College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China.
Computing and Intelligence Department, Institute of High Performance Computing (IHPC), Agency for Science, Technology and Research (A*STAR), 1 Fusionopolis Way, #16-16 Connexis, Singapore 138632, Singapore.
Biosensors (Basel). 2023 Mar 15;13(3):389. doi: 10.3390/bios13030389.
Organs-on-chips (OoCs) are miniature microfluidic systems that have arguably become a class of advanced in vitro models. Deep learning, as an emerging topic in machine learning, has the ability to extract a hidden statistical relationship from the input data. Recently, these two areas have become integrated to achieve synergy for accelerating drug screening. This review provides a brief description of the basic concepts of deep learning used in OoCs and exemplifies the successful use cases for different types of OoCs. These microfluidic chips are of potential to be assembled as highly potent human-on-chips with complex physiological or pathological functions. Finally, we discuss the future supply with perspectives and potential challenges in terms of combining OoCs and deep learning for image processing and automation designs.
器官芯片(Organs-on-chips,OoCs)是一种微型微流控系统,可以说是一种先进的体外模型。深度学习作为机器学习中的一个新兴主题,具有从输入数据中提取隐藏统计关系的能力。最近,这两个领域已经结合起来,以实现协同作用,加速药物筛选。本文简要介绍了深度学习在 OoCs 中的基本概念,并举例说明了不同类型 OoCs 的成功应用案例。这些微流控芯片具有组装成具有复杂生理或病理功能的高通量人体芯片的潜力。最后,我们讨论了未来在图像处理和自动化设计方面结合 OoCs 和深度学习的观点和潜在挑战。