The prospect of artificial intelligence to personalize assisted reproductive technology
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.
Citation
Hanassab , S , Abbara , A , Yeung , A C , Voliotis , M , Tsaneva-Atanasova , K , Kelsey , T , Trew , G H , Nelson , S M , Heinis , T & Dhillo , W S 2024 , ' The prospect of artificial intelligence to personalize assisted reproductive technology ' , npj Digital Medicine , vol. 7 , no. 55 , 55 . https://doi.org/10.1038/s41746-024-01006-x
Publication
npj Digital Medicine
Status
Peer reviewed
ISSN
2398-6352Type
Journal item
Description
The Department of Metabolism, Digestion, and Reproduction is funded by grants from the MRC and NIHR. S.H. is supported by the UKRI CDT in AI for Healthcare http://ai4health.io (EP/S023283/1). A.A. is supported by an NIHR Clinician Scientist Award (CS-2018-18-ST2-002). M.V. and K.T.A. are supported by the EPSRC (EP/T017856/1). W.S.D. is supported by an NIHR Senior Investigator Award (NIHR202371).Collections
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