M. Valdés-Mas, J. Martín-Guerrero, M. J. Rupérez, C. Peris, Carlos Monserrat Aranda
{"title":"机器学习预测圆锥角膜患者角膜环植入术后散光","authors":"M. Valdés-Mas, J. Martín-Guerrero, M. J. Rupérez, C. Peris, Carlos Monserrat Aranda","doi":"10.1109/BHI.2014.6864474","DOIUrl":null,"url":null,"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.","PeriodicalId":177948,"journal":{"name":"IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)","volume":"18 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2014-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Machine learning for predicting astigmatism in patients with keratoconus after intracorneal ring implantation\",\"authors\":\"M. Valdés-Mas, J. Martín-Guerrero, M. J. Rupérez, C. Peris, Carlos Monserrat Aranda\",\"doi\":\"10.1109/BHI.2014.6864474\",\"DOIUrl\":null,\"url\":null,\"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.\",\"PeriodicalId\":177948,\"journal\":{\"name\":\"IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)\",\"volume\":\"18 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2014-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/BHI.2014.6864474\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/BHI.2014.6864474","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Machine learning for predicting astigmatism in patients with keratoconus after intracorneal ring implantation
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.