An ultrasound-based deep learning radiomic model combined with clinical data to predict clinical pregnancy after frozen embryo transfer: a pilot cohort study.

Research Question: Can a multi-modal fusion model based on ultrasound-based deep learning radiomics combined with clinical parameters provide personalized evaluation of endometrial receptivity and predict the occurrence of clinical pregnancy after frozen embryo transfer (FET)?

Design: Prospective cohort study of women (n = 326) who underwent FET between August 2019 and December 2021. Input quantitative variables and input image data for radiomic feature extraction were collected to establish a multi-modal fusion prediction model. An additional independent dataset of 453 ultrasound endometrial images was used to establish the segmentation model to determine the endometrial region on ultrasound images for analysis.