Pattern recognition methods in human-assisted reproduction

The aim of this paper is to present a method which allows to select, more accurately than it is currently done, embryos, at the desired stage of development, which will lead to births, although perhaps with some slight differences in precision, that are deemed to be important. Thus we show that embryos, pronuclei and oocytes consist at least of two types: those suitable for procreation and those not suitable and both types can be recognised easily by an automatic procedure at whatever stage is desired. From their digitilised images before transfer, specific characteristics are formulated automatically and through a particular pattern recognition algorithm the specimen is recognised as belonging to one of two groups with a high precision. The algorithm works in two stages: in the first, the images and their outcomes are used to train the algorithm and in a second stage, on the basis of the rule learnt in training, embryos, pronuclei or oocytes are classified. As multiple transfers and pregnancies occur the training must be carried out with an imprecise ‘teacher’ through a suitable algorithm which can handle this.