Todhunter Michael E, Jubair Sheikh, Verma Ruchika, Saqe Rikard, Shen Kevin, Duffy Breanna
Todhunter Scientifics, Minneapolis, MN, United States.
Alberta Machine Intelligence Institute, Edmonton, AB, Canada.
Front Artif Intell. 2024 Sep 24;7:1424012. doi: 10.3389/frai.2024.1424012. eCollection 2024.
Cultured meat has the potential to provide a complementary meat industry with reduced environmental, ethical, and health impacts. However, major technological challenges remain which require time-and resource-intensive research and development efforts. Machine learning has the potential to accelerate cultured meat technology by streamlining experiments, predicting optimal results, and reducing experimentation time and resources. However, the use of machine learning in cultured meat is in its infancy. This review covers the work available to date on the use of machine learning in cultured meat and explores future possibilities. We address four major areas of cultured meat research and development: establishing cell lines, cell culture media design, microscopy and image analysis, and bioprocessing and food processing optimization. In addition, we have included a survey of datasets relevant to CM research. This review aims to provide the foundation necessary for both cultured meat and machine learning scientists to identify research opportunities at the intersection between cultured meat and machine learning.
培养肉有潜力为肉类行业提供补充,同时减少对环境、伦理和健康的影响。然而,主要的技术挑战依然存在,这需要耗费大量时间和资源的研发工作。机器学习有潜力通过简化实验、预测最佳结果以及减少实验时间和资源来加速培养肉技术的发展。然而,机器学习在培养肉中的应用尚处于起步阶段。本综述涵盖了迄今为止关于机器学习在培养肉中应用的现有研究,并探讨了未来的可能性。我们阐述了培养肉研发的四个主要领域:建立细胞系、细胞培养基设计、显微镜检查和图像分析,以及生物加工和食品加工优化。此外,我们还纳入了对与培养肉研究相关数据集的调查。本综述旨在为培养肉科学家和机器学习科学家提供必要的基础,以便他们识别培养肉与机器学习交叉领域的研究机会。