Artificial intelligence and machine learning applications for cultured meat.

Area of Science:

• Food Science
• Biotechnology
• Computer Science

Background:

• Cultured meat offers environmental, ethical, and health benefits but faces significant technological hurdles.
• Machine learning (ML) presents an opportunity to expedite research and development (R&D) in cultured meat (CM).
• Current ML applications in CM are nascent, necessitating a comprehensive overview.

Purpose of the Study:

• To review existing literature on ML applications in cultured meat R&D.
• To identify key areas where ML can address current challenges in CM production.
• To provide a foundation for interdisciplinary research between CM and ML scientists.

Main Methods:

• Literature review of ML applications in cultured meat.
• Analysis of ML's role in four key CM R&D areas: cell line establishment, media design, image analysis, and bioprocessing.
• Survey of relevant datasets for CM research.

Main Results:

• ML can streamline experiments, predict outcomes, and reduce R&D time and resources in CM.
• Identified opportunities for ML in cell line development, media optimization, image analysis, and bioprocessing.
• Highlighted the need for accessible datasets to advance ML in CM.

Conclusions:

• ML holds significant potential to accelerate the advancement of cultured meat technology.
• Interdisciplinary collaboration between ML and CM scientists is crucial for future progress.
• Further research and data sharing are needed to fully leverage ML in cultured meat production.