Artificial Intelligence for Glaucoma: Creating and Implementing Artificial Intelligence for Disease Detection and Progression

Collaborative Community for Ophthalmic Imaging Executive Committee and Glaucoma Workgroup

doi:10.1016/j.ogla.2022.02.010

Artificial Intelligence for Glaucoma: Creating and Implementing Artificial Intelligence for Disease Detection and Progression

Collaborative Community for Ophthalmic Imaging Executive Committee and Glaucoma Workgroup

Medicine

Producción científica: Contribución a una revista › Artículo › revisión exhaustiva

17 Citas (Scopus)

Resumen

On September 3, 2020, the Collaborative Community on Ophthalmic Imaging conducted its first 2-day virtual workshop on the role of artificial intelligence (AI) and related machine learning techniques in the diagnosis and treatment of various ophthalmic conditions. In a session entitled “Artificial Intelligence for Glaucoma,” a panel of glaucoma specialists, researchers, industry experts, and patients convened to share current research on the application of AI to commonly used diagnostic modalities, including fundus photography,OCT imaging, standard automated perimetry, and gonioscopy. The conference participants focused on the use of AI as a tool for disease prediction, highlighted its ability to address inequalities, and presented the limitations of and challenges to its clinical application. The panelists’ discussion addressed AI and health equities from clinical, societal, and regulatory perspectives.

Idioma original	English
Publicación	Ophthalmology. Glaucoma
DOI	https://doi.org/10.1016/j.ogla.2022.02.010
Estado	Accepted/In press - 2022

Nota bibliográfica

Funding Information:
Supported by the National Institutes of Health (Bethesda, MD; R01-EY013178). An unrestricted grant from Research to Prevent Blindness (New York, NY) to the Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, New York.

Funding Information:
L.A.A.-A.: Consultant, Advisor – Aerie Pharmaceuticals, Inc; Equity owner – GlobeChek; Advisor – AI Optics, Zeiss; Grant support – New World Medical Inc, Save Vision Foundation; Research support, Consultant – Topcon Medical Systems Inc; Consultant – Verily.

Funding Information:
Supported by the National Institutes of Health (Bethesda, MD; R01-EY013178 ). An unrestricted grant from Research to Prevent Blindness (New York, NY) to the Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine , New York, New York.

Publisher Copyright:
© 2022 American Academy of Ophthalmology

ASJC Scopus Subject Areas

Ophthalmology

PubMed: MeSH publication types

Journal Article
Research Support, Non-U.S. Gov't
Research Support, N.I.H., Extramural

Acceder al documento

10.1016/j.ogla.2022.02.010

Otros archivos y enlaces

Citar esto

@article{f9a02f2af4ce470a96375c0c55b8f6fa,

title = "Artificial Intelligence for Glaucoma: Creating and Implementing Artificial Intelligence for Disease Detection and Progression",

abstract = "On September 3, 2020, the Collaborative Community on Ophthalmic Imaging conducted its first 2-day virtual workshop on the role of artificial intelligence (AI) and related machine learning techniques in the diagnosis and treatment of various ophthalmic conditions. In a session entitled “Artificial Intelligence for Glaucoma,” a panel of glaucoma specialists, researchers, industry experts, and patients convened to share current research on the application of AI to commonly used diagnostic modalities, including fundus photography,OCT imaging, standard automated perimetry, and gonioscopy. The conference participants focused on the use of AI as a tool for disease prediction, highlighted its ability to address inequalities, and presented the limitations of and challenges to its clinical application. The panelists{\textquoteright} discussion addressed AI and health equities from clinical, societal, and regulatory perspectives.",

author = "{Collaborative Community for Ophthalmic Imaging Executive Committee and Glaucoma Workgroup} and Al-Aswad, {Lama A.} and Rithambara Ramachandran and Schuman, {Joel S.} and Felipe Medeiros and Eydelman, {Malvina B.} and Abramoff, {Michael D.} and Antony, {Bhavna J.} and Boland, {Michael V.} and Chauhan, {Balwantray C.} and Michael Chiang and Goldberg, {Jeffrey L.} and Naama Hammel and Pasquale, {Louis R.} and Quigley, {Harry A.} and Remo Susanna and Jayme Vianna and Linda Zangwill",

