Valdés Mas, MÁ.; Martin-Guerrero, JD.; Rupérez Moreno, MJ.; Pastor, F.; Dualde, C.; Monserrat, C.; Peris-Martinez, C. (2014). A new approach based on Machine Learning for predicting corneal curvature (K1) and astigmatism in patients with keratoconus after intracorneal ring implantation. Computer Methods and Programs in Biomedicine. 116(1):39-47. https://doi.org/10.1016/j.cmpb.2014.04.003
Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/51684
Title:
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A new approach based on Machine Learning for predicting corneal curvature (K1) and astigmatism in patients with keratoconus after intracorneal ring implantation
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Author:
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Valdés Mas, María Ángeles
Martin-Guerrero, J. D.
Rupérez Moreno, María José
Pastor, F
Dualde, C.
Monserrat, C.
Peris-Martinez, C.
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UPV Unit:
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Universitat Politècnica de València. Instituto Interuniversitario de Investigación en Bioingeniería y Tecnología Orientada al Ser Humano - Institut Interuniversitari d'Investigació en Bioenginyeria i Tecnologia Orientada a l'Ésser Humà
Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació
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Issued date:
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Abstract:
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Keratoconus (KC) is the most common type of corneal ectasia. A corneal transplantation
was the treatment of choice until the last decade. However, intra-corneal ring implantation
has become more and more common, and it ...[+]
Keratoconus (KC) is the most common type of corneal ectasia. A corneal transplantation
was the treatment of choice until the last decade. However, intra-corneal ring implantation
has become more and more common, and it is commonly used to treat KC thus avoiding a
corneal transplantation. This work proposes a new approach based on Machine Learning to
predict the vision gain of KC patients after ring implantation. That vision gain is assessed
by means of the corneal curvature and the astigmatism. Different models were proposed;
the best results were achieved by an artificial neural network based on the Multilayer Perceptron.
The error provided by the best model was 0.97D of corneal curvature and 0.93D of
astigmatism.
© 2014 Elsevier Ireland Ltd. All rights reserved.
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Subjects:
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Machine Learning
,
Keratoconus
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Intracorneal rings
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Astigmatism
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Copyrigths:
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Cerrado |
Source:
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Computer Methods and Programs in Biomedicine. (issn:
0169-2607
)
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DOI:
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10.1016/j.cmpb.2014.04.003
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Publisher:
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Elsevier
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Publisher version:
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http://dx.doi.org/10.1016/j.cmpb.2014.04.003
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Project ID:
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info:eu-repo/grantAgreement/MICINN//TIN2010-20999-C04-01/ES/MODELIZACION BIOMECANICA DE TEJIDOS APLICADO A CIRUGIA ASISTIDA POR ORDENADOR/
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Thanks:
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This work was supported by the Spanish Ministry of Science and Innovation, MICINN (reference TIN2010-20999-C04-01).
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Type:
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Artículo
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