AI Screening for Diabetic Retinopathy

(DR-NeoRetina Trial)

Overseen By Karim Hammamji, MD

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Centre hospitalier de l'Université de Montréal (CHUM)

Disqualifiers: Under 18, Prior retinal treatment, others

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Approved in 5 Jurisdictions This treatment is already approved in other countries

Compensation: Varies

Number of Visits: 1

630 Participants Needed

What You Need to Know Before You Apply

What is the purpose of this trial?

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.

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications.

Is the AI system for diabetic retinopathy screening safe for humans?

The studies reviewed focus on the effectiveness of AI systems in screening for diabetic retinopathy, but they do not report any specific safety concerns related to their use in humans. ^{1 2 3 4 5}

How is the treatment NeoRetina different from other treatments for diabetic retinopathy?

NeoRetina is unique because it uses artificial intelligence to analyze eye images for diabetic retinopathy, offering a non-invasive and efficient screening method compared to traditional manual examinations. ^{6 7 8 9 10}

What data supports the effectiveness of the treatment NeoRetina, NeoRetina, CARA for diabetic retinopathy?

Research shows that artificial intelligence (AI) can effectively screen for diabetic retinopathy (a diabetes-related eye condition) by analyzing eye images, which suggests that AI-based treatments like NeoRetina, NeoRetina, CARA could be useful in identifying this condition early. ^{6 7 9 10 11}

Who Is on the Research Team?

Karim Hammamji, MD

Principal Investigator

Centre hospitalier de l'Université de Montréal (CHUM)

Are You a Good Fit for This Trial?

This trial is for adults over 18 with diabetes (Type 1 for at least 5 years, or Type 2) who are being treated or referred by the CHUM hospital. They must be able to give informed consent. It's not suitable for those who don't meet these specific conditions.

Inclusion Criteria

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.

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

AI Screening and Ophthalmological Evaluation

Participants undergo screening for diabetic retinopathy using the NeoRetina AI algorithm and a full eye examination by an ophthalmologist

Baseline

1 visit (in-person)

Follow-up

Participants are monitored for safety and effectiveness after the initial screening and evaluation

3 years

What Are the Treatments Tested in This Trial?

Interventions

• NeoRetina

Trial Overview The study tests NeoRetina, an AI algorithm designed to detect and grade diabetic retinopathy severity from eye photos. It compares routine eye exams and manual grading by ophthalmologists against this new AI screening method.

How Is the Trial Designed?

Group I: Diabetic Retinopathy (DR) Experimental Treatment 3 Interventions

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

NeoRetina is already approved in Canada, United States, European Union for the following indications:

Approved in Canada as NeoRetina for:

• Diabetic retinopathy screening

Approved in United States as CARA for:

• Visualization, storage, and enhancement of color fundus images

Approved in European Union as CARA for:

• Visualization, storage, and enhancement of color fundus images

Who Is Running the Clinical Trial?

Centre hospitalier de l'Université de Montréal (CHUM)

Lead Sponsor

Trials

389

Recruited

143,000+

DIAGNOS Inc.

Collaborator

Trials

1

Recruited

630+

Published Research Related to This Trial

In a study involving 600 diabetic patients, AI-based screening for diabetic retinopathy (DR) demonstrated high sensitivity (97.78%) and specificity (98.38%) for detecting referable DR, indicating its effectiveness as a diagnostic tool.

The use of AI not only matched but also improved the referral rate for DR cases compared to ophthalmologists, increasing referrals from 45 to 75, showcasing its potential to enhance clinical outcomes in community settings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Application of artificial intelligence-based dual-modality analysis combining fundus photography and optical coherence tomography in diabetic retinopathy screening in a community hospital. Liu, R., Li, Q., Xu, F., et al.[2022]

A study involving 6146 patients showed that using AI for non-mydriatic fundus photography in diabetic retinopathy screening has a high sensitivity of 97.3% and a kappa coefficient of 0.75, indicating strong agreement with ophthalmic diagnoses.