note = "Funding Information: Supported by the National Institutes of Health (Bethesda, MD; R01-EY013178). An unrestricted grant from Research to Prevent Blindness (New York, NY) to the Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, New York. Funding Information: L.A.A.-A.: Consultant, Advisor – Aerie Pharmaceuticals, Inc; Equity owner – GlobeChek; Advisor – AI Optics, Zeiss; Grant support – New World Medical Inc, Save Vision Foundation; Research support, Consultant – Topcon Medical Systems Inc; Consultant – Verily. Funding Information: Supported by the National Institutes of Health (Bethesda, MD; R01-EY013178 ). An unrestricted grant from Research to Prevent Blindness (New York, NY) to the Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine , New York, New York. Publisher Copyright: {\textcopyright} 2022 American Academy of Ophthalmology",

year = "2022",

doi = "10.1016/j.ogla.2022.02.010",

language = "English",

journal = "Ophthalmology. Glaucoma",

issn = "2589-4234",

publisher = "Elsevier BV",

}

TY - JOUR

T1 - Artificial Intelligence for Glaucoma

T2 - Creating and Implementing Artificial Intelligence for Disease Detection and Progression

AU - Collaborative Community for Ophthalmic Imaging Executive Committee and Glaucoma Workgroup

AU - Al-Aswad, Lama A.

AU - Ramachandran, Rithambara

AU - Schuman, Joel S.

AU - Medeiros, Felipe

AU - Eydelman, Malvina B.

AU - Abramoff, Michael D.

AU - Antony, Bhavna J.

AU - Boland, Michael V.

AU - Chauhan, Balwantray C.

AU - Chiang, Michael

AU - Goldberg, Jeffrey L.

AU - Hammel, Naama

AU - Pasquale, Louis R.

AU - Quigley, Harry A.

AU - Susanna, Remo

AU - Vianna, Jayme

AU - Zangwill, Linda

N1 - Funding Information: Supported by the National Institutes of Health (Bethesda, MD; R01-EY013178). An unrestricted grant from Research to Prevent Blindness (New York, NY) to the Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine, New York, New York. Funding Information: L.A.A.-A.: Consultant, Advisor – Aerie Pharmaceuticals, Inc; Equity owner – GlobeChek; Advisor – AI Optics, Zeiss; Grant support – New World Medical Inc, Save Vision Foundation; Research support, Consultant – Topcon Medical Systems Inc; Consultant – Verily. Funding Information: Supported by the National Institutes of Health (Bethesda, MD; R01-EY013178 ). An unrestricted grant from Research to Prevent Blindness (New York, NY) to the Department of Ophthalmology, NYU Langone Health, NYU Grossman School of Medicine , New York, New York. Publisher Copyright: © 2022 American Academy of Ophthalmology

PY - 2022

Y1 - 2022

N2 - On September 3, 2020, the Collaborative Community on Ophthalmic Imaging conducted its first 2-day virtual workshop on the role of artificial intelligence (AI) and related machine learning techniques in the diagnosis and treatment of various ophthalmic conditions. In a session entitled “Artificial Intelligence for Glaucoma,” a panel of glaucoma specialists, researchers, industry experts, and patients convened to share current research on the application of AI to commonly used diagnostic modalities, including fundus photography,OCT imaging, standard automated perimetry, and gonioscopy. The conference participants focused on the use of AI as a tool for disease prediction, highlighted its ability to address inequalities, and presented the limitations of and challenges to its clinical application. The panelists’ discussion addressed AI and health equities from clinical, societal, and regulatory perspectives.

AB - On September 3, 2020, the Collaborative Community on Ophthalmic Imaging conducted its first 2-day virtual workshop on the role of artificial intelligence (AI) and related machine learning techniques in the diagnosis and treatment of various ophthalmic conditions. In a session entitled “Artificial Intelligence for Glaucoma,” a panel of glaucoma specialists, researchers, industry experts, and patients convened to share current research on the application of AI to commonly used diagnostic modalities, including fundus photography,OCT imaging, standard automated perimetry, and gonioscopy. The conference participants focused on the use of AI as a tool for disease prediction, highlighted its ability to address inequalities, and presented the limitations of and challenges to its clinical application. The panelists’ discussion addressed AI and health equities from clinical, societal, and regulatory perspectives.

UR - http://www.scopus.com/inward/record.url?scp=85129549669&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85129549669&partnerID=8YFLogxK

U2 - 10.1016/j.ogla.2022.02.010

DO - 10.1016/j.ogla.2022.02.010

M3 - Article

C2 - 35218987

AN - SCOPUS:85129549669

SN - 2589-4234

JO - Ophthalmology. Glaucoma

JF - Ophthalmology. Glaucoma

ER -

Artificial Intelligence for Glaucoma: Creating and Implementing Artificial Intelligence for Disease Detection and Progression

Resumen

Nota bibliográfica

ASJC Scopus Subject Areas

PubMed: MeSH publication types

Acceder al documento

Otros archivos y enlaces

Huella

Citar esto