Deep learning: applications in retinal and optic nerve diseases

ABSTRACT

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.

KEYWORDS:

• Artificial intelligence
• deep learning
• glaucoma
• retinal disease

Disclosure statement

The authors have no relevant financial or non-financial interests to disclose.

Additional information

Funding

DAM is supported by a National Health and Medical Research Council practitioner fellowship [GNT1154518]. FKC receives funding from the National Health and Medical Research Council [Centre of Research Excellence Grant GNT1116360 and Fellowship MRF1142962]. DA-C receives funding from the National Health and Medical Research Council [Ideas Grant NHMRC 1186915].