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Artificial Intelligence in Keratoconus Diagnosis

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Keratoconus

Abstract

Artificial intelligence aims to develop machines capable of simulating the human ability to think and act. Machine learning can be defined as the branch of artificial intelligence that aims to endow the machine with the ability to learn. Developing an efficient technique to assist practitioners in objectively detecting early keratoconus is of paramount importance. Various machine learning techniques have been developed for keratoconus detection and refractive surgery screening. This is a burgeoning field of study, with significant potential for continued advancement as screening devices and techniques become more sophisticated. Understanding these aspects and the revolution we are experiencing is important to guide the construction process and the validation of these decision-making assistant algorithms before deploying them to patient care.

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Author information

Authors and Affiliations

  1. State University of Health Sciences of Alagoas, Maceió, AL, Brazil

    João Marcelo de Almeida Gusmão Lyra & Edileuza Virginio Leão

  2. Brazilian Study Group of Artificial Intelligence and Corneal Analysis (BrAIN), Rio de Janeiro, RJ, Brazil

    Aydano Pamponet Machado

  3. Department of Ophthalmology, Federal University of São Paulo (UNIFESP), São Paulo, SP, Brazil

    Aydano Pamponet Machado

  4. Computing Institute, Federal University of Alagoas, Maceió, AL, Brazil

    Aydano Pamponet Machado

Authors
  1. João Marcelo de Almeida Gusmão Lyra
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  2. Edileuza Virginio Leão
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  3. Aydano Pamponet Machado
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Editor information

Editors and Affiliations

  1. Provisão Eye Hospital of Maringá, Maringá, Paraná, Brazil

    Edna Almodin

  2. Federal University of São Paulo, São Paulo, São Paulo, Brazil

    Belquiz Amaral Nassaralla

  3. Nucleo de Medicina Ocular, Goiânia, Goiás, Brazil

    Jordana Sandes

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de Almeida Gusmão Lyra, J.M., Leão, E.V., Machado, A.P. (2022). Artificial Intelligence in Keratoconus Diagnosis. In: Almodin, E., Nassaralla, B.A., Sandes, J. (eds) Keratoconus . Springer, Cham. https://doi.org/10.1007/978-3-030-85361-7_17

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  • DOI: https://doi.org/10.1007/978-3-030-85361-7_17

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-85360-0

  • Online ISBN: 978-3-030-85361-7

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