AI Screening for Vision Loss from Diabetes
Recruiting in Palo Alto (17 mi)
Overseen byRoomasa Channa
Age: 18+
Sex: Any
Travel: May Be Covered
Time Reimbursement: Varies
Trial Phase: Academic
Recruiting
Sponsor: University of Wisconsin, Madison
Disqualifiers: Diabetic eye disease, others
No Placebo Group
Trial Summary
What is the purpose of this trial?This study aims to investigate whether a novel artificial intelligence based screening strategy (AI-Based point of caRe, Incorporating Diagnosis, SchedulinG, and Education or AI-BRIDGE), which allows primary care providers to screen patients for vision-threatening diabetic eye disease in the primary care clinic, improves screening and follow-up care rates across race/ethnicity groups and reduces racial/ethnic disparities in screening.
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 AI-BRIDGE for vision loss from diabetes?
Research shows that artificial intelligence (AI) systems are highly promising for detecting diabetic retinopathy (a diabetes-related eye disease) from eye images and may predict its progression. These AI tools have been validated in various settings, demonstrating accuracy in identifying eye conditions related to diabetes.
12345Is the AI system for detecting diabetic retinopathy safe for humans?
The AI systems for detecting diabetic retinopathy, like AIDRScreening and others, have been tested in various studies and are generally considered safe for use in humans, as they involve analyzing images of the eye without direct physical intervention.
56789How is the AI-BRIDGE treatment for vision loss from diabetes different from other treatments?
AI-BRIDGE is unique because it uses artificial intelligence to screen for vision loss due to diabetes, specifically targeting diabetic retinopathy (damage to the retina caused by diabetes) and potentially other eye conditions, which is different from traditional methods that rely on manual examination by healthcare professionals.
35101112Eligibility Criteria
This trial is for individuals with diabetes who are at risk of vision loss. It's focused on helping those in socioeconomically disadvantaged communities. Participants should be willing to undergo screening using the AI-BRIDGE system in a primary care setting.Inclusion Criteria
I do not have any eye problems caused by diabetes.
Not had an eye exam in the prior year
I am older than 21 years.
+2 more
Trial Timeline
Screening
Participants are screened for eligibility to participate in the trial
2-4 weeks
AI-BRIDGE Implementation
AI-based eye screening program called AI-BRIDGE is implemented. Eye photos are obtained and reviewed using an AI algorithm. Patients with referrable diabetic retinopathy are detected and assisted with scheduling follow-up visits.
6 months
Regular primary care visits
Usual Care Screening
Primary care providers refer patients with diabetes to an eye care provider for a dilated eye exam. Patients receive educational materials.
6 months
Follow-up
Participants are monitored for follow-up with recommended eye care and screening effectiveness.
up to 6 months
Participant Groups
The study tests an artificial intelligence-based strategy, AI-BRIDGE, designed to help doctors screen for diabetic eye disease during regular visits. The goal is to see if it improves screening rates and reduces racial/ethnic disparities.
2Treatment groups
Experimental Treatment
Active Control
Group I: AI-BRIDGEExperimental Treatment1 Intervention
AI-based eye screening program called AI-Based point of caRe, Incorporating Diagnosis, SchedulinG, and Education (AI-BRIDGE). Eye photos of the patients will be obtained in the primary care clinic during a patient's regular primary care visit by a trained technician. Images will be reviewed using autonomous artificial-intelligence (AI) algorithm (Digital Diagnostics). Patients with referrable diabetic retinopathy are detected, and assisted with scheduling an in-person follow-up eye care visits. All patients irrespective of diabetic retinopathy status are also provided culturally adapted educational material on diabetic eye disease.
Group II: Usual Care ScreeningActive Control1 Intervention
Primary care providers refer patients with diabetes to an eye care provider for a dilated eye exam. Patients are provided with culturally adapted diabetic eye disease educational materials similar to that provided to patients in the AI-BRIDGE group.
Find a Clinic Near You
Research Locations NearbySelect from list below to view details:
UW School of Medicine and Public HealthMadison, WI
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Who Is Running the Clinical Trial?
