Feasibility and Patient Experience of a Pilot Artificial Intelligence-Based Diabetic Retinopathy Screening Program in Northern Ontario.

Abstract

Purpose: To assess the feasibility, implementation, and patient experience of autonomous artificial intelligence-based diabetic retinopathy detection models.

Methods: This was a prospective cohort study where consenting adult participants previously diagnosed with diabetes were screened for diabetic retinopathy using retinal imaging with autonomous artificial intelligence (AI) interpretation at their routine primary care appointment from December 2022 through October 2023 in Thunder Bay, Ontario. Demographic (age, sex, race) and clinical (type and duration of diabetes, last reported eye exam) data were collected using a data collection form. A 5-point Likert scale questionnaire was completed by participants to assess patient experience following the AI exam.

Results: Among the 202 participants (38.6% women) with a mean age of 70.8 ± 11.7 years included in the study and screened by AI, the exam was successfully completed by 93.6% (n = 189), with only 1.5% (n = 3) requiring dilating eyedrops. The most common reason for an unsuccessful exam was small pupils with patient refusal for dilating eyedrops (n = 4). Among the participants with successful eye exams, 22.2% (n = 42) had referable diabetic retinopathy detected and were referred to see an ophthalmologist; 32/42 (76.0%) of these attended their ophthalmologist appointment. A total of 184 participants completed the satisfaction questionnaire; the mean score (out of 5) for satisfaction with the addition of an eye exam to their primary care visit was 4.8 ± 0.6.

Conclusion: Screening for diabetic retinopathy using autonomous artificial intelligence in a primary care setting is feasible and acceptable. This approach has significant advantages for both physicians and patients while achieving very high patient satisfaction.