The fertility rates have declined in many industrialized countries during the last decades. This can partly be explained by socioeconomical factors, but may also be due to biologically related matters. Around 15% of couples will encounter fertility problems. Over the last decades, there has been a development of assisted reproduction technology (ART), and the use of ART treatment is increasing. The method of intracytoplasmic sperm injection (ICSI) was originally a treatment for couples where the male has reduced semen quality, but is often used also when the semen characteristics are normal. ART is to a high degree based on subjective assessments of spermatozoa and embryos, utilizing only a limited set of information. This project aims to develop strategies for making the selection of embryo and spermatozoa based on more objective criteria. AI methods make it possible to analyse large amounts of data from imaging and cell biological examinations to uncover patterns applicable in developing new methods for assessments of spermatozoa and embryos. By relating these patterns to ART outcome, the assessments will be optimized to improve the treatment results. This project also aims to develop tools for clinicians and embryologist to make more evidence-based decisions and thereby improving the ART outcome. AI methods constitute a new approach compared with traditional statistical methods, which are not equipped to reveal nonlinearities and complex relations between factors. A challenge using AI models is that they are more complex to interpret than traditional statistical models. Although the models may fit better to the data, it may be challenging to make them generalizable. Furthermore, the analysis may be difficult to understand by the users, but we will comply with this through a unique support system. Potential impact of the project findings is to reduce the number of treatment cycles and to lower cost per treatment.
Project leader: Trine Berit Haugen
Institution: Institutt for naturvitenskapelige helsefag