Role of three-dimensional Doppler ultrasonography and leukemia inhibitory factor from endometrial secretion in predicting endometrial receptivity in IVF treatment: a pilot study.

PURPOSE: This pilot study aimed to evaluate the potential synergistic role of three-dimensional power Doppler angiography ultrasound and the expression of Leukemia Inhibitory Factor (LIF) protein in predicting the endometrial receptivity of fresh In-Vitro Fertilization (IVF) cycles. MATERIALS AND METHODS: This prognostic cohort study involved 29 good prognosis women who underwent fresh IVF cycles with fresh blastocysts transfer. Serial measurements of sub-endometrial parameters including vascularity index (VI), flow index (FI), and vascularization flow index (VFI) were conducted consecutively via power Doppler angiography on the day of oocyte maturation trigger, oocyte retrieval, and blastocyst transfer. Aspiration of endometrial secretion was performed on the day of embryo transfer. RESULTS: The mean index of VI and VFI on the trigger and oocyte retrieval day and also LIF protein concentration at the window of implantation were significantly higher in clinically pregnant women than that of the non-pregnant women (p < 0.05). The area under the curve (AUC) of VI and VFI was shown to have a powerful predictive value to forecast receptive endometrium on either trigger day (0.788 and 0.813, respectively) or oocyte retrieval day (0.813 and 0.818). Likewise, LIF concentration on the day of embryo transfer was adequate to become a predictor for endometrial receptivity (AUC 0.874). A combination of the VI and VFI on the trigger day and LIF concentration at specific cut-off values (VI > 5.381, VFI > 1.483, LIF 703.5 pg/mL) produced an algorithm with high AUC (0.881) and high specificity (94.4%) for an adequate prediction of non-receptive endometrium. CONCLUSION: VI and VFI index assessed on maturation trigger day and the expression of LIF protein concentration at the window of implantation provided sufficient information to predict endometrial receptivity. A large randomized control trial is needed to validate these findings.

Genetic algorithm-assisted machine learning for clinical pregnancy prediction in in vitro fertilization.

BACKGROUND: A clinical pregnancy prediction model was developed by implementing machine learning technology that uses a combination of static images and medical data to calculate the outcome of an in vitro fertilization cycle. OBJECTIVE: To provide a system that can accurately and sufficiently assist with decision making that is critical to in vitro fertilization cycles, primarily embryo selection. STUDY DESIGN: Historical medical data, which consist of clinical information and a complete transferred embryo image dataset, of 697 patients who underwent unique in vitro fertilization were collected. Various techniques of machine learning were used, namely decision tree, random forest, and gradient boosting; each technique used the same data configuration for performance comparison and was subsequently optimized using genetic algorithm. RESULTS: A prediction model with a peak accuracy of approximately 65% was achieved. Significant differences in the performances of the 3 selected algorithms were apparent. Nonetheless, additional metric measurements, such as receiver operating characteristic, area under the receiver operating characteristic curve score, accuracy, and loss, suggested that the gradient boosting model performed the best in predicting clinical pregnancy. CONCLUSION: This study served as a stepping stone toward the application of in vitro fertilization prediction models that use machine learning techniques. However, additional validation steps are required to boost the model's performance for its implementation in the clinical setting.