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Artificial Intelligence for Keratoconus Detection and Refractive Surgery Screening

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Artificial Intelligence in Ophthalmology

Abstract

Artificial intelligence (AI) has been employed in Keratoconus screening for decades. Since the introduction of Neural Networks, the first machine learning techniques used for topographic classification, AI efforts have focused on detection of nascent forms of corneal ectasias (Forme Frustre Keratoconus and Keratoconus Suspects). Parallel to this, the development of new imaging technology has contributed to the increasing keratoconus data sets, improving the accuracy of algorithms. Currently, AI can assist ophthalmologists with automated interpretation of topographic maps, laser vision correction surgery (particularly in custom planning of the procedure), and prevention of iatrogenic ectasias. This area of medicine will continue to grow in complexity along with new technology promising to make its application routine in the future.

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Authors and Affiliations

  1. Boston, MA, USA

    José Luis Reyes Luis & Roberto Pineda

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  1. José Luis Reyes Luis
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  2. Roberto Pineda
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  1. Department of Ophthalmology, University of Warmia and Mazury, Olsztyn, Poland

    Andrzej Grzybowski

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Reyes Luis, J.L., Pineda, R. (2021). Artificial Intelligence for Keratoconus Detection and Refractive Surgery Screening. In: Grzybowski, A. (eds) Artificial Intelligence in Ophthalmology. Springer, Cham. https://doi.org/10.1007/978-3-030-78601-4_15

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  • DOI: https://doi.org/10.1007/978-3-030-78601-4_15

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  • Online ISBN: 978-3-030-78601-4

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