6533b7dcfe1ef96bd127355f
RESEARCH PRODUCT
A new approach based on Machine Learning for predicting corneal curvature (K1) and astigmatism in patients with keratoconus after intracorneal ring implantation
M. J. RupérezC. DualdeM. A. Valdes-masCarlos MonserratCristina Peris-martínezF. PastorJosé D. Martín-guerrerosubject
AdultMaleKeratoconusgenetic structuresComputer sciencemedicine.medical_treatmentHealth InformaticsAstigmatismMachine learningcomputer.software_genreKeratoconusCorneal TransplantationMachine LearningYoung AdultCorneal ectasiaIntracorneal ringsArtificial IntelligenceProsthesis FittingmedicineHumansIn patientCorneal transplantationAgedRing (mathematics)Corneal curvaturebusiness.industryCorneal TopographyAstigmatismProstheses and ImplantsMiddle AgedDecision Support Systems ClinicalPrognosismedicine.diseaseeye diseasesComputer Science ApplicationsPatient Outcome AssessmentTreatment OutcomeFemalesense organsArtificial intelligencebusinesscomputerLENGUAJES Y SISTEMAS INFORMATICOSSoftwaredescription
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.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2014-08-01 |