.qc.ca
First Name & Middle Initial & Last Name or Official Title & Degree
Karim Hammamji, MD
Phone
514-890-8000
Ext
11550
Email
ophtalmologie.recherche.chum@ssss.gouv.qc.ca
Overall Study Officials:
First Name & Middle Initial & Last Name & Degree
Karim Hammamji, MD
Organizational Affiliation
Centre hospitalier de l'Université de Montréal (CHUM)
Official's Role
Principal Investigator
12. IPD Sharing Statement
Plan to Share IPD
No
Citations:
Citation
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.
Results Reference
background
PubMed Identifier
32569310
Citation
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.
Results Reference
background
Links:
URL
https://www.diabete.qc.ca/en/understand-diabetes/all-about-diabetes/myths-and-statistics/
Description
Diabetes Quebec - Understanding Diabetes : Myths and Statistics
URL
http://www.diagnos.ca/
Description
DIAGNOS Inc.
Learn more about this trial
Evaluation of NeoRetina Artificial Intelligence Algorithm for the Screening of Diabetic Retinopathy at the CHUM
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