جستجوی مقالات مرتبط با کلیدواژه "intra cytoplasmic sperm injection" در نشریات گروه "پزشکی"

Intra-cytoplasmic sperm injection (ICSI) in infertile couples has created a great improvement for treatment of these patients. Unfortunately, despite of high cost of doing ICSI the rate of success is not acceptable and failing pregnancy put a heavy anxiety to couples. This study is aimed to make an effort in order to extract the best predictors for predict the success rate of intra cytoplasmic sperm injection and promote accuracy, sensivity and specificity by the use of artificial neural network and logistic regression.

345 patients received ICSI treatment and each of which constructed of 54 numerical and nominal records. This database was randomly divided into the estimation (n=276) and validation (n=69) data set. The models were used based on binary logistic regression (BLR) feature selection tools and Levenberg-Marquardth neural network classifier. Finally, models were evaluated using important criteria such as accuracy, sensitivity and specificity.

The best output of the B LR model by using 54 variables revealed accuracy (97%), sensitivity (86%)and specificity (%94). The best output of the LMNN model using Reduced dataset consisted of n=29 with a feature vector side yielded the accuracy (82%) and sensitivity (%92) and specificity (%76).

Our result demonstrated that BLR model outperformed highlighting the great power of BLR in success rate of ICSI prediction while using binary output.

In vitro fertilization techniques are used for many years. In such methods, if feasible, it is always better to transform a high quality embryo into uterus to enhance the chance of the success. Therefore, a comprehensive research was conducted to identify important factors related to the embryo quality. We considered potential correlation between embryos derived from an infertile couple in our statistical analyses. In addition, possible nonlinear relationships between the factors and the embryo quality were investigated. In this study, after collecting the necessary information, the statistical method of generalized additive models (GAM) has been employed to determine any nonlinear relationship between quantitative exploratory variables and the response. Then, the results were used to analyze the data using the Generalized Estimating Equation (GEE) technique. Such model can handle correlation which may present among infertile couple embryos. Finally, important factors related to the embryo quality of in-vitro fertilization by intra-cytoplasmic sperm injection methods were identified. Based on results of the GEE, linear relationships of drugs, type of infertility, and polycystic ovary syndrome was statistically significant on embryo quality. Interestingly, after examining the nonlinear relationships with GAM, in addition to the above variables, smoking status, sperm motility, and uterine factor were also shown to be significant. Since observations on a single subject in fertilization techniques are correlated and the relationship between factors and outcomes of fertilization may not be linear, the usual methods of GAM should incorporate the potential intra-cluster correlation using models such as the GEE. Our results suggest significant effects of tobacco use, drugs, polycystic ovarian syndrome, type of infertility, and sperm motility on the embryo quality. Sperm motility has non-linear effect on embryo quality resulting from in vitro fertilization and improved the overall model fit.

In vitro fertilization techniques are used for many years. In such methods, if feasible, it is always better to transform a high quality embryo into uterus to enhance the chance of the success. Therefore, a comprehensive research was conducted to identify important factors related to the embryo quality. We considered potential correlation between embryos derived from an infertile couple in our statistical analyses. In addition, possible nonlinear relationships between the factors and the embryo quality were investigated. In this study, after collecting the necessary information, the statistical method of generalized additive models (GAM) has been employed to determine any nonlinear relationship between quantitative exploratory variables and the response. Then, the results were used to analyze the data using the Generalized Estimating Equation (GEE) technique. Such model can handle correlation which may present among infertile couple embryos. Finally, important factors related to the embryo quality of in-vitro fertilization by intra-cytoplasmic sperm injection methods were identified. Based on results of the GEE, linear relationships of drugs, type of infertility, and polycystic ovary syndrome was statistically significant on embryo quality. Interestingly, after examining the nonlinear relationships with GAM, in addition to the above variables, smoking status, sperm motility, and uterine factor were also shown to be significant. Since observations on a single subject in fertilization techniques are correlated and the relationship between factors and outcomes of fertilization may not be linear, the usual methods of GAM should incorporate the potential intra-cluster correlation using models such as the GEE. Our results suggest significant effects of tobacco use, drugs, polycystic ovarian syndrome, type of infertility, and sperm motility on the embryo quality. Sperm motility has non-linear effect on embryo quality resulting from in vitro fertilization and improved the overall model fit.