Artificial intelligence in the in vitro fertilization laboratory: a review of advancements over the last decade

The integration of artificial intelligence (AI) and deep learning algorithms into medical care has been the focus of development over the last decade, particularly in the field of assisted reproductive technologies and in vitro fertilization (IVF). With embryo morphology the cornerstone of clinical decision making for IVF, the field of IVF is highly reliant on visual assessments that can be prone to error and subjectivity and be dependent on the level of training and expertise of the observing embryologist. Implementing AI algorithms into the IVF laboratory allows for reliable, objective, and timely assessments of both clinical parameters and microscopy images. This review discusses the ever-expanding applications of AI algorithms within the IVF embryology laboratory, aiming to discuss the many advances in multiple aspects of the IVF process. We will discuss how AI will improve various processes and procedures such as assessing oocyte quality, sperm selection, fertilization assessment, embryo assessment, ploidy prediction, embryo transfer selection, cell tracking, embryo witnessing, micromanipulation, and quality management. Overall, AI provides great potential and promise to improve not only clinical outcomes but also laboratory efficiency, a key focus because IVF clinical volume continues to increase nationwide.

Automated summary

The integration of AI and deep learning algorithms in medical care, particularly in the field of assisted reproductive technologies and IVF, has been a focus of development over the last decade. AI algorithms provide reliable, objective, and timely assessments for various processes in the IVF laboratory, improving clinical outcomes and laboratory efficiency. This review discusses the expanding applications of AI algorithms in IVF embryology, covering multiple aspects of the IVF process.