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Experimental Artificial Intelligence Systems in Ophthalmology: An Overview

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Artificial Intelligence in Ophthalmology

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Abstract

Novel artificial intelligence (AI) techniques, which encompass machine and deep learning systems, are providing opportunities to utilize multisource data for the purposes of disease detection, classification and prediction, as well as surgical screening, training and robotics. Automatic image processing and feature extraction from multimodal sources such as visual fields, optical coherence tomography and fundus photos can be combined with patient demographic, environmental and genetic data to achieve identification and grading of ophthalmic diseases and produce progression, and treatment predictions. In the below chapter, encouraging evidence of these emerging methods is described in the realms of surgical, corneal, retinal, and neuro-ophthalmic topics. While researchers have shown theoretical utility of AI systems, the validation of the proposed techniques on real-world external clinical data, a prerequisite to translating the techniques to patient care, is often times lacking or unsuccessful. Despite this, the potential of AI as a diagnostic and screening tool to improve patient care highlights the continued need for experimental approaches to be applied to tasks which have the potential to reduce time to diagnosis and treatment, improve diagnostic accuracy, predict progression of disease for better monitoring and earlier intervention, detect imaging features which may not be noticeable to manual imaging graders, and provide expert-level diagnosis and treatment recommendations to underserved areas. In particular, it can be predicted that AI will play an important role in the post-COVID-19 era by improving the functionality of telemedicine in the coming years.

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Authors and Affiliations

  1. Department of Ophthalmology and Visual Sciences, University of Illinois at Chicago, Chicago, IL, USA

    Joelle A. Hallak, Kathleen Emily Romond & Dimitri T. Azar

  2. Twenty/Twenty Therapeutics, San Francisco, CA, USA

    Dimitri T. Azar

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  1. Joelle A. Hallak
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  2. Kathleen Emily Romond
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Correspondence to Dimitri T. Azar .

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  1. Department of Ophthalmology, University of Warmia and Mazury, Olsztyn, Poland

    Andrzej Grzybowski

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Hallak, J.A., Romond, K.E., Azar, D.T. (2021). Experimental Artificial Intelligence Systems in Ophthalmology: An Overview. In: Grzybowski, A. (eds) Artificial Intelligence in Ophthalmology. Springer, Cham. https://doi.org/10.1007/978-3-030-78601-4_7

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  • DOI: https://doi.org/10.1007/978-3-030-78601-4_7

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