The AI's consistency with fluorescein angiography was moderate, with a kappa coefficient of 0.53 and a coincidence rate of 66.9%, suggesting that AI can effectively assist in early detection of diabetic retinopathy, which is crucial for preventing blindness.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Application and observation of artificial intelligence in clinical practice of fundus screening for diabetic retinopathy with non-mydriatic fundus photography: a retrospective observational study of T2DM patients in Tianjin, China. Hao, Z., Xu, R., Huang, X., et al.[2022]

The automated diabetic retinopathy screening tool, DART, demonstrated high sensitivity (94.6%) and a strong negative predictive value (98.1%) in analyzing ocular fundus photographs from 1123 diabetic eye exams, indicating its effectiveness in identifying potential cases of diabetic retinopathy.

With a specificity of 74.3% and an area under the ROC curve of 0.915, DART shows promise for implementation in Chile's national diabetic retinopathy screening program, suggesting it can be a reliable option in diverse healthcare settings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Clinical validation of an artificial intelligence-based diabetic retinopathy screening tool for a national health system. Arenas-Cavalli, JT., Abarca, I., Rojas-Contreras, M., et al.[2023]

In a study involving 827 participants screened for diabetic retinopathy (DR) using AI, 33.2% were referred for follow-up, demonstrating the AI's effectiveness in identifying cases needing further evaluation.

The AI system showed a high sensitivity of 92% and specificity of 85% for detecting referable DR compared to human grading, indicating that AI can be a reliable tool for DR screening in clinical settings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Feasibility and acceptance of artificial intelligence-based diabetic retinopathy screening in Rwanda. Whitestone, N., Nkurikiye, J., Patnaik, JL., et al.[2023]

The MONA.health artificial intelligence screening software demonstrated high diagnostic performance for detecting diabetic retinopathy (DR) and diabetic macular edema (DME), achieving an area under the curve (AUC) of 97.28% for DR and 98.08% for DME on a private test set.

The software maintained strong sensitivity (90.91%) and specificity (94.24%) across various subgroups, although sensitivity was slightly lower for individuals over 65 years old and Caucasians, indicating its effectiveness across diverse populations.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Artificial Intelligence Software for Diabetic Eye Screening: Diagnostic Performance and Impact of Stratification. Peeters, F., Rommes, S., Elen, B., et al.[2023]

The AIDRScreening system demonstrated high sensitivity (86.72%) and specificity (96.09%) in detecting referable diabetic retinopathy (RDR) among 962 participants with diabetes, indicating it is an effective tool for screening.

The system significantly improved diagnostic efficiency, achieving a 16.57% higher diagnostic accuracy compared to manual grading while reducing the average diagnostic time by 37.32%.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Performance of the AIDRScreening system in detecting diabetic retinopathy in the fundus photographs of Chinese patients: a prospective, multicenter, clinical study. Yang, Y., Pan, J., Yuan, M., et al.[2023]

An AI-based app was developed to predict retinal fluid in patients at risk for age-related macular degeneration (AMD) and diabetic retinopathy (DR), utilizing a convolutional neural network for accurate detection and monitoring.

The app demonstrated high accuracy in internal testing with an area under the curve (AUC) of 99.18% for fluid classification, indicating its potential effectiveness for early detection and personalized monitoring in clinical settings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Predictive, preventive, and personalized management of retinal fluid via computer-aided detection app for optical coherence tomography scans. Quek, TC., Takahashi, K., Kang, HG., et al.[2023]

In a study of 241 patients using an AI system for diabetic retinal disease diagnosis, 51% had nondiagnostic results, with type 1 diabetes, smoking, and older age significantly increasing the likelihood of these results.

A new predictive dilation workflow was developed to identify patients who would benefit most from dilation before their examination, potentially improving efficiency and ensuring more at-risk patients receive necessary retinal screenings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Risk Factors for Nondiagnostic Imaging in a Real-World Deployment of Artificial Intelligence Diabetic Retinal Examinations in an Integrated Health care System: Maximizing Workflow Efficiency Through Predictive Dilation. Shou, BL., Venkatesh, K., Chen, C., et al.[2023]

The AI algorithm Selena+ in the handheld Optomed Aurora fundus camera demonstrated a high sensitivity and specificity of 96.8% for detecting diabetic retinopathy (DR) in a study of 256 patients, making it a reliable screening tool.

