Advances in machine learning (ML) and artificial intelligence (AI) are transforming the way we treat patients in ways not even imagined a few years ago. Cancer research is at the forefront of this movement. Infertility, though not a life-threatening condition, affects around 15% of couples trying for a pregnancy. Increasing availability of large datasets from various sources creates an opportunity to introduce ML and AI into infertility prevention and treatment. At present in the field of assisted reproduction, very little is done in order to prevent infertility from arising, with the main focus put on treatment when often advanced maternal age and low ovarian reserve make it very difficult to conceive. A shift from this disease-centric model to a health centric model in infertility is already taking place with more emphasis on the patient as an active participator in the process. Poor quality and incomplete data as well as biological variability remain the main limitations in the widespread and reliable implementation of AI in the field of reproductive medicine. That said, one of the areas where this technology managed to find a foothold is identification of developmentally competent embryos. More work is required however to learn about ways to improve natural conception, the detection and diagnosis of infertility, and improve assisted reproduction treatments (ART) and ultimately, develop clinically useful algorithms able to adjust treatment regimens in order to assure a successful outcome of either fertility preservation or infertility treatment. Progress in genomics, digital technologies and advances in integrative biology has had a tremendousimpact on research and clinical medicine. With the rise of ‘big data’, artificial intelligence, and the advances in molecular profiling, there is an enormous potential to transform not only scientific research progress, but also clinical decision making towards predictive, preventive, and personalized medicine. In the field of reproductive health, there is now an exciting opportunity to leverage these technologies and develop more sophisticated approaches to diagnose and treat infertility disorders. In this review, we present a comprehensive analysis and interpretation of different innovation forces that are driving the emergence of a system approach to the infertility sector. Here we discuss recent influential work and explore the limitations of the use of Machine Learning models in this rapidly developing area.

The ability to predict the likelihood of a live birth after single fresh embryo transfer is important for treatment planning and managing patient expectation, particularly in their first in vitro fertilization (IVF) cycle. Cryopreservation of supernumerary embryos is often regarded as an important prognostic variable and a surrogate marker of success for several reasons. While previous large studies have examined the association between the number of oocytes retrieved and cleavage-stage embryos available, and the odds of a live birth following a fresh embryo transfer, the relationship between the number of supernumerary blastocysts cryopreserved following a fresh embryo transfer has not been rigorously studied. We performed a retrospective analysis of data collected between 2006 and 2018 for all first time IVF patients with a fresh autologous day 5 single blastocyst transfer. The relationship between the likelihood of a live birth and number of supernumerary blastocysts cryopreserved was assessed according to patient age group. In patients aged <35 years and 35-39 years old, the likelihood of a live birth increased linearly from 0.33 (95%CI:0.31–0.34) to 0.80 (95%CI:0.74–0.86; P<.0001) and 0.30 (95%CI:0.28–0.32) to 0.82 (95%CI:0.73–0.91;P<.0001) between 1-6 blastocysts cryopreserved and then non-linearly to 0.95 (95% CI 0.92–0.97; P<.0001) and 0.96 (95%CI:0.93–0.99; P<.0001) if 10 or more blastocysts were cryopreserved, respectively. When aged 40 years and above, the likelihood of a live birth increased linearly from 0.26 (95%CI:0.19–0.32) to 0.83 (95%CI:0.68–0.97; P<.0001) between 1-4 blastocysts cryopreserved and then non-linearly to 0.99 (95%CI:0.98–0.99; P<.0001) if 10 or more blastocysts were cryopreserved. The present study demonstrated a non-linear relationship between the number of supernumerary blastocysts cryopreserved and the likelihood of a live birth after single blastocyst transfer in the first autologous fresh IVF/ICSI cycle across different age groups.