Konferensartikel

This article deals with intra-ocular lens (IOL) power calculations during the cataract surgery. At present, IOL power calculated by formulas is usually able to provide acceptable results for the majority of the patients. The problem appears when any of input parameters have the value which is not normal in population distribution. Then the patient post-operative refraction result can inconsiderable deviate from intended target. This work describes approach how to preoperatively indicate which samples of a patient could be problematic in accurate IOL calculations by classi?cation of Arti?cial Neural Networks (ANN). Small and long eyes are used to test the ability of ANN to classify input samples which are taken from pre-operative measurements to several groups which represent probable post-operative result. In our experiment, ANN classi?es samples into two groups. The ?rst group is for data samples with a probable result in positive ranges of diopter and second group is for negative ranges. The accuracy of ANN, in this case, is 94.1 %.

intra-ocular lens (IOL) power calculation, arti?cial neural networks (ANN), cataract surgery, refraction result