Reproductive biology and endocrinology : RB&E
Nov 27, 2025
BACKGROUND: The application of Artificial Intelligence (AI) to sperm selection during Intracytoplasmic Sperm Injection (ICSI) procedures represents one of the most innovative advances in assisted reproductive technology (ART). Traditional sperm selec...
BACKGROUND: Accurate prediction of pregnancy outcomes in assisted reproductive technology (ART) remains a clinical challenge due to the complexity and heterogeneity of IVF/ICSI cycles. Existing models often focus on isolated treatment stages and rely...
BACKGROUND: Fresh embryo transfer reduces waiting time and minimizes embryo cryodamage for endometriosis (EM) patients. The current prediction models for fresh embryo transfer outcomes in EM primarily rely on logistic regression, with limited applica...
OBJECTIVE: To investigate the determinants affecting live birth outcomes in fresh embryo transfer among polycystic ovary syndrome (PCOS) patients using various machine learning (ML) algorithms and to construct predictive models, offering novel insigh...
Reproductive biology and endocrinology : RB&E
Sep 11, 2024
BACKGROUND: Data sciences and artificial intelligence are becoming encouraging tools in assisted reproduction, favored by time-lapse technology incubators. Our objective is to analyze, compare and identify the most predictive machine learning algorit...
Journal of assisted reproduction and genetics
Sep 3, 2024
PURPOSE: Identification of mature sperm at microdissection testicular sperm extraction (mTESE) is a crucial step of sperm retrieval to help patients with non-obstructive azoospermia (NOA) proceed to intracytoplasmic sperm injection. Touch print smear...
RESEARCH QUESTION: Can federated learning be used to develop an artificial intelligence (AI) model for evaluating oocyte competence using two-dimensional images of denuded oocytes in metaphase II prior to intracytoplasmic sperm injection (ICSI)?
BACKGROUND: The relationship between surgical sperm retrieval of different etiologies and clinical pregnancy is unclear. We aimed to develop a robust and interpretable machine learning (ML) model for predicting clinical pregnancy using the SHapley Ad...
Reproductive biology and endocrinology : RB&E
Jul 10, 2024
OBJECTIVE: To explore the optimal models for predicting the formation of high-quality embryos in Poor Ovarian Response (POR) Patients with Progestin-Primed Ovarian Stimulation (PPOS) using machine learning algorithms.
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