AI Screening for Diabetic Retinopathy
(DR-NeoRetina Trial)
Recruiting in Palo Alto (17 mi)
Age: 18+
Sex: Any
Travel: May Be Covered
Time Reimbursement: Varies
Trial Phase: Academic
Recruiting
Sponsor: Centre hospitalier de l'Université de Montréal (CHUM)
Disqualifiers: Under 18, Prior retinal treatment, others
No Placebo Group
Approved in 5 Jurisdictions
Trial Summary
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.
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.
12345Is 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.
678910How 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.
123411Eligibility Criteria
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.
+1 more
Trial Timeline
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
Participant Groups
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.
1Treatment groups
Experimental Treatment
Group I: Diabetic Retinopathy (DR)Experimental Treatment3 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
Find a Clinic Near You
Research Locations NearbySelect from list below to view details:
Centre hospitalier de l'Université de MontréalMontréal, Canada
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Who Is Running the Clinical Trial?
Centre hospitalier de l'Université de Montréal (CHUM)Lead Sponsor
DIAGNOS Inc.Collaborator
References
Application of artificial intelligence-based dual-modality analysis combining fundus photography and optical coherence tomography in diabetic retinopathy screening in a community hospital. [2022]To assess the feasibility and clinical utility of artificial intelligence (AI)-based screening for diabetic retinopathy (DR) and macular edema (ME) by combining fundus photos and optical coherence tomography (OCT) images in a community hospital.
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]To observe the consistency of a preliminary report of artificial intelligence (AI) in the clinical practice of fundus screening for diabetic retinopathy (DR) using non-mydriatic fundus photography.
Clinical validation of an artificial intelligence-based diabetic retinopathy screening tool for a national health system. [2023]To evaluate the accuracy and validity of an automated diabetic retinopathy (DR) screening tool (DART, TeleDx, Santiago, Chile) that uses artificial intelligence to analyze ocular fundus photographs for potential implementation in the national Chilean DR screening programme.
Feasibility and acceptance of artificial intelligence-based diabetic retinopathy screening in Rwanda. [2023]Evidence on the practical application of artificial intelligence (AI)-based diabetic retinopathy (DR) screening is needed.
Artificial Intelligence Software for Diabetic Eye Screening: Diagnostic Performance and Impact of Stratification. [2023]To evaluate the MONA.health artificial intelligence screening software for detecting referable diabetic retinopathy (DR) and diabetic macular edema (DME), including subgroup analysis.
Performance of the AIDRScreening system in detecting diabetic retinopathy in the fundus photographs of Chinese patients: a prospective, multicenter, clinical study. [2023]Diabetic retinopathy (DR) is the leading cause of blindness in the working-age population worldwide, and there is a large unmet need for DR screening in China. This observational, prospective, multicenter, gold standard-controlled study sought to evaluate the effectiveness and safety of the AIDRScreening system (v. 1.0), which is an artificial intelligence (AI)-enabled system that detects DR in the Chinese population based on fundus photographs.
Predictive, preventive, and personalized management of retinal fluid via computer-aided detection app for optical coherence tomography scans. [2023]Computer-aided detection systems for retinal fluid could be beneficial for disease monitoring and management by chronic age-related macular degeneration (AMD) and diabetic retinopathy (DR) patients, to assist in disease prevention via early detection before the disease progresses to a "wet AMD" pathology or diabetic macular edema (DME), requiring treatment. We propose a proof-of-concept AI-based app to help predict fluid via a "fluid score", prevent fluid progression, and provide personalized, serial monitoring, in the context of predictive, preventive, and personalized medicine (PPPM) for patients at risk of retinal fluid complications.
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]Label="OBJECTIVE" NlmCategory="UNASSIGNED">In the pivotal clinical trial that led to Food and Drug Administration De Novo "approval" of the first fully autonomous artificial intelligence (AI) diabetic retinal disease diagnostic system, a reflexive dilation protocol was used. Using real-world deployment data before implementation of reflexive dilation, we identified factors associated with nondiagnostic results. These factors allow a novel predictive dilation workflow, where patients most likely to benefit from pharmacologic dilation are dilated a priori to maximize efficiency and patient satisfaction.
Artificial intelligence in diabetic retinopathy screening: clinical assessment using handheld fundus camera in a real-life setting. [2023]Diabetic retinopathy (DR) represents the main cause of vision loss among working age people. A prompt screening of this condition may prevent its worst complications. This study aims to validate the in-built artificial intelligence (AI) algorithm Selena+ of a handheld fundus camera (Optomed Aurora, Optomed, Oulu, Finland) in a first line screening of a real-world clinical setting.
Multicenter, Head-to-Head, Real-World Validation Study of Seven Automated Artificial Intelligence Diabetic Retinopathy Screening Systems. [2021]With rising global prevalence of diabetic retinopathy (DR), automated DR screening is needed for primary care settings. Two automated artificial intelligence (AI)-based DR screening algorithms have U.S. Food and Drug Administration (FDA) approval. Several others are under consideration while in clinical use in other countries, but their real-world performance has not been evaluated systematically. We compared the performance of seven automated AI-based DR screening algorithms (including one FDA-approved algorithm) against human graders when analyzing real-world retinal imaging data.
Efficacy of artificial intelligence-based screening for diabetic retinopathy in type 2 diabetes mellitus patients. [2022]To explore the efficacy of artificial intelligence (AI)-based screening for diabetic retinopathy (DR) in type 2 diabetes mellitus (T2DM) patients.