Deep learning: applications in retinal and optic nerve diseases

Deep learning (DL) represents a paradigm-shifting, burgeoning field of research with emerging clinical applications in optometry. Unlike traditional programming, which relies on human-set specific rules, DL works by exposing the algorithm to a large amount of annotated data and allowing the software...

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Bibliographic Details
Published in:Clinical and experimental optometry 2023-07, Vol.106 (5), p.466-475
Main Authors: Charng, Jason, Alam, Khyber, Swartz, Gavin, Kugelman, Jason, Alonso-Caneiro, David, Mackey, David A, Chen, Fred K
Format: Article
Language:English
Subjects:
Artificial intelligence
Deep Learning
Eye diseases
glaucoma
Humans
Optic nerve
Optic Nerve Diseases - therapy
retinal disease
Retinal Ganglion Cells
Visual Field Tests
Visual Fields
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Summary:Deep learning (DL) represents a paradigm-shifting, burgeoning field of research with emerging clinical applications in optometry. Unlike traditional programming, which relies on human-set specific rules, DL works by exposing the algorithm to a large amount of annotated data and allowing the software to develop its own set of rules (i.e. learn) by adjusting the parameters inside the model (network) during a training process in order to complete the task on its own. One major limitation of traditional programming is that, with complex tasks, it may require an extensive set of rules to accurately complete the assignment. Additionally, traditional programming can be susceptible to human bias from programmer experience. With the dramatic increase in the amount and the complexity of clinical data, DL has been utilised to automate data analysis and thus to assist clinicians in patient management. This review will present the latest advances in DL, for managing posterior eye diseases as well as DL-based solutions for patients with vision loss.
ISSN:0816-4622
1444-0938
DOI:10.1080/08164622.2022.2111201