Lei Jin

Over the last 20 years, China’s infertility rate has risen from 3% to 12.5%–15%. Infertility has become the third largest disease following cancer and cardiovascular disease. Then, the in vitro fertilization and embryo transfer (IVF-ET) becomes more and more important in infertility treatment field. However, the reported success rate for IVT-ET is 30%–40% and costs are gradually rising. Meanwhile, to increase success rates and decrease costs, the optimal selection of the IVF-ET treatment strategy is crucial. In a clinical work, the IVF-ET treatment strategy selection is always based on the experience of the doctor without a uniform standard. To solve this important and complex problem, we proposed an artificial intelligence (AI)-based optimal treatment strategy selection system to extract implicit knowledge from clinical data for new and returning patients, by mimicking the IVF-ET process and analysing a myriad of treatment decisions. We demonstrated that the performance of the model was different in 10 AI classification algorithms. Hence, we need to select the optimal method for predicting patient pregnancy result in different IVF-ET treatment strategies. Moreover, feature ranking is determined in the proposed model to measure the importance of each patient characteristics. Therefore, better advice can be provided for individual patient characteristics, doctors can provide more valid suggestions regarding certain patient characteristics to improve the accuracy of diagnosis and efficiency.

Background: Different endometrial patterns have an important effect on the relationship between endometrial thickness (EMT) and clinical pregnancy rate. There is a significant difference in age, selection of cycle protocols, and clinical pregnancy rates among four groups with diverse endometrial patterns. Methods: This retrospective study aimed to assess the association between EMT on human chorionic gonadotropin (HCG) administration day and the clinical outcome of fresh in vitro fertilization (IVF). The 5th, 50th, and 95th percentiles for EMT were determined as 8, 11, and 14 mm, respectively. Patients were sub-divided into four groups based on their EMT in different endometrial patterns (Group 1: < 8 mm; Group 2: ≥ 8 and ≤ 11 mm; Group 3: > 11 and ≤ 14 mm; Group 4: > 14 mm). We divided patients into three groups based on their endometrial pattern and evaluated the correlation between EMT and clinical pregnancy rate. Results: We found a positive correlation between pregnancy rates and EMT in all endometrial patterns. Multiple logistic regression analysis proved age, duration of infertility, cycle protocols, number of embryos transferred, progesterone on HCG day, endometrial patterns, and EMT have significant effects on clinical pregnancy rates. Meanwhile, there was a significant difference in age, selection of cycle protocols, and clinical pregnancy rates among four groups with diverse endometrial patterns. Conclusions: Different endometrial patterns have an important effect on the relationship between EMT and clinical pregnancy rate.