Detection of subclinical keratoconus using biometric parameters
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URI: http://hdl.handle.net/10317/8967ISBN: 978-3-030-17934-2 (Print)
ISBN: 978-3-030-17935-9 (Online)
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Velázquez Blázquez, José Sebastián; Cavas Martínez, Francisco; Alió del Barrio, Jorge Luis; García Fernández Pacheco, Daniel; Fernández Cañavate, Francisco José; [et al.]Área de conocimiento
Expresión Gráfica en IngenieríaPatrocinadores
This publication has been carried out in the framework of the Thematic Network for Co-Operative Research in Health (RETICS) reference number RD16/0008/0012 financed by the Carlos III Health Institute-General Subdirection of Networks and Cooperative Investigation Centers (R&D&I National Plan 2013–2016) and the European Regional Development Fund (FEDER).Fecha de publicación
2019Editorial
Springer International PublishingCita bibliográfica
Velázquez-Blázquez J.S. et al. (2019) Detection of Subclinical Keratoconus Using Biometric Parameters. In: Rojas I., Valenzuela O., Rojas F., Ortuño F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2019. Lecture Notes in Computer Science, vol 11466. Springer, Cham. https://doi.org/10.1007/978-3-030-17935-9_44Palabras clave
OphthalmologyEarly keratoconus
Computational modelling
Scheimpflug technology
Resumen
The validation of innovative methodologies for diagnosing keratoconus in its earliest stages is of major interest in ophthalmology. So far, subclinical keratoconus diagnosis has been made by combining several clinical criteria that allowed the definition of indices and decision trees, which proved to be valuable diagnostic tools. However, further improvements need to be made in order to reduce the risk of ectasia in patients who undergo corneal refractive surgery. The purpose of this work is to report a new subclinical keratoconus detection method based in the analysis of certain biometric parameters extracted from a custom 3D corneal model. This retrospective study includes two groups: the first composed of 67 patients with healthy eyes and normal vision, and the second composed of 24 patients with subclinical keratoconus and normal vision as well. The proposed detection method generates a 3D custom corneal model using computer-aided graphic design (CAGD) tools and corneal surfaces’ ...
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