个性化
辅助生殖技术
计算机科学
加权
人工智能
选择(遗传算法)
不育
医学
生物
怀孕
遗传学
放射科
万维网
作者
Simon Hanassab,Ali Abbara,Arthur C. Yeung,Margaritis Voliotis,Krasimira Tsaneva-Atanasova,Tom Kelsey,Geoffrey Trew,Scott M. Nelson,Thomas Heinis,Waljit S. Dhillo
标识
DOI:10.1038/s41746-024-01006-x
摘要
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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