The Synergy between Deep Learning and Organs-on-Chips for High-Throughput Drug Screening: A Review

Dai, Manna; Xiao, Gao; Shao, Ming; Zhang, Yu Shrike

doi:10.3390/bios13030389

Open AccessReview

The Synergy between Deep Learning and Organs-on-Chips for High-Throughput Drug Screening: A Review

¹

College of Physics and Information Engineering, Fuzhou University, Fuzhou 350108, China

²

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

³

College of Environment and Safety Engineering, Fuzhou University, Fuzhou 350108, China

⁴

Department of Biomedical Engineering, Tsinghua University, Beijing 100084, China

⁵

Department of Computer and Information Science, College of Engineering, University of Massachusetts Dartmouth, North Dartmouth, MA 02747, USA

⁶

Division of Engineering in Medicine, Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Cambridge, MA 02139, USA

^*

Authors to whom correspondence should be addressed.

^†

These authors contributed equally to this work.

Biosensors 2023, 13(3), 389; https://doi.org/10.3390/bios13030389

Submission received: 17 December 2022 / Revised: 22 February 2023 / Accepted: 7 March 2023 / Published: 15 March 2023

(This article belongs to the Special Issue Lab on a Chip for High-Throughput Drug Screening)

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Abstract

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.

Keywords: organs-on-chips; microfluidic systems; deep learning; drug screening; human-on-chips

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MDPI and ACS Style

Dai, M.; Xiao, G.; Shao, M.; Zhang, Y.S. The Synergy between Deep Learning and Organs-on-Chips for High-Throughput Drug Screening: A Review. Biosensors 2023, 13, 389. https://doi.org/10.3390/bios13030389

AMA Style

Dai M, Xiao G, Shao M, Zhang YS. The Synergy between Deep Learning and Organs-on-Chips for High-Throughput Drug Screening: A Review. Biosensors. 2023; 13(3):389. https://doi.org/10.3390/bios13030389

Chicago/Turabian Style

Dai, Manna, Gao Xiao, Ming Shao, and Yu Shrike Zhang. 2023. "The Synergy between Deep Learning and Organs-on-Chips for High-Throughput Drug Screening: A Review" Biosensors 13, no. 3: 389. https://doi.org/10.3390/bios13030389

APA Style

Dai, M., Xiao, G., Shao, M., & Zhang, Y. S. (2023). The Synergy between Deep Learning and Organs-on-Chips for High-Throughput Drug Screening: A Review. Biosensors, 13(3), 389. https://doi.org/10.3390/bios13030389

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

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The Synergy between Deep Learning and Organs-on-Chips for High-Throughput Drug Screening: A Review

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