Artificial intelligence for glaucoma : state of the art and future perspectives
Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved..
PURPOSE OF REVIEW: To address the current role of artificial intelligence (AI) in the field of glaucoma.
RECENT FINDINGS: Current deep learning (DL) models concerning glaucoma diagnosis have shown consistently improving diagnostic capabilities, primarily based on color fundus photography and optical coherence tomography, but also with multimodal strategies. Recent models have also suggested that AI may be helpful in detecting and estimating visual field progression from different input data. Moreover, with the emergence of newer DL architectures and synthetic data, challenges such as model generalizability and explainability have begun to be tackled.
SUMMARY: While some challenges remain before AI is routinely employed in clinical practice, new research has expanded the range in which it can be used in the context of glaucoma management and underlined the relevance of this research avenue.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:35 |
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| Enthalten in: |
Current opinion in ophthalmology - 35(2024), 2 vom: 01. Feb., Seite 104-110 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Correia Barão, Rafael [VerfasserIn] |
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| Links: |
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| Anmerkungen: |
Date Completed 08.02.2024 Date Revised 08.02.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1097/ICU.0000000000001022 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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NLM365099600 |
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| 520 | |a Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved. | ||
| 520 | |a PURPOSE OF REVIEW: To address the current role of artificial intelligence (AI) in the field of glaucoma | ||
| 520 | |a RECENT FINDINGS: Current deep learning (DL) models concerning glaucoma diagnosis have shown consistently improving diagnostic capabilities, primarily based on color fundus photography and optical coherence tomography, but also with multimodal strategies. Recent models have also suggested that AI may be helpful in detecting and estimating visual field progression from different input data. Moreover, with the emergence of newer DL architectures and synthetic data, challenges such as model generalizability and explainability have begun to be tackled | ||
| 520 | |a SUMMARY: While some challenges remain before AI is routinely employed in clinical practice, new research has expanded the range in which it can be used in the context of glaucoma management and underlined the relevance of this research avenue | ||
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| 700 | 1 | |a Stalmans, Ingeborg |e verfasserin |4 aut | |
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