Evaluation of NeoRetina Artificial Intelligence Algorithm for the Screening of Diabetic Retinopathy at the CHUM

Not Applicable

Not yet recruiting

• Conditions: Diabetic Macular Edema; Diabetic Maculopathy; Diabetic Retinopathy
• Interventions: Diagnostic Test: Screening of DR and DME with artificial intelligence using NeoRetina; Diagnostic Test: Routine ophthalmological evaluation of DR and DME; Diagnostic Test: Manual grading of DR and DME by CHUM ophthalmologists based on retinal photographies acquired by Diagnos

• Registration Number: NCT04699864
• Lead Sponsor: Centre hospitalier de l'Université de Montréal (CHUM)

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.

Study Timeline

• Study First Submitted: 2020-12-16
• Study First Submitted QC: 2021-01-05
• Study First Posted: 2021-01-07
• Status Verified: 2024-02
• Last Update Submitted: 2024-02-06
• Last Update Posted: 2024-02-07
• Study Start: 2024-04
• Primary Completion: 2025-04
• Study Completion: 2026-12

Recruitment & Eligibility

• Status: NOT_YET_RECRUITING
• Sex: All
• Target Recruitment: 630

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.

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: SINGLE_GROUP

Arm && Interventions

Group	Intervention	Description
Diabetic Retinopathy (DR)	Routine ophthalmological evaluation of DR and DME	Screening of DR with artificial intelligence (NeoRetina algorithm) and diagnostic evaluation with a standard of care ophthalmological examination.
Diabetic Retinopathy (DR)	Screening of DR and DME with artificial intelligence using NeoRetina	Screening of DR with artificial intelligence (NeoRetina algorithm) and diagnostic evaluation with a standard of care ophthalmological examination.
Diabetic Retinopathy (DR)	Manual grading of DR and DME by CHUM ophthalmologists based on retinal photographies acquired by Diagnos	Screening of DR with artificial intelligence (NeoRetina algorithm) and diagnostic evaluation with a standard of care ophthalmological examination.

Primary Outcome Measures

Name	Time	Method
Manual Analysis of Retinal Images - Absence or Presence of Diabetic Retinopathy (DR)	Baseline	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 - Absence or Presence of Diabetic Macular Edema (DME)	Baseline	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)	Baseline	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)	Baseline	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 - Absence or Presence of Diabetic Retinopathy (DR)	Baseline	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)	Baseline	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 - Severity of Diabetic Retinopathy (DR)	Baseline	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 - Severity of Diabetic Macular Edema (DME)	Baseline	Analysis of retinal images by artificial intelligence (NeoRetina) to grade the severity of diabetic macular edema (DME); * M1 : Non Central DME; * M2 : Central DME
Manual Analysis of Retinal Images - Severity of Diabetic Macular Edema (DME)	Baseline	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
Artificial Intelligence - Severity of Diabetic Retinopathy (DR)	Baseline	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)	Baseline	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
Eye Examination - Severity of Diabetic Macular Edema (DME)	Baseline	Eye examination done by an ophthalmologist to grade the severity of diabetic macular edema (DME) (blind assessment); * M1 : Non Central DME; * M2 : Central DME

Secondary Outcome Measures

Name	Time	Method
Performance of NeoRetina Algorithm - Diabetic Macular Edema (DME)	3 years	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.
Performance of NeoRetina Algorithm - Diabetic Retinopathy (DR)	3 years	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.

Trial Locations

Locations (1)

Centre hospitalier de l'Université de Montréal; 🇨🇦 Montréal, Quebec, Canada