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

Sponsor
Centre hospitalier de l'Université de Montréal (CHUM) (Other)
Overall Status
Not yet recruiting
CT.gov ID
NCT04699864
Collaborator
DIAGNOS Inc. (Other)
630
Enrollment
1
Arm
43
Anticipated Duration (Months)

Study Details

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.

Condition or DiseaseIntervention/TreatmentPhase
  • 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
N/A

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 Design

Study Type:
Interventional
Anticipated Enrollment :
630 participants
Allocation:
N/A
Intervention Model:
Single Group Assignment
Masking:
None (Open Label)
Primary Purpose:
Diagnostic
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.)
Anticipated Study Start Date :
May 1, 2022
Anticipated Primary Completion Date :
May 1, 2024
Anticipated Study Completion Date :
Dec 1, 2025

Arms and Interventions

ArmIntervention/Treatment
Experimental: Diabetic Retinopathy (DR)

Screening of DR with artificial intelligence (NeoRetina algorithm) and diagnostic evaluation with a standard of care ophthalmological examination.

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.

Outcome Measures

Primary Outcome Measures

  1. 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

  2. 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

  3. 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

  4. 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

  5. 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

  6. 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

  7. 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

  8. 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

  9. 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

  10. 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

  11. 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

  12. 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

Secondary Outcome Measures

  1. 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.

  2. 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.

Eligibility Criteria

Criteria

Ages Eligible for Study:
18 Years and Older
Sexes Eligible for Study:
All
Accepts Healthy Volunteers:
No
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.

Contacts and Locations

Locations

No locations specified.

Sponsors and Collaborators

  • Centre hospitalier de l'Université de Montréal (CHUM)
  • DIAGNOS Inc.

Investigators

  • Principal Investigator: Salim Lahoud, MD, Centre hospitalier de l'Université de Montréal (CHUM)

Study Documents (Full-Text)

None provided.

More Information

Additional Information:

Publications

Responsible Party:
Centre hospitalier de l'Université de Montréal (CHUM)
ClinicalTrials.gov Identifier:
NCT04699864
Other Study ID Numbers:
  • 20.292
First Posted:
Jan 7, 2021
Last Update Posted:
Mar 31, 2022
Last Verified:
Mar 1, 2022
Individual Participant Data (IPD) Sharing Statement:
No
Plan to Share IPD:
No
Studies a U.S. FDA-regulated Drug Product:
No
Studies a U.S. FDA-regulated Device Product:
No
Keywords provided by Centre hospitalier de l'Université de Montréal (CHUM)
Additional relevant MeSH terms:

Study Results

No Results Posted as of Mar 31, 2022