Diabetic RetinopathyDR-NeoRetinaNCT04699864
Evaluation of NeoRetina Artificial Intelligence Algorithm for the Screening of Diabetic Retinopathy at the CHUM
A study testing Screening of DR and DME with artificial intelligence using NeoRetina, Routine ophthalmological evaluation of DR and DME and Manual grading of DR and DME by CHUM ophthalmologists based on retinal photographies acquired by Diagnos in people with Diabetic Retinopathy and Diabetic Macular Edema, run by Centre hospitalier de l'Université de Montréal (CHUM) at 1 site in Canada.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Diabetic Retinopathy, Diabetic Macular Edema, Diabetic Maculopathy
- What is being tested?
- Screening of DR and DME with artificial intelligence using NeoRetina, Routine ophthalmological evaluation of DR and DME and Manual grading of DR and DME by CHUM ophthalmologists based on retinal photographies acquired by Diagnos
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 24 participants
- Where
- 1 siteCanada
- Study dates
- 10 Jun 2024to 1 Jul 2026
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 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.
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
Canada1 site
Centre hospitalier de l'Université de Montréal
Montreal, Quebec, H2X 0A9
About this study
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.)
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.
Read the 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.
- Being tested
- Screening of DR and DME with artificial intelligence using NeoRetina, Routine ophthalmological evaluation of DR and DME, Manual grading of DR and DME by CHUM ophthalmologists based on retinal photographies acquired by Diagnos
- 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
- Medical subject headings
- Diabetic Retinopathy, Diabetes Mellitus, Diabetes Mellitus, Type 1, Diabetes Mellitus, Type 2, Eye Diseases
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Screening of DR and DME with artificial intelligence using NeoRetinaDiagnostic test
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.
Routine ophthalmological evaluation of DR and DMEDiagnostic test
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.
Manual grading of DR and DME by CHUM ophthalmologists based on retinal photographies acquired by DiagnosDiagnostic test
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.
| Study group | Role | What they receive |
|---|---|---|
| Diabetic Retinopathy (DR) | Experimental | Screening of DR with artificial intelligence (NeoRetina algorithm) and diagnostic evaluation with a standard of care ophthalmological examination. Receives: 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 |
What is being measured
The primary outcome is the main question the study is designed to answer.
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
Measured: Baseline
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
Measured: Baseline
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
Measured: Baseline
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
Measured: Baseline
Eye Examination - Severity of Diabetic Retinopathy (DR)
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
Measured: Baseline
Manual Analysis of Retinal Images - Severity of Diabetic Retinopathy (DR)
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
Measured: Baseline
Artificial Intelligence - Absence or Presence of Diabetic Macular Edema (DME)
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
Measured: Baseline
Eye Examination - Absence or Presence of Diabetic Macular Edema (DME)
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
Measured: Baseline
Manual Analysis of Retinal Images - Absence or Presence of Diabetic Macular Edema (DME)
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
Measured: Baseline
Artificial Intelligence - Severity of Diabetic Macular Edema (DME)
Analysis of retinal images by artificial intelligence (NeoRetina) to grade the severity of diabetic macular edema (DME)
- M1 : Non Central DME
- M2 : Central DME
Measured: Baseline
Eye Examination - Severity of Diabetic Macular Edema (DME)
Eye examination done by an ophthalmologist to grade the severity of diabetic macular edema (DME) (blind assessment)
- M1 : Non Central DME
- M2 : Central DME
Measured: Baseline
Manual Analysis of Retinal Images - Severity of Diabetic Macular Edema (DME)
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
Measured: Baseline
Secondary outcomes (2)
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.
Measured: 3 years
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.
Measured: 3 years
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted7 Jan 2021
- Study start10 Jun 2024
- Primary completion1 Jul 2026
- Study completion1 Jul 2026
Why it stopped: logistical constraints
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Diabetic RetinopathyStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Enrolment
- 24 participants (actual)
Sponsor and investigators
- Lead sponsor
- Centre hospitalier de l'Université de Montréal (CHUM)
- Sponsor type
- University, hospital or other
- Collaborators
- DIAGNOS Inc.
