An Overview of Organs-on-Chips Based on Deep Learning

TL;DR

This paper reviews how deep learning can enhance organs-on-chips technology for drug development and personalized medicine.

Contribution

The paper provides a comprehensive overview of integrating deep learning with organs-on-chips for biomedical applications.

Findings

Deep learning can automate data analysis from high-parallel organs-on-chips systems.

Combining deep learning with OoCs improves image digitization and system automation.

Challenges remain in fully integrating deep learning with OoCs, requiring further research.

Abstract

Microfluidic-based organs-on-chips (OoCs) are a rapidly developing technology in biomedical and chemical research and have emerged as one of the most advanced and promising in vitro models. The miniaturization, stimulated tissue mechanical forces, and microenvironment of OoCs offer unique properties for biomedical applications. However, the large amount of data generated by the high parallelization of OoC systems has grown far beyond the scope of manual analysis by researchers with biomedical backgrounds. Deep learning, an emerging area of research in the field of machine learning, can automatically mine the inherent characteristics and laws of “big data” and has achieved remarkable applications in computer vision, speech recognition, and natural language processing. The integration of deep learning in OoCs is an emerging field that holds enormous potential for drug development, disease modeling, and personalized medicine. This review briefly describes the basic concepts and mechanisms of microfluidics and deep learning and summarizes their successful integration. We then analyze the combination of OoCs and deep learning for image digitization, data analysis, and automation. Finally, the problems faced in current applications are discussed, and future perspectives and suggestions are provided to further strengthen this integration.

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