Clinical trial NCT04699864: Evaluation of NeoRetina Artificial Intelligence Algorithm for the Screening of Diabetic Retinopathy at the CHUM
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
Overall status: RECRUITING
Phase: NA
Conditions: 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
Lead sponsor: Centre hospitalier de l'Université de Montréal (CHUM)
Sponsor class: OTHER
Collaborators: name: DIAGNOS Inc.
First posted: 2021-01-07
Enrollment: 630
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Primary purpose: DIAGNOSTIC

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.

Eligibility criteria: 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.

Primary outcomes:
measure: Artificial Intelligence - Absence or Presence of Diabetic Retinopathy (DR)
timeFrame: Baseline
measure: Eye Examination - Absence or Presence of Diabetic Retinopathy (DR)
timeFrame: Baseline
measure: Manual Analysis of Retinal Images - Absence or Presence of Diabetic Retinopathy (DR)
timeFrame: Baseline
measure: Artificial Intelligence - Severity of Diabetic Retinopathy (DR)
timeFrame: Baseline
measure: Eye Examination - Severity of Diabetic Retinopathy (DR)
timeFrame: Baseline
measure: Manual Analysis of Retinal Images - Severity of Diabetic Retinopathy (DR)
timeFrame: Baseline
measure: Artificial Intelligence - Absence or Presence of Diabetic Macular Edema (DME)
timeFrame: Baseline
measure: Eye Examination - Absence or Presence of Diabetic Macular Edema (DME)
timeFrame: Baseline
measure: Manual Analysis of Retinal Images - Absence or Presence of Diabetic Macular Edema (DME)
timeFrame: Baseline
measure: Artificial Intelligence - Severity of Diabetic Macular Edema (DME)
timeFrame: Baseline
measure: Eye Examination - Severity of Diabetic Macular Edema (DME)
timeFrame: Baseline
measure: Manual Analysis of Retinal Images - Severity of Diabetic Macular Edema (DME)
timeFrame: Baseline
Secondary outcomes:
measure: Performance of NeoRetina Algorithm - Diabetic Retinopathy (DR)
timeFrame: 3 years
measure: Performance of NeoRetina Algorithm - Diabetic Macular Edema (DME)
timeFrame: 3 years
Other outcomes:
Locations:
city: Montréal
country: Canada
facility: Centre hospitalier de l'Université de Montréal
state: Quebec
Study officials:
affiliation: Centre hospitalier de l'Université de Montréal (CHUM)
name: Karim Hammamji, MD
role: PRINCIPAL_INVESTIGATOR
References:
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.
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.