This study aimed to develop and validate a multimodal deep learning model that leverages 2D grayscale ultrasound (US) images alongside readily available clinical data to improve diagnostic performance for ovarian cancer (OC). A retrospectiv…
PURPOSE: The objective of this study was to develop a deep learning model, using the ConvNeXt algorithm, that can effectively differentiate between ovarian endometriosis cysts (OEC) and benign mucinous cystadenomas (MC) by analyzing ultraso…
BACKGROUND: Based on the ovarian-adnexal reporting and data system (O-RADS), we constructed a nomogram model to predict the malignancy potential of adnexal masses with sophisticated ultrasound morphology. METHODS: In a multicenter retrospe…
Well-orchestrated maternal-fetal cross talk occurs via secreted ligands, interacting receptors, and coupled intracellular pathways between the conceptus and endometrium and is essential for successful embryo implantation. However, previous …
OBJECTIVE: This study systematically evaluated the effectiveness and safety of therapeutic vaccines for precancerous cervical lesions, providing evidence for future research. METHODS: We systematically searched the literature in 10 databas…