A Case for the Use of Artificial Intelligence in Glaucoma Assessment

Collaborative Community on Ophthalmic Imaging Executive Committee and Glaucoma Workgroup

doi:10.1016/j.ogla.2021.12.003

A Case for the Use of Artificial Intelligence in Glaucoma Assessment

Collaborative Community on Ophthalmic Imaging Executive Committee and Glaucoma Workgroup

School of Medicine

Research output: Contribution to journal › Article › peer-review

Abstract

We hypothesize that artificial intelligence (AI) applied to relevant clinical testing in glaucoma has the potential to enhance the ability to detect glaucoma. This premise was discussed at the recent Collaborative Community on Ophthalmic Imaging meeting, “The Future of Artificial Intelligence–Enabled Ophthalmic Image Interpretation: Accelerating Innovation and Implementation Pathways,” held virtually September 3–4, 2020. The Collaborative Community on Ophthalmic Imaging (CCOI) is an independent self-governing consortium of stakeholders with broad international representation from academic institutions, government agencies, and the private sector whose mission is to act as a forum for the purpose of helping speed innovation in healthcare technology. It was 1 of the first 2 such organizations officially designated by the Food and Drug Administration in September 2019 in response to their announcement of the collaborative community program as a strategic priority for 2018–2020. Further information on the CCOI can be found online at their website (https://www.cc-oi.org/about). Artificial intelligence for glaucoma diagnosis would have high utility globally, because access to care is limited in many parts of the world and half of all people with glaucoma are unaware of their illness. The application of AI technology to glaucoma diagnosis has the potential to broadly increase access to care worldwide, in essence flattening the Earth by providing expert-level evaluation to individuals even in the most remote regions of the planet.

Original language	English (US)
Pages (from-to)	e3-e13
Journal	Ophthalmology Glaucoma
Volume	5
Issue number	3
DOIs	https://doi.org/10.1016/j.ogla.2021.12.003
State	Published - May 1 2022

Keywords

Artificial intelligence
Deep learning
Glaucoma
Neural networks
OCT

ASJC Scopus subject areas

Ophthalmology

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10.1016/j.ogla.2021.12.003

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title = "A Case for the Use of Artificial Intelligence in Glaucoma Assessment",

abstract = "We hypothesize that artificial intelligence (AI) applied to relevant clinical testing in glaucoma has the potential to enhance the ability to detect glaucoma. This premise was discussed at the recent Collaborative Community on Ophthalmic Imaging meeting, “The Future of Artificial Intelligence–Enabled Ophthalmic Image Interpretation: Accelerating Innovation and Implementation Pathways,” held virtually September 3–4, 2020. The Collaborative Community on Ophthalmic Imaging (CCOI) is an independent self-governing consortium of stakeholders with broad international representation from academic institutions, government agencies, and the private sector whose mission is to act as a forum for the purpose of helping speed innovation in healthcare technology. It was 1 of the first 2 such organizations officially designated by the Food and Drug Administration in September 2019 in response to their announcement of the collaborative community program as a strategic priority for 2018–2020. Further information on the CCOI can be found online at their website (https://www.cc-oi.org/about). Artificial intelligence for glaucoma diagnosis would have high utility globally, because access to care is limited in many parts of the world and half of all people with glaucoma are unaware of their illness. The application of AI technology to glaucoma diagnosis has the potential to broadly increase access to care worldwide, in essence flattening the Earth by providing expert-level evaluation to individuals even in the most remote regions of the planet.",

keywords = "Artificial intelligence, Deep learning, Glaucoma, Neural networks, OCT",

author = "{Collaborative Community on Ophthalmic Imaging Executive Committee and Glaucoma Workgroup} and Schuman, {Joel S.} and {De Los Angeles Ramos Cadena}, Maria and Rebecca McGee and Al-Aswad, {Lama A.} and Medeiros, {Felipe A.} and Michael Abramoff and Mark Blumenkranz and Emily Chew and Michael Chiang and Malvina Eydelman and David Myung and Carol Shields and Antony, {Bhavna J.} and Tin Aung and Michael Boland and Tom Brunner and Chang, {Robert T.} and Balwantray Chauhan and Cherwek, {D. Hunter} and David Garway-Heath and Adrienne Graves and Goldberg, {Jeffrey L.} and Minguang He and Naama Hammel and Donald Hood and Hiroshi Ishikawa and Chris Leung and Louis Pasquale and Quigley, {Harry A.} and Roberts, {Calvin W.} and Robin, {Alan L.} and Elena Sturman and Remo Susanna and Jayme Vianna and Linda Zangwill",

note = "Publisher Copyright: {\textcopyright} 2022 American Academy of Ophthalmology",

year = "2022",

month = may,

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doi = "10.1016/j.ogla.2021.12.003",

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AU - Al-Aswad, Lama A.

AU - Medeiros, Felipe A.

AU - Abramoff, Michael

AU - Blumenkranz, Mark

AU - Chew, Emily

AU - Chiang, Michael

AU - Eydelman, Malvina

AU - Myung, David

AU - Shields, Carol

AU - Antony, Bhavna J.

AU - Aung, Tin

AU - Boland, Michael

AU - Brunner, Tom

AU - Chang, Robert T.

AU - Chauhan, Balwantray

AU - Cherwek, D. Hunter

AU - Garway-Heath, David

AU - Graves, Adrienne

AU - Goldberg, Jeffrey L.

AU - He, Minguang

AU - Hammel, Naama

AU - Hood, Donald

AU - Ishikawa, Hiroshi

AU - Leung, Chris

AU - Pasquale, Louis

AU - Quigley, Harry A.

AU - Roberts, Calvin W.

AU - Robin, Alan L.

AU - Sturman, Elena

AU - Susanna, Remo

AU - Vianna, Jayme

AU - Zangwill, Linda

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KW - OCT

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A Case for the Use of Artificial Intelligence in Glaucoma Assessment

Abstract

Keywords

ASJC Scopus subject areas

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