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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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Detalles Bibliográficos
Autores principales: Dai, Manna, Xiao, Gao, Shao, Ming, Zhang, Yu Shrike
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10046732/
https://www.ncbi.nlm.nih.gov/pubmed/36979601
http://dx.doi.org/10.3390/bios13030389
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author Dai, Manna
Xiao, Gao
Shao, Ming
Zhang, Yu Shrike
author_facet Dai, Manna
Xiao, Gao
Shao, Ming
Zhang, Yu Shrike
author_sort Dai, Manna
collection PubMed
description 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.
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spelling pubmed-100467322023-03-29 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 Biosensors (Basel) 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 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. MDPI 2023-03-15 /pmc/articles/PMC10046732/ /pubmed/36979601 http://dx.doi.org/10.3390/bios13030389 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Review
Dai, Manna
Xiao, Gao
Shao, Ming
Zhang, Yu Shrike
The Synergy between Deep Learning and Organs-on-Chips for High-Throughput Drug Screening: A Review
title The Synergy between Deep Learning and Organs-on-Chips for High-Throughput Drug Screening: A Review
title_full The Synergy between Deep Learning and Organs-on-Chips for High-Throughput Drug Screening: A Review
title_fullStr The Synergy between Deep Learning and Organs-on-Chips for High-Throughput Drug Screening: A Review
title_full_unstemmed The Synergy between Deep Learning and Organs-on-Chips for High-Throughput Drug Screening: A Review
title_short The Synergy between Deep Learning and Organs-on-Chips for High-Throughput Drug Screening: A Review
title_sort synergy between deep learning and organs-on-chips for high-throughput drug screening: a review
topic Review
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10046732/
https://www.ncbi.nlm.nih.gov/pubmed/36979601
http://dx.doi.org/10.3390/bios13030389
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