University of Wisconsin, MadisonLead Sponsor
National Eye Institute (NEI)Collaborator
References
Artificial Intelligence in the assessment of diabetic retinopathy from fundus photographs. [2021]Background: Over the next 25 years, the global prevalence of diabetes is expected to grow to affect 700 million individuals. Consequently, an unprecedented number of patients will be at risk for vision loss from diabetic eye disease. This demand will almost certainly exceed the supply of eye care professionals to individually evaluate each patient on an annual basis, signaling the need for 21st century tools to assist our profession in meeting this challenge. Methods: Review of available literature on artificial intelligence (AI) as applied to diabetic retinopathy (DR) detection and predictionResults: The field of AI has seen exponential growth in evaluating fundus photographs for DR. AI systems employ machine learning and artificial neural networks to teach themselves how to grade DR from libraries of tens of thousands of images and may be able to predict future DR progression based on baseline fundus photographs. Conclusions: AI algorithms are highly promising for the purposes of DR detection and will likely be able to reliably predict DR worsening in the future. A deeper understanding of these systems and how they interpret images is critical as they transition from the bench into the clinic.
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.
Validating automated eye disease screening AI algorithm in community and in-hospital scenarios. [2022]To assess the accuracy and robustness of the AI algorithm for detecting referable diabetic retinopathy (RDR), referable macular diseases (RMD), and glaucoma suspect (GCS) from fundus images in community and in-hospital screening scenarios.
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.
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.
Comparison of early diabetic retinopathy staging in asymptomatic patients between autonomous AI-based screening and human-graded ultra-widefield colour fundus images. [2023]Comparison of diabetic retinopathy (DR) severity between autonomous Artificial Intelligence (AI)-based outputs from an FDA-approved screening system and human retina specialists' gradings from ultra-widefield (UWF) colour images.
External validation of a deep learning detection system for glaucomatous optic neuropathy: a real-world multicentre study. [2023]To conduct an external validation of an automated artificial intelligence (AI) diagnostic system using fundus photographs from a real-life multicentre cohort.
Automated diabetic retinopathy screening for primary care settings using deep learning. [2022]Diabetic Retinopathy (DR) is one of the leading causes of blindness in the United States and other high-income countries. Early detection is key to prevention, which could be achieved effectively with a fully automated screening tool performing well on clinically relevant measures in primary care settings. We have built an artificial intelligence-based tool on a cloud-based platform for large-scale screening of DR as referable or non-referable. In this paper, we aim to validate this tool built using deep learning based techniques. The cloud-based screening model was developed and tested using deep learning techniques with 88702 images from the Kaggle dataset and externally validated using 1748 high-resolution images of the retina (or fundus images) from the Messidor-2 dataset. For validation in the primary care settings, 264 images were taken prospectively from two diabetes clinics in Queens, New York. The images were uploaded to the cloud-based software for testing the automated system as compared to expert ophthalmologists' evaluations of referable DR. Measures used were area under the curve (AUC), sensitivity, and specificity of the screening model with respect to professional graders. The screening system achieved a high sensitivity of 99.21% and a specificity of 97.59% on the Kaggle test dataset with an AUC of 0.9992. The system was also externally validated in Messidor-2, where it achieved a sensitivity of 97.63% and a specificity of 99.49% (AUC, 0.9985). On primary care data, the sensitivity was 92.3% overall (12/13 referable images are correctly identified), and overall specificity was 94.8% (233/251 non-referable images). The proposed DR screening tool achieves state-of-the-art performance among the publicly available datasets: Kaggle and Messidor-2 to the best of our knowledge. The performance on various clinically relevant measures demonstrates that the tool is suitable for screening and early diagnosis of DR in primary care settings.
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.
Evaluation of an Artificial Intelligence System for the Detection of Diabetic Retinopathy in Chinese Community Healthcare Centers. [2022]To evaluate the sensitivity and specificity of a Comprehensive Artificial Intelligence Retinal Expert (CARE) system for detecting diabetic retinopathy (DR) in a Chinese community population.
Simultaneous screening and classification of diabetic retinopathy and age-related macular degeneration based on fundus photos-a prospective analysis of the RetCAD system. [2022]To assess the accuracy of an artificial intelligence (AI) based software (RetCAD, Thirona, The Netherlands) to identify and grade age-related macular degeneration (AMD) and diabetic retinopathy (DR) simultaneously based on fundus photos.