OBJECTIVE: To analyze the performance of an annotation-free embryo scoring system on the basis of deep learning for pregnancy prediction after single vitrified blastocyst transfer (SVBT) compared with the performance of other blastocyst grading syste...
OBJECTIVE: To measure human sperm intracellular pH (pH) and develop a machine-learning algorithm to predict successful conventional in vitro fertilization (IVF) in normospermic patients.
OBJECTIVE: To describe a computer algorithm designed for in vitro fertilization (IVF) management and to assess the algorithm's accuracy in the day-to-day decision making during ovarian stimulation for IVF when compared to evidence-based decisions by ...
OBJECTIVE: To develop a random forest model (RFM) to predict implantation potential of a transferred embryo and compare it with a multivariate logistic regression model (MvLRM), based on data from a large cohort including in vitro fertilization (IVF)...
The goal of this Views and Interviews series was to bring together the thought leaders in the field and envision what the laboratory will look like in the future. This consensus piece strives to take the thoughts of those leaders and develop themes a...