- Responsible party
- Sponsor
- Investigators
- Karim Hammamji, MD, Principal investigator, Centre hospitalier de l'Université de Montréal (CHUM)
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT04699864
- Sponsor's protocol ID
- 20.292
- Acronym
- DR-NeoRetina
- Status last verified
- Jul 2026
- First submitted
- 16 Dec 2020
- Last updated
- 8 Jul 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
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.
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.
PubMed 32569310
Study at a glance
From the registry record, last updated by the sponsor on 8 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- 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.)
- ClinicalTrials.gov ID
- NCT04699864
- Acronym / sponsor ID
- DR-NeoRetina / 20.292
- Keywords
- Diabetes, Type 1 Diabetes, Type 2 Diabetes, Diabetic Retinopathy (DR), Diabetic Macular Edema (DME), Ophthalmological Evaluation, Screening of Diabetic Retinopathy, Eye Disease
- Study type
- Interventional
- Recruitment status
- TerminatedReason stopped: logistical constraints
- Enrolment
- 24 participants (actual)
- Lead sponsor
- Centre hospitalier de l'Université de Montréal (CHUM)University, hospital or other
- Collaborators
- DIAGNOS Inc.
- Study start
- 10 Jun 2024 (actual)
- Primary completion
- 1 Jul 2026 (actual)
- Study completion
- 1 Jul 2026 (actual)
- First posted
- 7 Jan 2021 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Primary purpose
- Diagnosis
- Allocation
- Not applicable
- Intervention model
- Single group
- Masking
- None (open label)
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Diabetic Retinopathy (DR) | Experimental | 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 DiagnosScreening of DR with artificial intelligence (NeoRetina algorithm) and diagnostic evaluation with a standard of care ophthalmological examination. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- 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.
Key exclusion criteria (3 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Screening of DR and DME with artificial intelligence using NeoRetinaMacula-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 | Diabetic Retinopathy (DR) |
| Routine ophthalmological evaluation of DR and DMEStandard 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 | Diabetic Retinopathy (DR) |
| Manual grading of DR and DME by CHUM ophthalmologists based on retinal photographies acquired by DiagnosOphthalmologists 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. | Diagnostic test | Diabetic Retinopathy (DR) |
Primary outcome
Artificial Intelligence - Absence or Presence of Diabetic Retinopathy (DR)
Time frame: 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)
Time frame: 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 - Absence or Presence of Diabetic Retinopathy (DR)
Time frame: 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 - Severity of Diabetic Retinopathy (DR)
Time frame: 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)
Time frame: 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
Manual Analysis of Retinal Images - Severity of Diabetic Retinopathy (DR)
Time frame: 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 - Absence or Presence of Diabetic Macular Edema (DME)
Time frame: 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)
Time frame: 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)
Time frame: 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 - Severity of Diabetic Macular Edema (DME)
Time frame: 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
Eye Examination - Severity of Diabetic Macular Edema (DME)
Time frame: 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
Manual Analysis of Retinal Images - Severity of Diabetic Macular Edema (DME)
Time frame: 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
2 secondary outcomes
Performance of NeoRetina Algorithm - Diabetic Retinopathy (DR)
Time frame: 3 years
Performance of NeoRetina Algorithm - Diabetic Macular Edema (DME)
Time frame: 3 years
Results and safety
The study was stopped early (reason given: logistical constraints). Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 8 Jul 2026.
- BackgroundUnité 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.
- Background: PubMed 32569310Shaban 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.
- Diabetes Quebec - Understanding Diabetes : Myths and StatisticsLink provided by the sponsor.
- DIAGNOS Inc.Link provided by the sponsor.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Karim Hammamji, MD principal investigator
Centre hospitalier de l'Université de Montréal (CHUM)
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Screening of DR and DME with artificial intelligence using NeoRetina, Routine ophthalmological evaluation of DR and DME and Manual grading of DR and DME by CHUM ophthalmologists based on retinal photographies acquired by Diagnos in people with Diabetic Retinopathy and Diabetic Macular Edema, run by Centre hospitalier de l'Université de Montréal (CHUM) at 1 site in Canada.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: stopped early, as of the sponsor's last update on 8 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Canada. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study was stopped early (reason given: logistical constraints). Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Artificial Intelligence - Absence or Presence of Diabetic Retinopathy (DR) (time frame: Baseline). The study lists 12 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT04699864, which also lists 2 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 8 Jul 2026.