Name
Title | ||
|---|---|---|
Machine learning for predicting astigmatism in patients with keratoconus after intracorneal ring implantation |
Abstract | ||
|---|---|---|
This work proposes a new approach based on Machine Learning to predict astigmatism in patients with kera-toconus (KC) after ring implantation. KC is a non-inflamatory, progressive thinning disorder of the cornea, resulting in a protusion, myopia and irregular astigmatism. The intracorneal ring implantation surgery has become a suitable technique to deal with keratoconus without the need of a corneal transplant. Two machine learning (ML) classifiers based on artificial neural network and a decision tree were used in this work. Artificial neural networks performed better than decision trees, achieving an absolute mean error lower than 2 diopters in a validation data set. An analysis of the most relevant features was also carried out. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/BHI.2014.6864474 | Biomedical and Health Informatics |
Keywords | Field | DocType |
decision trees,eye,learning (artificial intelligence),medical computing,medical disorders,neural nets,prosthetics,surgery,vision defects,KC,artificial neural network,astigmatism prediction,corneal transplant,decision tree,intracorneal ring implantation surgery,irregular astigmatism,keratoconus,machine learning classifier,myopia,noninflamatory disorder,patient,progressive thinning disorder,protusion,validation data set | Keratoconus,Decision tree,Astigmatism,Computer science,Corneal Transplant,Cornea,Irregular astigmatism,Artificial intelligence,Artificial neural network,Machine learning,Dioptre | Conference |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| M. A. Valdés-Mas | 1 | 0 | 0.34 |
| José D. Martín-Guerrero | 2 | 241 | 25.60 |
| María José Rupérez | 3 | 7 | 2.59 |
| Cristina Peris | 4 | 0 | 0.34 |
| C. Monserrat | 5 | 111 | 9.17 |