Diagnosis of Subclinical Keratoconus Based on Machine Learning Techniques
Ficheros
Identificadores
Compartir
Metadatos
Mostrar el registro completo del ítemAutor
Castro De Luna, Gracia María; Jiménez Rodríguez, Diana; Castaño Fernández, Ana Belén; Pérez Rueda, AntonioFecha
2021-09-21Resumen
Background: Keratoconus is a non-inflammatory corneal disease characterized by gradual thinning of the stroma, resulting in irreversible visual quality and quantity decline. Early detection of keratoconus and subsequent prevention of possible risks are crucial factors in its progression. Random forest is a machine learning technique for classification based on the construction of thousands of decision trees. The aim of this study was to use the random forest technique in the classification and prediction of subclinical keratoconus, considering the metrics proposed by Pentacam and Corvis. (2) Methods: The design was a retrospective cross-sectional study. A total of 81 eyes of 81 patients were enrolled: sixty-one eyes with healthy corneas and twenty patients with subclinical keratoconus (SCKC): This initial stage includes patients with the following conditions: (1) minor topographic signs of keratoconus and suspicious topographic findings (mild asymmetric bow tie, with or without deviati...
Palabra/s clave
subclinical keratoconus
deep learning
corneal topography
random forest