Seeking arrangements: cell contact as a cleavage-stage biomarker.

Research Question: What can three-dimensional cell contact networks tell us about the developmental potential of cleavage-stage human embryos?

Design: This pilot study was a retrospective analysis of two Embryoscope imaging datasets from two clinics. An artificial intelligence system was used to reconstruct the three-dimensional structure of embryos from 11-plane focal stacks. Networks of cell contacts were extracted from the resulting embryo three-dimensional models and each embryo's mean contacts per cell was computed.

Predicting the number of oocytes retrieved from controlled ovarian hyperstimulation with machine learning.

Study Question: Can machine learning predict the number of oocytes retrieved from controlled ovarian hyperstimulation (COH)?

Summary Answer: Three machine-learning models were successfully trained to predict the number of oocytes retrieved from COH.

What Is Known Already: A number of previous studies have identified and built predictive models on factors that influence the number of oocytes retrieved during COH. Many of these studies are, however, limited in the fact that they only consider a small number of variables in isolation.

Machine learning for prediction of euploidy in human embryos: in search of the best-performing model and predictive features.

Objective: To assess the best-performing machine learning (ML) model and features to predict euploidy in human embryos.

Design: Retrospective cohort analysis.

Setting: Department for reproductive medicine in a university hospital.

Predictive modeling in reproductive medicine: Where will the future of artificial intelligence research take us?

Artificial intelligence (AI) systems have been proposed for reproductive medicine since 1997. Although AI is the main driver of emergent technologies in reproduction, such as robotics, Big Data, and internet of things, it will continue to be the engine for technological innovation for the foreseeable future. What does the future of AI research look like?

Data sharing: using blockchain and decentralized data technologies to unlock the potential of artificial intelligence: What can assisted reproduction learn from other areas of medicine?

The extension of blockchain use for nonfinancial domains has revealed opportunities to the health care sector that answer the need for efficient and effective data and information exchanges in a secure and transparent manner. Blockchain is relatively novel in health care and particularly for data analytics, although there are examples of improvements achieved. We provide a systematic review of blockchain uses within the health care industry, with a particular focus on the in vitro fertilization (IVF) field.