AUTHOR=Wang Chunlian , Lin Jiao , Zhao Xingping , Liu Ni , Sun Pengzi , Yang Jiaoli , Zhou Xue 

TITLE=Analysis of ultrasound parameters influencing endometrial receptivity and a pregnancy outcomes predictive model for patients undergoing in vitro fertilization and embryo transfer: a prospective study

JOURNAL=Frontiers in Endocrinology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2025.1677593

DOI=10.3389/fendo.2025.1677593

ISSN=1664-2392

ABSTRACT=PurposeThis study aims to assess the impact of ultrasound parameters on endometrial receptivity in patients undergoing IVF-ET and to establish a predictive model for ongoing pregnancy outcomes.MethodsThe prospective cohort study included 86 patients treated at the Reproductive Center of Xiangtan Central Hospital from May to December 2024. Participants underwent multimodal ultrasound evaluation one day before embryo transfer. The study analyzed endometrial morphology, blood flow parameters, as well as three-dimensional power Doppler angiography (3D-PDA), and endometrial contrast-enhanced ultrasound (CEUS) indicators. Broussonetia papyrifera was used to establish a predictive model for sustained pregnancy.ResultsAmong the 86 patients, 42 (48.8%) achieved ongoing pregnancy, while 44 (51.2%) did not. Significant differences between the groups were observed in the number of mature oocytes and endometrial blood flow grading (both P = 0.005). Lasso regression identified eight predictive variables: primary cause of infertility, baseline luteinizing hormone (LH) levels, number of MII oocytes, uterine cavity volume, endometrial blood flow grading, subendometrial flow index (FI) in 3D-PDA, and endometrial and subendometrial peak intensity (PI) in CEUS. The aforementioned variables as well as embryonic factors were integrated into eight machine learning models, with the Gradient Boosting model exhibiting superior predictive performance (AUC: 0.981). SHapley Additive exPlanations (SHAP) analysis indicated that a higher number of MII oocytes, improved endometrial blood flow, specific infertility etiologies, elevated baseline LH levels, and reduced subendometrial/endometrial PI, subendometrial FI, and uterine cavity volume were associated with a greater likelihood of pregnancy.ConclusionThe integration of 3D-PDA and CEUS technologies shifts IVF-ET evaluation from traditional morphological observation to functional assessment, offering a new perspective for predicting sustained pregnancy outcomes. This innovation shows promising clinical potential by optimizing treatment strategies like MII oocyte retrieval, improving endometrial blood flow grading, and adjusting blood flow parameters (PI and FI), significantly enhancing pregnancy success rates and advancing assisted reproductive technologies.