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Acceptability of artificial intelligence-based retina screening in general population
Authors:Payal Shah  Divyansh Mishra  Mahesh Shanmugam  M J Vighnesh  Hariprasad Jayaraj
Affiliation:Department of Vitreoretinal Services, Sankara Eye Hospital, Bengaluru, Karnataka, India;1.Sankara College of Optometry, Bengaluru, Karnataka, India;2.Lebencare Technologies Private Limited, Singapore
Abstract:Purpose:A deep learning system (DLS) using artificial intelligence (AI) is emerging as a very promising technology in the future of healthcare diagnostics. While the concept of telehealth is emerging in every field of medicine, AI assistance in diagnosis can become a great tool for successful screening in telemedicine and teleophthalmology. The aim of our study was to assess the acceptability of AI-based retina screening.Methods:This was a prospective non-randomized study performed in the outpatient department of a tertiary eye care hospital. Patients older than 18 years who came for a regular eye check-up or a routine retina screening were recruited in the study. Fundus images of the posterior pole were captured on fundus on a phone camera (REMIDIO™, India) with a built-in AI software (Netra.AI) that can identify normal versus abnormal retina. The patients were then given an 8-point questionnaire to assess their acceptance and willingness toward AI-based screening. We recruited 104 participants.Results:We found that 90.4% were willing for an AI-based fundus screening; 96.2% were satisfied with AI-based screening. Patients with diabetes (P = 0.03) and the male population (P = 0.029) were more satisfied with the AI-based screening. The majority (i.e., 97.1%) felt that AI-based screening gave them a better understanding of their eye condition and 37.5% felt that AI-based retina screening prior to a doctor’s visit can help in routine screeningConclusion:Considering the current COVID-19 pandemic situation across the globe, this study highlights the importance of AI-based telescreening and positive patient approach toward this technology.
Keywords:Acceptance   artificial intelligence   deep learning in retina   retina screening
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