TheEngineRoom, Department of Informatics Bioengineering, Robotics and System Engineering, University of Genoa, Via Opera Pia 13, Genoa, 16131, Italy.
TheEngineRoom, Department of Informatics Bioengineering, Robotics and System Engineering, University of Genoa, Via Opera Pia 13, Genoa, 16131, Italy.
Artif Intell Med. 2024 Nov;157:102997. doi: 10.1016/j.artmed.2024.102997. Epub 2024 Oct 8.
Nowadays, the most adopted technique to address infertility problems is in vitro fertilisation (IVF). However, its success rate is limited, and the associated procedures, known as assisted reproduction technology (ART), suffer from a lack of objectivity at the laboratory level and in clinical practice. This paper deals with applications of Artificial Intelligence (AI) techniques to IVF procedures. Artificial intelligence is considered a promising tool for ascertaining the quality of embryos, a critical step in IVF. Since the oocyte quality influences the final embryo quality, we present a systematic review of the literature on AI-based techniques used to assess oocyte quality; we analyse its results and discuss several promising research directions. In particular, we highlight how AI-based techniques can support the IVF process and examine their current applications as presented in the literature. Then, we discuss the challenges research must face in fully deploying AI-based solutions in current medical practice. Among them, the availability of high-quality data sets as well as standardised imaging protocols and data formats, the use of physics-informed simulation and machine learning techniques, the study of informative, descriptive yet observable features, and, above all, studies of the quality of oocytes and embryos, specifically about their live birth potential. An improved understanding of determinants for oocyte quality can improve success rates while reducing costs, risks for long-term embryo cultures, and bioethical concerns.
如今，解决不孕问题最常用的方法是体外受精（IVF）。然而，其成功率有限，而且相关的程序，即辅助生殖技术（ART），在实验室和临床实践层面缺乏客观性。本文涉及人工智能（AI）技术在 IVF 程序中的应用。人工智能被认为是确定胚胎质量的有前途的工具，而胚胎质量是 IVF 的关键步骤。由于卵母细胞的质量会影响最终的胚胎质量，我们对基于 AI 的技术在评估卵母细胞质量方面的应用进行了系统的文献回顾；我们分析了其结果，并讨论了几个有前途的研究方向。特别是，我们强调了基于 AI 的技术如何能够支持 IVF 过程，并检查了它们在文献中呈现的当前应用。然后，我们讨论了研究在将基于 AI 的解决方案全面应用于当前医学实践中必须面对的挑战。其中包括高质量数据集的可用性，以及标准化的成像协议和数据格式，物理信息模拟和机器学习技术的使用，信息性、描述性但可观察特征的研究，以及最重要的是卵母细胞和胚胎质量的研究，特别是关于它们的活产潜力。对卵母细胞质量决定因素的更好理解可以提高成功率，同时降低成本、长期胚胎培养的风险和生物伦理问题。
