Big Data and Artificial Intelligence (AI) are Poised to Transform Infertility Healthcare

Abstract

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.

Embryo selection with artificial intelligence: how to evaluate and compare methods?

“…Similar cross-validation analyses could split the dataset across IVF/ICSI fertilization, age groups, ethnicities, years of treatment, etc. to reveal potential model or dataset biases such as "spectrum bias" and "historical bias" [7]. However, a pure external validation requires the new setting (e.g., clinic) to be held out entirely during model development such as the so-called "double-blind test sets" reported by [12].…”

“…Several studies have reviewed AI algorithms and their uses for various applications within IVF [2][3][4][5][6][7][8]. In this paper, however, we focus specifically on embryo evaluation and selection, as this is currently the most active research area of applying AI within IVF, with more than 10 papers published in 2020.…”