The study found a perfect agreement (100%) between the AI analysis and the expert retina specialist's evaluation, suggesting that this technology could effectively support large-scale screening efforts for DR in real-world clinical settings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Artificial intelligence in diabetic retinopathy screening: clinical assessment using handheld fundus camera in a real-life setting. Lupidi, M., Danieli, L., Fruttini, D., et al.[2023]

In a study analyzing 311,604 retinal images from 23,724 veterans, seven automated AI-based diabetic retinopathy (DR) screening algorithms were evaluated, revealing high negative predictive values (82.72-93.69%) but varying sensitivities (50.98-85.90%).

While some algorithms performed comparably or better than human graders, most did not exceed human performance, highlighting the need for thorough real-world testing of AI algorithms before they are used in clinical settings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Multicenter, Head-to-Head, Real-World Validation Study of Seven Automated Artificial Intelligence Diabetic Retinopathy Screening Systems. Lee, AY., Yanagihara, RT., Lee, CS., et al.[2021]

The AI-based screening tool EyeWisdom®DSS demonstrated high sensitivity (91.0%) and good specificity (81.3%) for detecting diabetic retinopathy (DR) in a study of 549 type 2 diabetes patients, indicating its effectiveness as a screening method.

EyeWisdom®MCS was particularly effective at identifying patients without DR, with a specificity of 92.4%, suggesting that AI screening can be a valuable resource in areas with limited access to eye care.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Efficacy of artificial intelligence-based screening for diabetic retinopathy in type 2 diabetes mellitus patients. Pei, X., Yao, X., Yang, Y., et al.[2022]

Citations

1. England pubmed.ncbi.nlm.nih.gov

Application of artificial intelligence-based dual-modality analysis combining fundus photography and optical coherence tomography in diabetic retinopathy screening in a community hospital. [2022]

2. United States pubmed.ncbi.nlm.nih.gov

Application and observation of artificial intelligence in clinical practice of fundus screening for diabetic retinopathy with non-mydriatic fundus photography: a retrospective observational study of T2DM patients in Tianjin, China. [2022]

3. England pubmed.ncbi.nlm.nih.gov

Clinical validation of an artificial intelligence-based diabetic retinopathy screening tool for a national health system. [2023]

4. England pubmed.ncbi.nlm.nih.gov

Feasibility and acceptance of artificial intelligence-based diabetic retinopathy screening in Rwanda. [2023]

5. Switzerland pubmed.ncbi.nlm.nih.gov

Artificial Intelligence Software for Diabetic Eye Screening: Diagnostic Performance and Impact of Stratification. [2023]

6. China pubmed.ncbi.nlm.nih.gov

Performance of the AIDRScreening system in detecting diabetic retinopathy in the fundus photographs of Chinese patients: a prospective, multicenter, clinical study. [2023]

7. Switzerland pubmed.ncbi.nlm.nih.gov

Predictive, preventive, and personalized management of retinal fluid via computer-aided detection app for optical coherence tomography scans. [2023]

8. United States pubmed.ncbi.nlm.nih.gov

Risk Factors for Nondiagnostic Imaging in a Real-World Deployment of Artificial Intelligence Diabetic Retinal Examinations in an Integrated Health care System: Maximizing Workflow Efficiency Through Predictive Dilation. [2023]

9. Germany pubmed.ncbi.nlm.nih.gov

Artificial intelligence in diabetic retinopathy screening: clinical assessment using handheld fundus camera in a real-life setting. [2023]

10. United States pubmed.ncbi.nlm.nih.gov

Multicenter, Head-to-Head, Real-World Validation Study of Seven Automated Artificial Intelligence Diabetic Retinopathy Screening Systems. [2021]

11. Ireland pubmed.ncbi.nlm.nih.gov

Efficacy of artificial intelligence-based screening for diabetic retinopathy in type 2 diabetes mellitus patients. [2022]