Automation in ART: Paving the Way for the Future of Infertility Treatment

Abstract

In vitro fertilisation (IVF) is estimated to account for the birth of more than nine million babies worldwide, perhaps making it one of the most intriguing as well as commoditised and industrialised modern medical interventions. Nevertheless, most IVF procedures are currently limited by accessibility, affordability and most importantly multistep, labour-intensive, technically challenging processes undertaken by skilled professionals. Therefore, in order to sustain the exponential demand for IVF on one hand, and streamline existing processes on the other, innovation is essential. This may not only effectively manage clinical time but also reduce cost, thereby increasing accessibility, affordability and efficiency. Recent years have seen a diverse range of technologies, some integrated with artificial intelligence, throughout the IVF pathway, which promise personalisation and, at least, partial automation in the not-so-distant future. This review aims to summarise the rapidly evolving state of these innovations in automation, with or without the integration of artificial intelligence, encompassing the patient treatment pathway, gamete/embryo selection, endometrial evaluation and cryopreservation of gametes/embryos. Additionally, it shall highlight the resulting prospective change in the role of IVF professionals and challenges of implementation of some of these technologies, thereby aiming to motivate continued research in this field. Graphical abstract

Data-Driven Medicine in the Diagnosis and Treatment of Infertility

“…Advanced algorithms associated with ML or AI have found their way into modern medicine and are transforming many aspects of patient care. Infertility and reproductive health are catching up [ 151 ]; however, much more can be done to improve the chance of conception for infertile couples. The major limitations include access to enough good-quality data to produce algorithms that are reliable and reproducible on a global scale.…”

Laser-Guided Sperm Selection: Optimizing the Reproductive Success Rate in Assisted Reproductive Technology

“…Robotic devices could manage the laser target and sperm manipulating processes, reducing the possibility of human error and outcome unpredictability. This ensures that the laser is precisely directed to the appropriate sperm cells, hence enhancing the selection process [ 30 ].…”

Evolution of the basic semen analysis and processing sperm

“…This technology can assist but cannot replace embryologists or infertility specialists. It must be able to be appropriately transitioned into clinical practice before being standardized [54 ▪ ].…”

“…Although machine learning may have higher processing potential and reduced human bias, algorithmic bias can be a limitation of this technology [54 ▪ ]. This technology can assist but cannot replace embryologists or infertility specialists.…”