Objective To evaluate the decreased level of serum inhibin B(INHB)treated by gunadotropin releasing hormone agonist(GNRH-a)in predicting ovarian response and pregnancy in in vitro fertilization-embryo transfer(IVF-ET).Methods The prospective study enrolled 124 women given by GnRH-a+recombine follicle stimulating hormone(rFSH)+human chorionic gonadotrophin(hCG)long term stimulation protocol undergone their first cycle of IVF-ET treatment.The following predictive factors were collected and analyzed,such as age,basal level of follicle stimulating hormone(FSH),the ratio of FSH/luteinizing hormone(LH),the concentration of INHB after down-regulation,total number of antral follicle count(AFC)and mean ovarian volume. Ovarian response was evaluated by the number of oecytes obtained.A multiple regression analysis and logistic regression model were used for all possible prognostic variables to evaluate the value of difierent hormones in predicting ovariall response and pregnancy after IVF-ET.Receiver operating characteristic(ROC) analysis was used to evaluate the level of INHB in predicting the number of oocytes obtained.The sensitivity and specificity were calculated at the discriminating cut-off point Results The concentration of INHB after down-regulation showed a highly significant positive correlations with the number of oocytes obtained(r=0.435,P<0.01).The multiple regression analyses showed INHB was the most significant predictor of the number of retrieved oocytes,but INHB was not associated with IVF-ET outcome significantly(P>0.05).ROC analyses showed INHB after down-regulation had the largest area under curve(AUC)0.933(95%CI:0.878-0.988).When a threshold of 15 ng/L of INHB was established,95.5%sensitivity and 50.0% specificity in ovarian response were observed.Conclusions The level of INHB Was the best factor in predicting ovarian response in IVF-ET.Decreased level of INHB Was the early sign of ovarian reserve function failure,however,useless in predicting IVF-ET outcome.

n occurring after the treatment of IVM in women with PCOS are not mounting. However, the relative high rates of multiple pregnancies, low birth weight and preterm labor were increased.

The integration of artificial intelligence into the medical field is developing rapidly and has achieved ground-breaking advances in the diagnosis, treatment, and efficacy evaluation of imaging medicine. This article reviews the research advances in artificial intelligence in imaging diagnosis of hepatocellular carcinoma and its performance in evaluating treatment outcome and predicting prognosis in combination with clinical features and looks forward to how artificial intelligence can be better used in the practice of hepatocellular carcinoma imaging in the era of growing clinical needs and rapid advances in diagnosis and treatment techniques.