Cargando…
Artificial intelligence for ocular oncology
The aim of this article is to provide an update on the latest applications of deep learning (DL) and classical machine learning (ML) techniques to the detection and prognostication of intraocular and ocular surface malignancies. RECENT FINDINGS: Most recent studies focused on using DL and classical...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Lippincott Williams & Wilkins
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10399931/ https://www.ncbi.nlm.nih.gov/pubmed/37326226 http://dx.doi.org/10.1097/ICU.0000000000000982 |
_version_ | 1785084356258168832 |
---|---|
author | Koseoglu, Neslihan Dilruba Corrêa, Zélia Maria Liu, T.Y. Alvin |
author_facet | Koseoglu, Neslihan Dilruba Corrêa, Zélia Maria Liu, T.Y. Alvin |
author_sort | Koseoglu, Neslihan Dilruba |
collection | PubMed |
description | The aim of this article is to provide an update on the latest applications of deep learning (DL) and classical machine learning (ML) techniques to the detection and prognostication of intraocular and ocular surface malignancies. RECENT FINDINGS: Most recent studies focused on using DL and classical ML techniques for prognostication purposes in patients with uveal melanoma (UM). SUMMARY: DL has emerged as the leading ML technique for prognostication in ocular oncological conditions, particularly in UM. However, the application of DL may be limited by the relatively rarity of these conditions. |
format | Online Article Text |
id | pubmed-10399931 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Lippincott Williams & Wilkins |
record_format | MEDLINE/PubMed |
spelling | pubmed-103999312023-08-04 Artificial intelligence for ocular oncology Koseoglu, Neslihan Dilruba Corrêa, Zélia Maria Liu, T.Y. Alvin Curr Opin Ophthalmol ARTIFICIAL INTELLIGENCE/BIG DATA: Edited by Daniel Ting and Ehsan Rahimy The aim of this article is to provide an update on the latest applications of deep learning (DL) and classical machine learning (ML) techniques to the detection and prognostication of intraocular and ocular surface malignancies. RECENT FINDINGS: Most recent studies focused on using DL and classical ML techniques for prognostication purposes in patients with uveal melanoma (UM). SUMMARY: DL has emerged as the leading ML technique for prognostication in ocular oncological conditions, particularly in UM. However, the application of DL may be limited by the relatively rarity of these conditions. Lippincott Williams & Wilkins 2023-09 2023-06-19 /pmc/articles/PMC10399931/ /pubmed/37326226 http://dx.doi.org/10.1097/ICU.0000000000000982 Text en Copyright © 2023 The Author(s). Published by Wolters Kluwer Health, Inc. https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution-Non Commercial-No Derivatives License 4.0 (CCBY-NC-ND), where it is permissible to download and share the work provided it is properly cited. The work cannot be changed in any way or used commercially without permission from the journal. http://creativecommons.org/licenses/by-nc-nd/4.0 (https://creativecommons.org/licenses/by-nc-nd/4.0/) . |
spellingShingle | ARTIFICIAL INTELLIGENCE/BIG DATA: Edited by Daniel Ting and Ehsan Rahimy Koseoglu, Neslihan Dilruba Corrêa, Zélia Maria Liu, T.Y. Alvin Artificial intelligence for ocular oncology |
title | Artificial intelligence for ocular oncology |
title_full | Artificial intelligence for ocular oncology |
title_fullStr | Artificial intelligence for ocular oncology |
title_full_unstemmed | Artificial intelligence for ocular oncology |
title_short | Artificial intelligence for ocular oncology |
title_sort | artificial intelligence for ocular oncology |
topic | ARTIFICIAL INTELLIGENCE/BIG DATA: Edited by Daniel Ting and Ehsan Rahimy |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10399931/ https://www.ncbi.nlm.nih.gov/pubmed/37326226 http://dx.doi.org/10.1097/ICU.0000000000000982 |
work_keys_str_mv | AT koseogluneslihandilruba artificialintelligenceforocularoncology AT correazeliamaria artificialintelligenceforocularoncology AT liutyalvin artificialintelligenceforocularoncology |