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Japanese Journal of Ophthalmology
Erschienen in: Japanese Journal of Ophthalmology 5/2023

04.08.2023 | Forefront Review

Prediction of visual field progression in glaucoma: existing methods and artificial intelligence

verfasst von: Ryo Asaoka, Hiroshi Murata

Erschienen in: Japanese Journal of Ophthalmology | Ausgabe 5/2023

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Abstract

Timely treatment is essential in the management of glaucoma. However, subjective assessment of visual field (VF) progression is not recommended, because it can be unreliable. There are two types of artificial intelligence (AI) strong and weak (machine learning). Weak AIs can perform specific tasks. Linear regression is a method of weak AI. Using linear regression in the real-world clinic has enabled analyzing and predicting VF progression. However, caution is still required when interpreting the results, because whenever the number of VF data sets investigated is small, the predictions can be inaccurate. Several other non-ordinal, or modern AI methods have been constructed to improve prediction accuracy, such as clustering and more modern AI methods of Analysis with Non-Stationary Weibull Error Regression and Spatial Enhancement (ANSWERS), Variational Bayes Linear Regression (VBLR), Kalman Filter and sparse modeling (The least absolute shrinkage and selection operator regression: Lasso). It is also possible to improve the prediction performance using retinal thickness measured with optical coherence tomography by using machine learning methods, such as multitask learning.
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Metadaten
Titel
Prediction of visual field progression in glaucoma: existing methods and artificial intelligence
verfasst von
Ryo Asaoka
Hiroshi Murata
Publikationsdatum
04.08.2023
Verlag
Springer Japan
Erschienen in
Japanese Journal of Ophthalmology / Ausgabe 5/2023
Print ISSN: 0021-5155
Elektronische ISSN: 1613-2246
DOI
https://doi.org/10.1007/s10384-023-01009-3

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