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

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.

Conditions

• Diabetic Retinopathy
• Diabetic Macular Edema
• Diabetic Maculopathy

Interventions

DIAGNOSTIC_TEST

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.

DIAGNOSTIC_TEST

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.

DIAGNOSTIC_TEST

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.

Sponsors & Collaborators

• DIAGNOS Inc.

  collaborator UNKNOWN

• Centre hospitalier de l'Université de Montréal (CHUM)

  lead OTHER

Principal Investigators

• Karim Hammamji, MD · Centre hospitalier de l'Université de Montréal (CHUM)

Study Design

Allocation

NA

Purpose

DIAGNOSTIC

Masking

NONE

Model

SINGLE_GROUP

Eligibility

Min Age

18 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2024-06-10

Primary Completion

2025-04-30

Completion

2026-12-31

Countries

• Canada

Study Locations