Olawade David B, Teke Jennifer, Adeleye Khadijat K, Weerasinghe Kusal, Maidoki Momudat, Clement David-Olawade Aanuoluwapo
Department of Allied and Public Health, School of Health, Sport and Bioscience, University of East London, London, United Kingdom; Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom; Department of Public Health, York St John University, London, United Kingdom.
Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom; Faculty of Medicine, Health and Social Care, Canterbury Christ Church University, United Kingdom.
J Gynecol Obstet Hum Reprod. 2025 Mar;54(3):102903. doi: 10.1016/j.jogoh.2024.102903. Epub 2024 Dec 27.
In-vitro fertilization (IVF) has been a transformative advancement in assisted reproductive technology. However, success rates remain suboptimal, with only about one-third of cycles resulting in pregnancy and fewer leading to live births. This narrative review explores the potential of artificial intelligence (AI), machine learning (ML), and deep learning (DL) to enhance various stages of the IVF process. Personalization of ovarian stimulation protocols, gamete selection, and embryo annotation and selection are critical areas where AI may benefit significantly. AI-driven tools can analyze vast datasets to predict optimal stimulation protocols, potentially improving oocyte quality and fertilization rates. In sperm and oocyte quality assessment, AI can offer precise, objective analyses, reducing subjectivity and standardizing evaluations. In embryo selection, AI can analyze time-lapse imaging and morphological data to support the prediction of embryo viability, potentially aiding implantation outcomes. However, the role of AI in improving clinical outcomes remains to be confirmed by large-scale, well-designed clinical trials. Additionally, AI has the potential to enhance quality control and workflow optimization within IVF laboratories by continuously monitoring key performance indicators (KPIs) and facilitating efficient resource utilization. Ethical considerations, including data privacy, algorithmic bias, and fairness, are paramount for the responsible implementation of AI in IVF. Future research should prioritize validating AI tools in diverse clinical settings, ensuring their applicability and reliability. Collaboration among AI experts, clinicians, and embryologists is essential to drive innovation and improve outcomes in assisted reproduction. AI's integration into IVF holds promise for advancing patient care, but its clinical potential requires careful evaluation and ongoing refinement.
体外受精（IVF）是辅助生殖技术的一项变革性进展。然而，成功率仍不尽人意，只有约三分之一的周期能导致怀孕，而能实现活产的更少。这篇叙述性综述探讨了人工智能（AI）、机器学习（ML）和深度学习（DL）在增强IVF过程各个阶段的潜力。卵巢刺激方案的个性化、配子选择以及胚胎注释和选择是AI可能显著受益的关键领域。AI驱动的工具可以分析大量数据集以预测最佳刺激方案，有可能提高卵母细胞质量和受精率。在精子和卵母细胞质量评估中，AI可以提供精确、客观的分析，减少主观性并使评估标准化。在胚胎选择方面，AI可以分析延时成像和形态学数据以支持对胚胎活力的预测，有可能有助于着床结果。然而，AI在改善临床结果方面的作用仍有待大规模、精心设计的临床试验来证实。此外，AI有潜力通过持续监测关键绩效指标（KPI）并促进高效资源利用来提高IVF实验室的质量控制和工作流程优化。伦理考量，包括数据隐私、算法偏差和公平性，对于在IVF中负责任地实施AI至关重要。未来的研究应优先在不同临床环境中验证AI工具，确保其适用性和可靠性。AI专家、临床医生和胚胎学家之间的合作对于推动辅助生殖领域的创新和改善结果至关重要。将AI整合到IVF中有望推进患者护理，但其临床潜力需要仔细评估和持续完善。
