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

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 in...

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Bibliographic Details
Published in:Biosensors (Basel) 2023-03, Vol.13 (3), p.389
Main Authors: Dai, Manna, Xiao, Gao, Shao, Ming, Zhang, Yu Shrike
Format: Article
Language:English
Subjects:
Artificial intelligence
Automation
Biology
Clinical trials
Datasets
Deep Learning
Drug Evaluation, Preclinical - methods
Drug screening
Drugs
Efficiency
High-Throughput Screening Assays
human-on-chips
Humans
Image processing
Machine learning
microfluidic systems
Microfluidics
Microfluidics - methods
Microphysiological Systems
Neural networks
Organs
organs-on-chips
Product/Service Evaluations
Review
Screening
Success
Transplants & implants
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Summary: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.
ISSN:2079-6374
2079-6374
DOI:10.3390/bios13030389