Acceptability of artificial intelligence-based retina screening in general population.

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 (REMIDIOTM, 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 screening. Conclusion: 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.

A questionnaire-based assessment of Safe Eye Examination (SEE) technique.

PURPOSE: This study aims to assess the novel and innovative method of Safe Eye Examination (SEE) technique using the model eye for the purpose of teaching, training, and resident examination. METHODS: A questionnaire-based study (Descriptive Data) with 53 participants, including ophthalmology residents, fellows in various subspecialties, and trainee optometrists was used. In our study, we used the Reti Eye model, but instead of the usual retina template sheet, we used prominent pathological fundus photographs loaded into the model eye. The study participants were asked to view prominent pathological fundus images printed on a matte finish paper. A circular image was cut and was placed in the Reti Eye model. The candidates were made to perform indirect ophthalmoscopy with a + 20D lens and to fill up a Google image assessment scale questionnaire with characteristics, including pixelation, sharpness, contrast, reflexes, blotchy appearance, and diagnostic confidence, which were then analyzed and depicted. Association between categorical variables was analyzed using Fisher exact test and Chi-square test. A P value of less than 0.05 was considered statistically significant. All data were analyzed with a statistical software package (SPSS, Version 16.0 for Windows). RESULTS: The number of positive responses (>90%) obtained for the pixelation, sharpness, contrast, reflexes, blotchy appearance, and diagnostic confidence of the image viewed were statistically more significant than the negative responses (P < 0.05). CONCLUSION: The SEE technique of using the model eye can be incorporated for teaching, training, and skill assessment in the examinations in these difficult times of COVID-19 (coronavirus disease 2019) pandemic.