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Portable hardware & software technologies for addressing ophthalmic health disparities: A systematic review

Vision impairment continues to be a major global problem, as the WHO estimates 2.2 billion people struggling with vision loss or blindness. One billion of these cases, however, can be prevented by expanding diagnostic capabilities. Direct global healthcare costs associated with these conditions tota...

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Autores principales: Labkovich, Margarita, Paul, Megan, Kim, Eliott, A. Serafini, Randal, Lakhtakia, Shreyas, Valliani, Aly A, Warburton, Andrew J, Patel, Aashay, Zhou, Davis, Sklar, Bonnie, Chelnis, James, Elahi, Ebrahim
Formato: Online Artículo Texto
Lenguaje:English
Publicado: SAGE Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9087242/
https://www.ncbi.nlm.nih.gov/pubmed/35558637
http://dx.doi.org/10.1177/20552076221090042
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author Labkovich, Margarita
Paul, Megan
Kim, Eliott
A. Serafini, Randal
Lakhtakia, Shreyas
Valliani, Aly A
Warburton, Andrew J
Patel, Aashay
Zhou, Davis
Sklar, Bonnie
Chelnis, James
Elahi, Ebrahim
author_facet Labkovich, Margarita
Paul, Megan
Kim, Eliott
A. Serafini, Randal
Lakhtakia, Shreyas
Valliani, Aly A
Warburton, Andrew J
Patel, Aashay
Zhou, Davis
Sklar, Bonnie
Chelnis, James
Elahi, Ebrahim
author_sort Labkovich, Margarita
collection PubMed
description Vision impairment continues to be a major global problem, as the WHO estimates 2.2 billion people struggling with vision loss or blindness. One billion of these cases, however, can be prevented by expanding diagnostic capabilities. Direct global healthcare costs associated with these conditions totaled $255 billion in 2010, with a rapid upward projection to $294 billion in 2020. Accordingly, WHO proposed 2030 targets to enhance integration and patient-centered vision care by expanding refractive error and cataract worldwide coverage. Due to the limitations in cost and portability of adapted vision screening models, there is a clear need for new, more accessible vision testing tools in vision care. This comparative, systematic review highlights the need for new ophthalmic equipment and approaches while looking at existing and emerging technologies that could expand the capacity for disease identification and access to diagnostic tools. Specifically, the review focuses on portable hardware- and software-centered strategies that can be deployed in remote locations for detection of ophthalmic conditions and refractive error. Advancements in portable hardware, automated software screening tools, and big data-centric analytics, including machine learning, may provide an avenue for improving ophthalmic healthcare.
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spelling pubmed-90872422022-05-11 Portable hardware & software technologies for addressing ophthalmic health disparities: A systematic review Labkovich, Margarita Paul, Megan Kim, Eliott A. Serafini, Randal Lakhtakia, Shreyas Valliani, Aly A Warburton, Andrew J Patel, Aashay Zhou, Davis Sklar, Bonnie Chelnis, James Elahi, Ebrahim Digit Health Review Article Vision impairment continues to be a major global problem, as the WHO estimates 2.2 billion people struggling with vision loss or blindness. One billion of these cases, however, can be prevented by expanding diagnostic capabilities. Direct global healthcare costs associated with these conditions totaled $255 billion in 2010, with a rapid upward projection to $294 billion in 2020. Accordingly, WHO proposed 2030 targets to enhance integration and patient-centered vision care by expanding refractive error and cataract worldwide coverage. Due to the limitations in cost and portability of adapted vision screening models, there is a clear need for new, more accessible vision testing tools in vision care. This comparative, systematic review highlights the need for new ophthalmic equipment and approaches while looking at existing and emerging technologies that could expand the capacity for disease identification and access to diagnostic tools. Specifically, the review focuses on portable hardware- and software-centered strategies that can be deployed in remote locations for detection of ophthalmic conditions and refractive error. Advancements in portable hardware, automated software screening tools, and big data-centric analytics, including machine learning, may provide an avenue for improving ophthalmic healthcare. SAGE Publications 2022-05-06 /pmc/articles/PMC9087242/ /pubmed/35558637 http://dx.doi.org/10.1177/20552076221090042 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by-nc-nd/4.0/This article is distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 License (https://creativecommons.org/licenses/by-nc-nd/4.0/) which permits non-commercial use, reproduction and distribution of the work as published without adaptation or alteration, without further permission provided the original work is attributed as specified on the SAGE and Open Access page (https://us.sagepub.com/en-us/nam/open-access-at-sage).
spellingShingle Review Article
Labkovich, Margarita
Paul, Megan
Kim, Eliott
A. Serafini, Randal
Lakhtakia, Shreyas
Valliani, Aly A
Warburton, Andrew J
Patel, Aashay
Zhou, Davis
Sklar, Bonnie
Chelnis, James
Elahi, Ebrahim
Portable hardware & software technologies for addressing ophthalmic health disparities: A systematic review
title Portable hardware & software technologies for addressing ophthalmic health disparities: A systematic review
title_full Portable hardware & software technologies for addressing ophthalmic health disparities: A systematic review
title_fullStr Portable hardware & software technologies for addressing ophthalmic health disparities: A systematic review
title_full_unstemmed Portable hardware & software technologies for addressing ophthalmic health disparities: A systematic review
title_short Portable hardware & software technologies for addressing ophthalmic health disparities: A systematic review
title_sort portable hardware & software technologies for addressing ophthalmic health disparities: a systematic review
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9087242/
https://www.ncbi.nlm.nih.gov/pubmed/35558637
http://dx.doi.org/10.1177/20552076221090042
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