The prospect of artificial intelligence to personalize assisted reproductive technology
dc.contributor.author | Hanassab, S | |
dc.contributor.author | Abbara, A | |
dc.contributor.author | Yeung, AC | |
dc.contributor.author | Voliotis, M | |
dc.contributor.author | Tsaneva-Atanasova, K | |
dc.contributor.author | Kelsey, TW | |
dc.contributor.author | Trew, GH | |
dc.contributor.author | Nelson, SM | |
dc.contributor.author | Heinis, T | |
dc.contributor.author | Dhillo, WS | |
dc.date.accessioned | 2024-03-04T10:21:46Z | |
dc.date.issued | 2024-03-01 | |
dc.date.updated | 2024-03-02T13:20:10Z | |
dc.description.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. | en_GB |
dc.description.sponsorship | UK Research and Innovation | en_GB |
dc.description.sponsorship | National Institute for Health Research (NIHR) | en_GB |
dc.description.sponsorship | Engineering and Physical Sciences Research Council (EPSRC) | en_GB |
dc.description.sponsorship | National Institute for Health Research | en_GB |
dc.identifier.citation | Vol. 7, No. 1, article 55 | en_GB |
dc.identifier.doi | https://doi.org/10.1038/s41746-024-01006-x | |
dc.identifier.grantnumber | EP/S023283/1 | en_GB |
dc.identifier.grantnumber | CS-2018-18-ST2-002 | en_GB |
dc.identifier.grantnumber | EP/ T017856/1 | en_GB |
dc.identifier.grantnumber | NIHR202371 | en_GB |
dc.identifier.uri | http://hdl.handle.net/10871/135458 | |
dc.language.iso | en | en_GB |
dc.publisher | Nature Research | en_GB |
dc.rights | © The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http:// creativecommons.org/licenses/by/4.0/. | en_GB |
dc.title | The prospect of artificial intelligence to personalize assisted reproductive technology | en_GB |
dc.type | Article | en_GB |
dc.date.available | 2024-03-04T10:21:46Z | |
exeter.article-number | 55 | |
dc.description | This is the final version. Available from Nature Research via the DOI in this record. | en_GB |
dc.description | DATA AVAILABILITY: Data sharing is not applicable to this article as no datasets were generated or analyzed during the current study | en_GB |
dc.identifier.eissn | 2398-6352 | |
dc.identifier.journal | npj Digital Medicine | en_GB |
dc.relation.ispartof | npj Digital Medicine, 7(1) | |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | en_GB |
dcterms.dateAccepted | 2024-01-10 | |
rioxxterms.version | VoR | en_GB |
rioxxterms.licenseref.startdate | 2024-03-01 | |
rioxxterms.type | Journal Article/Review | en_GB |
refterms.dateFCD | 2024-03-04T10:16:46Z | |
refterms.versionFCD | VoR | |
refterms.dateFOA | 2024-03-04T10:21:51Z | |
refterms.panel | A | en_GB |
refterms.dateFirstOnline | 2024-03-01 |
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Commons licence, and indicate if changes were made. The images or other third party
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indicated otherwise in a credit line to the material. If material is not included in the
article’s Creative Commons licence and your intended use is not permitted by statutory
regulation or exceeds the permitted use, you will need to obtain permission directly
from the copyright holder. To view a copy of this licence, visit http://
creativecommons.org/licenses/by/4.0/.