Intelligent Microfluidics: The Convergence of Machine Learning and Microfluidics in Materials Science and Biomedicine

Galan, Edgar A.; Zhao, Haoran; Wang, Xukang; Dai, Qionghai; Huck, Wilhelm T. S.; Ma, Shaohua

doi:10.1016/j.matt.2020.08.034

Cited by 104 publications

(77 citation statements)

References 176 publications

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“…Hence, instead of analyzing the results of the experiment after it is performed, machine learning allows the device to learn from the data and make accurate predictions to guide and optimize the conducted research. This intelligent microfluidics represents the next generation of platforms for drug discovery, nanomaterials, in vitro organ modeling, and developmental biology [151].…”

Section: Emerging and Future Applicationsmentioning

confidence: 99%

Fabrication and Applications of Microfluidic Devices: A Review

Niculescu

Chircov

Bîrcă

et al. 2021

IJMS

347

208

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Microfluidics is a relatively newly emerged field based on the combined principles of physics, chemistry, biology, fluid dynamics, microelectronics, and material science. Various materials can be processed into miniaturized chips containing channels and chambers in the microscale range. A diverse repertoire of methods can be chosen to manufacture such platforms of desired size, shape, and geometry. Whether they are used alone or in combination with other devices, microfluidic chips can be employed in nanoparticle preparation, drug encapsulation, delivery, and targeting, cell analysis, diagnosis, and cell culture. This paper presents microfluidic technology in terms of the available platform materials and fabrication techniques, also focusing on the biomedical applications of these remarkable devices.

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Section: Emerging and Future Applicationsmentioning

confidence: 99%

Fabrication and Applications of Microfluidic Devices: A Review

Niculescu

Chircov

Bîrcă

et al. 2021

IJMS

347

208

View full text Add to dashboard Cite

show abstract

“…It has previously been used in TB chest X-ray diagnosis, which helps to prevent the overdiagnosis or underdiagnosis of TB made by variations in X-rays [174]. Converging microfluidic analysis with machine learning could provide high-throughput accuracy and prediction models in the field of TB [175]. Machine learning algorithms made from large datasets obtained from microfluidic chip arrays will possibly predict antimicrobial resistance to tuberculosis.…”

Section: Discussion and Future Perspectivesmentioning

confidence: 99%

Microfluidics as a Novel Technique for Tuberculosis: From Diagnostics to Drug Discovery

et al. 2021

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Tuberculosis (TB) remains a global healthcare crisis, with an estimated 5.8 million new cases and 1.5 million deaths in 2020. TB is caused by infection with the major human pathogen Mycobacterium tuberculosis, which is difficult to rapidly diagnose and treat. There is an urgent need for new methods of diagnosis, sufficient in vitro models that capably mimic all physiological conditions of the infection, and high-throughput drug screening platforms. Microfluidic-based techniques provide single-cell analysis which reduces experimental time and the cost of reagents, and have been extremely useful for gaining insight into monitoring microorganisms. This review outlines the field of microfluidics and discusses the use of this novel technique so far in M. tuberculosis diagnostics, research methods, and drug discovery platforms. The practices of microfluidics have promising future applications for diagnosing and treating TB.

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“…DNA‐based microfluidic biosensors, enzymes‐based microfluidic biosensors, microfluidic immunosensors are the most common microfluidic‐based biosensors. The application of machine learning on microfluidics, which is called “intelligent microfluidics,” [ 45 ] takes full advantages of the microfluidic‐based biosensor and provides more opportunities. For example, the supervised machine learning of the bacteria‐particle aggregation pattern was investigated.…”

Section: Noninvasive Biosensors and Collected Physiological Informationmentioning

confidence: 99%

Machine Learning‐Reinforced Noninvasive Biosensors for Healthcare

Zhang

Wang

Liu

et al. 2021

Adv Healthcare Materials

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The emergence and development of noninvasive biosensors largely facilitate the collection of physiological signals and the processing of health‐related data. The utilization of appropriate machine learning algorithms improves the accuracy and efficiency of biosensors. Machine learning‐reinforced biosensors are started to use in clinical practice, health monitoring, and food safety, bringing a digital revolution in healthcare. Herein, the recent advances in machine learning‐reinforced noninvasive biosensors applied in healthcare are summarized. First, different types of noninvasive biosensors and physiological signals collected are categorized and summarized. Then machine learning algorithms adopted in subsequent data processing are introduced and their practical applications in biosensors are reviewed. Finally, the challenges faced by machine learning‐reinforced biosensors are raised, including data privacy and adaptive learning capability, and their prospects in real‐time monitoring, out‐of‐clinic diagnosis, and onsite food safety detection are proposed.

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Intelligent Microfluidics: The Convergence of Machine Learning and Microfluidics in Materials Science and Biomedicine

Cited by 104 publications

References 176 publications

Fabrication and Applications of Microfluidic Devices: A Review

Fabrication and Applications of Microfluidic Devices: A Review

Microfluidics as a Novel Technique for Tuberculosis: From Diagnostics to Drug Discovery

Machine Learning‐Reinforced Noninvasive Biosensors for Healthcare

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