In Vitro Fertilization:
A study done by Guh et al. used data mining and AI to create a computer algorithm that can help predict pregnancy in vitro fertilization (IVF) [20]. Data mining (DM) uses AI in conjunction with advanced statistics to discover patterns in large databases. DM extracts the information of interest and is also able to find new key elements that might influence the outcome, thus increasing the amount of data that can be utilized [13,21]. Finding new trends that influence success rates for IVF are important for the patient and the clinician, to have realistic outcome expectations. To help clinicians predict pregnancy success rates, they created a hybrid intelligence model that used DM to integrate genetic algorithm-based and decision tree learning techniques that extracted information from the IVF patient records. They found that this model not only helped predict outcomes but suggested modified IVF treatment according to individual patient characteristics. A downside of the study is that the model they created used data from only one IVF center. However, if the centers united to share information, their pooled data can significantly expand to represent a wider population with increased accuracy. Other studies suggest using ANN systems to predict IVF outcomes by using Learning Vector Quantizer which allows generalization and standard parameters for enhanced predictive power [20]. Another aspect of IVF and AI is the possibility of identifying the most viable oocytes and embryos. An AI system used by Manna et al. suggests combining AI to extract texture descriptors from an image (local binary pattern), and assembling it using an ANN [22]. These results proved to be above average when compared to current methods and can help to select the best possible oocytes or embryos noninvasively and objectively. Furthermore, they highlight the advantages of this technology in selecting the most viable embryos, even in countries where legislation precludes embryo selection by sex.