Artificial intelligence and machine learning applications for cultured meat

TL;DR

This paper reviews how machine learning can accelerate cultured meat development by optimizing experiments and processes, highlighting current research and future opportunities in this emerging interdisciplinary field.

Contribution

It provides a comprehensive overview of machine learning applications in cultured meat research, identifying key areas and future research directions.

Findings

Machine learning can streamline cell line establishment.

Optimization of cell culture media using ML.

Image analysis enhances cultured meat quality assessment.

Abstract

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. 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.