人工智能在辅助生殖技术领域的应用

doi:10.12280/gjszjk.20260367

摘要/Abstract

摘要：

体外受精-胚胎移植（in vitro fertilization-embryo transfer，IVF-ET）作为辅助生殖技术（assisted reproductive technology，ART）的核心手段，其临床结局常受限于配子与胚胎质量评估的主观性、实验室操作误差及个体化方案制定的复杂性。人工智能（artificial intelligence，AI）作为前沿科技手段，可深度融入IVF-ET治疗全周期，在卵巢刺激、配子与胚胎评估、实验室管理及临床决策中发挥关键辅助作用。其核心机制在于利用机器学习与深度学习算法强大的数据处理能力，通过整合海量临床数据，精准预测促排卵药物剂量并优化卵泡监测；基于图像识别技术，实现精子活力与胚胎发育潜能的自动化、客观化评级与非侵入性整倍性预测；结合电子见证系统与机器人技术，显著提升样本追踪安全性及显微操作标准化水平。AI技术全流程、多维度的辅助优势，能有效降低人为误差，为提升IVF-ET临床妊娠率、推动ART向精准医疗模式转变提供关键技术支撑。尽管现有AI技术仍面临数据标准化不足等挑战，其持续优化与临床转化将有力推动ART规范化实践与技术创新。

关键词: 人工智能, 生殖技术, 辅助, 机器学习, 深度学习, 精准医疗, 胚胎评估, 配子选择

Abstract:

In vitro fertilization-embryo transfer (IVF-ET), as a core method of assisted reproductive technology (ART), often faces the limitations of clinical outcomes due to the subjectivity of gamete and embryo quality assessment, laboratory procedural errors, and the complexity of developing individualized treatment plans. Artificial intelligence (AI), as a cutting-edge technological tool, can be deeply integrated into the entire IVF-ET treatment cycle, playing a key supporting role in ovarian stimulation, gamete and embryo assessment, laboratory management, and clinical decision-making. Its core mechanism lies in leveraging the powerful data processing capabilities of machine learning and deep learning algorithms to integrate vast amounts of clinical data, thereby accurately predicting the drug dosage of ovulation induction, and optimizing the follicle monitoring; utilizing the image recognition technology to enable the automated and objective grading of sperm motility and embryonic developmental potential, as well as non-invasive karyotype prediction; and combining the electronic witnessing system with robotic technology to significantly enhance the safety of sample tracking and the standardization of micromanipulation procedures. The comprehensive and multi-dimensional support provided by AI technology throughout the entire process effectively reduces human error, offering a critical technical support for improving the clinical pregnancy rates of IVF-ET and advancing ART toward a precision medicine model. Although existing AI technologies still face challenges, such as insufficient data standardization, their continuous optimization and clinical translation will strongly promote the standardized ART practices and technological innovation.

Key words: Artificial intelligence, Reproductive technology, assisted, Machine learning, Deep learning, Precision medicine, Embryo assessment, Gamete selection

冯宇, 张宁, 席瑾, 袁榕澧, 刘奇, 赵娴, 王梦静. 人工智能在辅助生殖技术领域的应用[J]. 国际生殖健康/计划生育杂志, 2026, 45(5): 353-357.

FENG Yu, ZHANG Ning, XI Jin, YUAN Rong-li, LIU Qi, ZHAO Xian, WANG Meng-jing. The Application of Artificial Intelligence in Assisted Reproductive Technology[J]. Journal of International Reproductive Health/Family Planning, 2026, 45(5): 353-357.

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