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Review
. 2024 Mar 1;7(1):55.
doi: 10.1038/s41746-024-01006-x.

The prospect of artificial intelligence to personalize assisted reproductive technology

Simon Hanassab #  1   2   3 , Ali Abbara #  1   4 , Arthur C Yeung  1   4 , Margaritis Voliotis  5   6   7 , Krasimira Tsaneva-Atanasova  5   6   7 , Tom W Kelsey  8 , Geoffrey H Trew  1   9 , Scott M Nelson  9   10   11 , Thomas Heinis  2 , Waljit S Dhillo  12   13
Affiliations
Review

The prospect of artificial intelligence to personalize assisted reproductive technology

Simon Hanassab et al. NPJ Digit Med. .

Abstract

Infertility affects 1-in-6 couples, with repeated intensive cycles of assisted reproductive technology (ART) required by many to achieve a desired live birth. In ART, typically, clinicians and laboratory staff consider patient characteristics, previous treatment responses, and ongoing monitoring to determine treatment decisions. However, the reproducibility, weighting, and interpretation of these characteristics are contentious, and highly operator-dependent, resulting in considerable reliance on clinical experience. Artificial intelligence (AI) is ideally suited to handle, process, and analyze large, dynamic, temporal datasets with multiple intermediary outcomes that are generated during an ART cycle. Here, we review how AI has demonstrated potential for optimization and personalization of key steps in a reproducible manner, including: drug selection and dosing, cycle monitoring, induction of oocyte maturation, and selection of the most competent gametes and embryos, to improve the overall efficacy and safety of ART.

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Conflict of interest statement

A.A. has received grants from the BRC; and has provided consulting services for Myovant Sciences Ltd. G.H.T. has stock in TFP; has received honoraria and travel support from Ferring Pharmaceuticals; and has provided consultancy services to ARC Medical Inc. S.M.N. received grants from NIHR, CSO, and BRC; provided consultancy services for Access Fertility, Modern Fertility, TFP, and Ferring Pharmaceuticals; received honoraria from Ferring Pharmaceuticals and Merck; received support for attending meetings and/or travel from Ferring Pharmaceuticals and Merck; participated in a data safety monitoring board or advisory board for NIHR; owns stock or stock options in TFP. W.S.D. received grants from NIHR, MRC, and Imperial Health Charity, and is a Consultant for Myovant Sciences Ltd. The remaining authors declare no competing interests.

Figures

Fig. 1
Fig. 1. Potential targets for artificial intelligence in assisted reproductive technology.
Potential targets for the application of artificial intelligence and machine learning methods during clinical and embryological steps in assisted reproductive technology (ART). Investigations of infertility and pre-treatment counseling are not captured here and discussed independently in Section Pre-treatment counseling. The order and timings of the steps can differ depending on the ART protocol used. Figure created with BioRender.com.
Fig. 2
Fig. 2. The artificial intelligence landscape.
A Venn diagram providing a holistic view of the artificial intelligence (AI) landscape, with a particular focus on machine learning (ML) methods. ML is a subfield that is often used in conjunction with other AI subfields, such as computer vision. Some methods can be used in alternative learning frameworks however their most common current manifestations are presented here.
See this image and copyright information in PMC

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