No abstract

Background Intrauterine Insemination (IUI) outcome prediction is a challenging issue which the assisted reproductive technology (ART) practitioners are dealing with. Predicting the success or failure of IUI based on the couples' features can assist the physicians to make the appropriate decision for suggesting IUI to the couples or not and/or continuing the treatment or not for them. Many previous studies have been focused on predicting the in vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) outcome using machine learning algorithms. But, to the best of our knowledge, a few studies have been focused on predicting the outcome of IUI. The main aim of this study is to propose an automatic classification and feature scoring method to predict intrauterine insemination (IUI) outcome and ranking the most significant features. Methods For this purpose, a novel approach combining complex network-based feature engineering and stacked ensemble (CNFE-SE) is proposed. Three complex networks are extracted considering the patients' data similarities. The feature engineering step is performed on the complex networks. The original feature set and/or the features engineered are fed to the proposed stacked ensemble to classify and predict IUI outcome for couples per IUI treatment cycle. Our study is a retrospective study of a 5-year couples' data undergoing IUI. Data is collected from Reproductive Biomedicine Research Center, Royan Institute describing 11,255 IUI treatment cycles for 8,360 couples. Our dataset includes the couples' demographic characteristics, historical data about the patients' diseases, the clinical diagnosis, the treatment plans and the prescribed drugs during the cycles, semen quality, laboratory tests and the clinical pregnancy outcome. Results Experimental results show that the proposed method outperforms the compared methods with Area under receiver operating characteristics curve (AUC) of 0.84 ± 0.01, sensitivity of 0.79 ± 0.01, specificity of 0.91 ± 0.01, and accuracy of 0.85 ± 0.01 for the prediction of IUI outcome. Conclusions The most important predictors for predicting IUI outcome are semen parameters (sperm motility and concentration) as well as female body mass index (BMI).

47,XYY syndrome is a sex chromosomal anomaly in men, which may be associated with infertility and has an incidence of 0.1% of male births. The clinical and paraclinical characteristics of men suffering from this anomaly have not been fully described. In this retrospective study, we present 37 cases of 47,XYY infertile men with sperm counts varying from normal to azoospermia, referred to the Genetics Laboratory at the Royan Institute, Iran. Thirteen individuals were mosaic and 24 non-mosaics. Non-mosaic patients were classified as azoospermic (nine cases) and normospermic/oligozoospermic men (15 cases). Two of the non-mosaic and three mosaic patients had secondary infertility. In addition, 13 of them underwent IUI, IVF or ICSI, and in seven cases, there was a biochemical pregnancy. The remaining 14 patients did not have ART. The 47,XYY syndrome is relatively unusual and can be missed clinically because of the lack of symptoms and of diverse phenotypes. Diagnosis of this aneuploidy can provide valuable data for counselling and early management of the patients who undergo fertility evaluation.