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.
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.
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.
Single group
Screening of DR and DME with artificial intelligence using NeoRetina
Diagnostic testMacula-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.
Routine ophthalmological evaluation of DR and DME
Diagnostic testStandard 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.
Manual grading of DR and DME by CHUM ophthalmologists based on retinal photographies acquired by Diagnos
Diagnostic testOphthalmologists 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.
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
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.
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Centre hospitalier de l'Université de Montréal (CHUM)
Lead sponsor
DIAGNOS Inc.
Collaborator
