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Open Access 26-07-2023 | Macular Degeneration | REVIEW

Deep Learning Applications to Classification and Detection of Age-Related Macular Degeneration on Optical Coherence Tomography Imaging: A Review

Authors: Neslihan Dilruba Koseoglu, Andrzej Grzybowski, T. Y. Alvin Liu

Published in: Ophthalmology and Therapy | Issue 5/2023

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Abstract

Age-related macular degeneration (AMD) is one of the leading causes of blindness in the elderly, more commonly in developed countries. Optical coherence tomography (OCT) is a non-invasive imaging device widely used for the diagnosis and management of AMD. Deep learning (DL) uses multilayered artificial neural networks (NN) for feature extraction, and is the cutting-edge technique for medical image analysis for diagnostic and prognostication purposes. Application of DL models to OCT image analysis has garnered significant interest in recent years. In this review, we aimed to summarize studies focusing on DL models used in classification and detection of AMD. Additionally, we provide a brief introduction to other DL applications in AMD, such as segmentation, prediction/prognostication, and models trained on multimodal imaging.
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Metadata
Title
Deep Learning Applications to Classification and Detection of Age-Related Macular Degeneration on Optical Coherence Tomography Imaging: A Review
Authors
Neslihan Dilruba Koseoglu
Andrzej Grzybowski
T. Y. Alvin Liu
Publication date
26-07-2023
Publisher
Springer Healthcare
Published in
Ophthalmology and Therapy / Issue 5/2023
Print ISSN: 2193-8245
Electronic ISSN: 2193-6528
DOI
https://doi.org/10.1007/s40123-023-00775-0

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