The prospect of artificial intelligence to personalize assisted reproductive technology

Simon  Hanassab; Ali  Abbara; Arthur C.  Yeung; Margaritis  Voliotis; Krasimira  Tsaneva-Atanasova; Tom Kelsey; Geoffrey H.  Trew; Scott M.  Nelson; Thomas  Heinis; Waljit S.  Dhillo

doi:10.1038/s41746-024-01006-x

The prospect of artificial intelligence to personalize assisted reproductive technology

Simon Hanassab, Ali Abbara, Arthur C. Yeung, Margaritis Voliotis, Krasimira Tsaneva-Atanasova, Tom Kelsey, Geoffrey H. Trew, Scott M. Nelson, Thomas Heinis, Waljit S. Dhillo^*

^*Corresponding author for this work

Research output: Contribution to journal › Review article › peer-review

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.

Original language	English
Article number	55
Number of pages	19
Journal	npj Digital Medicine
Volume	7
Issue number	55
DOIs	https://doi.org/10.1038/s41746-024-01006-x
Publication status	Published - 1 Mar 2024

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title = "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.",

author = "Simon Hanassab and Ali Abbara and Yeung, {Arthur C.} and Margaritis Voliotis and Krasimira Tsaneva-Atanasova and Tom Kelsey and Trew, {Geoffrey H.} and Nelson, {Scott M.} and Thomas Heinis and Dhillo, {Waljit S.}",

note = "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).",

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doi = "10.1038/s41746-024-01006-x",

language = "English",

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AU - Hanassab, Simon

AU - Abbara, Ali

AU - Yeung, Arthur C.

AU - Voliotis, Margaritis

AU - Tsaneva-Atanasova, Krasimira

AU - Kelsey, Tom

AU - Trew, Geoffrey H.

AU - Nelson, Scott M.

AU - Heinis, Thomas

AU - Dhillo, Waljit S.

N1 - 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).

PY - 2024/3/1

Y1 - 2024/3/1

N2 - 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.

AB - 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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DO - 10.1038/s41746-024-01006-x

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SN - 2398-6352

VL - 7

JO - npj Digital Medicine

JF - npj Digital Medicine

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M1 - 55

ER -

The prospect of artificial intelligence to personalize assisted reproductive technology

Abstract

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