A Cross-Sectional Survey of Optometrists in Canada Regarding Referral Patterns and a Needs Assessment for an Artificial Intelligence Referral Screening Tool for Epiretinal Membrane.

Background and objective: This study evaluated optometrists' referral patterns for epiretinal membrane (ERM) patients in Ontario, Canada, and their attitudes towards an artificial intelligence (AI) tool for improving referral accuracy. An anonymous online survey with 11 questions was conducted.

Patients and methods: The survey targeted optometrists across Ontario, Canada. The survey aimed to understand optometrists' reasons for referring ERM patients to retina specialists, their expectations of the specialists' management, and their openness to using an AI tool for triage. To prevent bias, the survey described an AI tool as an online consultation feature limited to predefined questions without directly mentioning "AI." The main objective was to assess if this AI tool could decrease unnecessary ERM referrals to retina specialists.

Results: A total of 135 optometrists participated. They reported seeing an average of eight ERM cases monthly, referring every fourth case to a specialist. The primary referral reason (84.3%) was to evaluate for surgery. In terms of referral confidence, 34.3% felt fully confident (5/5), and 47.8% slightly less so (4/5). They anticipated that 20% of patients would have a change in management post-consultation with a specialist. When introduced to the concept of an online consultation tool for patient screening, optometrists believed it could reduce their ERM referrals by 40%.

Conclusions: Optometrists often refer ERM patients to retina specialists. An AI tool for screening ERM referrals, based on presenting vision and OCT images, could significantly lower the number of unnecessary referrals, offering clinical guidance to optometrists. [Ophthalmic Surg Lasers Imaging Retina 2025;56:166-169.].