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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 scree...

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Autores principales: Shah, Payal, Mishra, Divyansh, Shanmugam, Mahesh, Vighnesh, M J, Jayaraj, Hariprasad
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer - Medknow 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9240537/
https://www.ncbi.nlm.nih.gov/pubmed/35326001
http://dx.doi.org/10.4103/ijo.IJO_1840_21
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author Shah, Payal
Mishra, Divyansh
Shanmugam, Mahesh
Vighnesh, M J
Jayaraj, Hariprasad
author_facet Shah, Payal
Mishra, Divyansh
Shanmugam, Mahesh
Vighnesh, M J
Jayaraj, Hariprasad
author_sort Shah, Payal
collection PubMed
description 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 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.
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spelling pubmed-92405372022-06-30 Acceptability of artificial intelligence-based retina screening in general population Shah, Payal Mishra, Divyansh Shanmugam, Mahesh Vighnesh, M J Jayaraj, Hariprasad Indian J Ophthalmol Featured Article, Artificial Intelligence, Original Article 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 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. Wolters Kluwer - Medknow 2022-04 2022-03-22 /pmc/articles/PMC9240537/ /pubmed/35326001 http://dx.doi.org/10.4103/ijo.IJO_1840_21 Text en Copyright: © 2022 Indian Journal of Ophthalmology https://creativecommons.org/licenses/by-nc-sa/4.0/This is an open access journal, and articles are distributed under the terms of the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as appropriate credit is given and the new creations are licensed under the identical terms.
spellingShingle Featured Article, Artificial Intelligence, Original Article
Shah, Payal
Mishra, Divyansh
Shanmugam, Mahesh
Vighnesh, M J
Jayaraj, Hariprasad
Acceptability of artificial intelligence-based retina screening in general population
title Acceptability of artificial intelligence-based retina screening in general population
title_full Acceptability of artificial intelligence-based retina screening in general population
title_fullStr Acceptability of artificial intelligence-based retina screening in general population
title_full_unstemmed Acceptability of artificial intelligence-based retina screening in general population
title_short Acceptability of artificial intelligence-based retina screening in general population
title_sort acceptability of artificial intelligence-based retina screening in general population
topic Featured Article, Artificial Intelligence, Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9240537/
https://www.ncbi.nlm.nih.gov/pubmed/35326001
http://dx.doi.org/10.4103/ijo.IJO_1840_21
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