File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: The Definition of Glaucomatous Optic Neuropathy in Artificial Intelligence Research and Clinical Applications

TitleThe Definition of Glaucomatous Optic Neuropathy in Artificial Intelligence Research and Clinical Applications
Authors
KeywordsArtificial intelligence
Glaucoma
Glaucomatous optic neuropathy
Issue Date31-Jan-2023
PublisherElsevier
Citation
Ophthalmology Glaucoma, 2023, v. 6, n. 4, p. 432-438 How to Cite?
Abstract

Objective: Although artificial intelligence (AI) models may offer innovative and powerful ways to use the wealth of data generated by diagnostic tools, there are important challenges related to their development and validation. Most notable is the lack of a perfect reference standard for glaucomatous optic neuropathy (GON). Because AI models are trained to predict presence of glaucoma or its progression, they generally rely on a reference standard that is used to train the model and assess its validity. If an improper reference standard is used, the model may be trained to detect or predict something that has little or no clinical value. This article summarizes the issues and discussions related to the definition of GON in AI applications as presented by the Glaucoma Workgroup from the Collaborative Community for Ophthalmic Imaging (CCOI) US Food and Drug Administration Virtual Workshop, on September 3 and 4, 2020, and on January 28, 2022. Design: Review and conference proceedings. Subjects: No human or animal subjects or data therefrom were used in the production of this article. Methods: A summary of the Workshop was produced with input and approval from all participants. Main Outcome Measures: Consensus position of the CCOI Workgroup on the challenges in defining GON and possible solutions. Results: The Workshop reviewed existing challenges that arise from the use of subjective definitions of GON and highlighted the need for a more objective approach to characterize GON that could facilitate replication and comparability of AI studies and allow for better clinical validation of proposed AI tools. Different tests and combination of parameters for defining a reference standard for GON have been proposed. Different reference standards may need to be considered depending on the scenario in which the AI models are going to be applied, such as community-based or opportunistic screening versus detection or monitoring of glaucoma in tertiary care. Conclusions: The development and validation of new AI-based diagnostic tests should be based on rigorous methodology with clear determination of how the reference standards for glaucomatous damage are constructed and the settings where the tests are going to be applied. Financial Disclosure(s): Proprietary or commercial disclosure may be found after the references. Ophthalmology Glaucoma 2023;6:432-438 2023 by the American Academy of Ophthalmology


Persistent Identifierhttp://hdl.handle.net/10722/340879
ISSN
2023 Impact Factor: 2.8
2023 SCImago Journal Rankings: 1.266
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorMedeiros, Felipe A-
dc.contributor.authorLee, Terry-
dc.contributor.authorJammal, Alessandro A-
dc.contributor.authorAl-Aswad, Lama A-
dc.contributor.authorEydelman, Malvina B-
dc.contributor.authorSchuman, Joel S-
dc.contributor.authorAbramoff, Michael-
dc.contributor.authorBlumenkranz, Mark-
dc.contributor.authorChew, Emily-
dc.contributor.authorChiang, Michael-
dc.contributor.authorEydelman, Malvina-
dc.contributor.authorMyung, David-
dc.contributor.authorSchuman, Joel S-
dc.contributor.authorShields, Carol-
dc.contributor.authorAbramoff, Michael-
dc.contributor.authorAl-Aswad, Lama-
dc.contributor.authorAntony, Bhavna J-
dc.contributor.authorAung, Tin-
dc.contributor.authorBoland, Michael-
dc.contributor.authorBrunner, Tom-
dc.contributor.authorChang, Robert T-
dc.contributor.authorChauhan, Balwantray-
dc.contributor.authorChiang, Michael-
dc.contributor.authorCherwek, D Hunter-
dc.contributor.authorGarway-Heath, David-
dc.contributor.authorGraves, Adrienne-
dc.contributor.authorGoldberg, Jeffrey L-
dc.contributor.authorHe, Minguang-
dc.contributor.authorHammel, Naama-
dc.contributor.authorHood, Donald-
dc.contributor.authorIshikawa, Hiroshi-
dc.contributor.authorLeung, Chris-
dc.contributor.authorMedeiros, Felipe-
dc.contributor.authorPasquale, Louis R-
dc.contributor.authorQuigley, Harry A-
dc.contributor.authorRoberts, Calvin W-
dc.contributor.authorRobin, Alan L-
dc.contributor.authorSchuman, Joel S-
dc.contributor.authorSturman, Elena-
dc.contributor.authorSusanna, Remo-
dc.contributor.authorVianna, Jayme-
dc.contributor.authorZangwill, Linda -
dc.date.accessioned2024-03-11T10:47:59Z-
dc.date.available2024-03-11T10:47:59Z-
dc.date.issued2023-01-31-
dc.identifier.citationOphthalmology Glaucoma, 2023, v. 6, n. 4, p. 432-438-
dc.identifier.issn2589-4234-
dc.identifier.urihttp://hdl.handle.net/10722/340879-
dc.description.abstract<p>Objective: Although artificial intelligence (AI) models may offer innovative and powerful ways to use the wealth of data generated by diagnostic tools, there are important challenges related to their development and validation. Most notable is the lack of a perfect reference standard for glaucomatous optic neuropathy (GON). Because AI models are trained to predict presence of glaucoma or its progression, they generally rely on a reference standard that is used to train the model and assess its validity. If an improper reference standard is used, the model may be trained to detect or predict something that has little or no clinical value. This article summarizes the issues and discussions related to the definition of GON in AI applications as presented by the Glaucoma Workgroup from the Collaborative Community for Ophthalmic Imaging (CCOI) US Food and Drug Administration Virtual Workshop, on September 3 and 4, 2020, and on January 28, 2022. Design: Review and conference proceedings. Subjects: No human or animal subjects or data therefrom were used in the production of this article. Methods: A summary of the Workshop was produced with input and approval from all participants. Main Outcome Measures: Consensus position of the CCOI Workgroup on the challenges in defining GON and possible solutions. Results: The Workshop reviewed existing challenges that arise from the use of subjective definitions of GON and highlighted the need for a more objective approach to characterize GON that could facilitate replication and comparability of AI studies and allow for better clinical validation of proposed AI tools. Different tests and combination of parameters for defining a reference standard for GON have been proposed. Different reference standards may need to be considered depending on the scenario in which the AI models are going to be applied, such as community-based or opportunistic screening versus detection or monitoring of glaucoma in tertiary care. Conclusions: The development and validation of new AI-based diagnostic tests should be based on rigorous methodology with clear determination of how the reference standards for glaucomatous damage are constructed and the settings where the tests are going to be applied. Financial Disclosure(s): Proprietary or commercial disclosure may be found after the references. Ophthalmology Glaucoma 2023;6:432-438 2023 by the American Academy of Ophthalmology<br></p>-
dc.languageeng-
dc.publisherElsevier-
dc.relation.ispartofOphthalmology Glaucoma-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectArtificial intelligence-
dc.subjectGlaucoma-
dc.subjectGlaucomatous optic neuropathy-
dc.titleThe Definition of Glaucomatous Optic Neuropathy in Artificial Intelligence Research and Clinical Applications-
dc.typeArticle-
dc.identifier.doi10.1016/j.ogla.2023.01.007-
dc.identifier.scopuseid_2-s2.0-85150788401-
dc.identifier.volume6-
dc.identifier.issue4-
dc.identifier.spage432-
dc.identifier.epage438-
dc.identifier.eissn2589-4196-
dc.identifier.isiWOS:001047595200001-
dc.identifier.issnl2589-4196-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats