Schuman, Joel S;
De Los Angeles Ramos Cadena, Maria;
McGee, Rebecca;
Al-Aswad, Lama A;
Medeiros, Felipe A;
7, Collaborative Community for Ophthalmic Imaging Executive Comm, .;
Blumenkranz, M;
... Abramoff, M; + view all
(2022)
A Case for the Use of Artificial Intelligence in Glaucoma Assessment.
Ophthalmology Glaucoma
, 5
(3)
E3-E13.
10.1016/j.ogla.2021.12.003.
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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.
| Type: | Article |
|---|---|
| Title: | A Case for the Use of Artificial Intelligence in Glaucoma Assessment |
| Location: | United States |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1016/j.ogla.2021.12.003 |
| Publisher version: | https://doi.org/10.1016/j.ogla.2021.12.003 |
| Language: | English |
| Additional information: | This version is the author-accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
| Keywords: | Artificial intelligence, Deep learning, Glaucoma, Neural networks, OCT |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Institute of Ophthalmology |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10207312 |
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