A multi-relational learning approach for knowledge extraction in in vitro fertilization domain

Abstract

In the field of assisted reproductive technologies, ICSI fertilization is a medically-assisted reproduction technique, enabling infertile couples to achieve successful pregnancy. In this field crucial points are: the analysis of clinical data of the patient, aimed at adopting an appropriate stimulation protocol to obtain an adequate number of oocytes, and the selection of the best oocytes to fertilize. In this paper we would provide a framework able to extract useful morphological features from oocyte images that combined with the provided clinical data of the patients can be used to discover new information for defining therapeutic plans for new patients as well as selecting the most promising oocytes. © 2010 Springer-Verlag.