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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
Centre hospitalier de l'Université de Montréal (CHUM)
About
Eligibility
Locations
Arms
Outcomes
Full info

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

18 Years - undefined (Adult, Older Adult)All SexesDoes not accept healthy volunteers

Inclusion Criteria:

  1. Patients of 18 years old and older;
  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:

  1. Patients less than 18 years old;
  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.

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

    First Posted
    December 16, 2020
    Last Updated
    June 28, 2023
    Sponsor
    Centre hospitalier de l'Université de Montréal (CHUM)
    Collaborators
    DIAGNOS Inc.
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    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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