AI in the treatment of fertility: key considerations
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OPINION
AI in the treatment of fertility: key considerations Jason Swain 1 & Matthew Tex VerMilyea 2 & Marcos Meseguer 3 & Diego Ezcurra 4
&
Fertility AI Forum Group
Received: 15 July 2020 / Accepted: 13 September 2020 # Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract Artificial intelligence (AI) has been proposed as a potential tool to help address many of the existing problems related with empirical or subjective assessments of clinical and embryological decision points during the treatment of infertility. AI technologies are reviewed and potential areas of implementation of algorithms are discussed, highlighting the importance of following a proper path for the development and validation of algorithms, including regulatory requirements, and the need for ecosystems containing enough quality data to generate it. As evidenced by the consensus of a group of experts in fertility if properly developed, it is believed that AI algorithms may help practitioners from around the globe to standardize, automate, and improve IVF outcomes for the benefit of patients. Collaboration is required between AI developers and healthcare professionals to make this happen. Keywords fertility . AI . algorithms . embryos
Introduction The treatment of infertility has continually evolved since the birth of Louise Brown in 1978. However, a constant factor during this evolution has remained; that success in assisted reproductive technologies (ART) is in the details. Indeed, ART success relies on a complex series of interrelated events, with each procedural step having several variables and impacting the other. Therefore, to excel in ART and achieve the ultimate goal of a healthy singleton live birth in the shortest time span possible, it is necessary to optimize the success and efficiency in each of these procedural steps and variables. Despite continued advancements in the development of new drugs, innovative stimulation protocols, and lab technologies, success rates continue to still tend in only average around one-third of patients taking home a baby [1].
* Diego Ezcurra [email protected] 1
CCRM Fertility Network, Lone Tree, CO, USA
2
Ovation Fertility and Texas Fertility Centers, Austin and San Antonio, TX, USA
3
Instituto Valenciano de Infertilidad (IVI) Valencia, INCLIVA-Universidad de Valencia, Valencia, Spain
4
EMD Serono, One Technology Place, Rockland MA02370, USA
Notably, reported success rates of some centers are considerably higher than others. These significant differences in ART outcomes observed between countries [2, 3] and even individual clinics [4] are likely a result of patient variability and the minute details of these ART procedural steps. In recent years, the development and implementation of artificial intelligence (AI) technology have shown the potential to address inefficiencies in various steps of ART, including the standardization of some IVF laboratory processes and particularly in embryo selection. Indeed, AI is also being proposed for clinical applications in
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