1
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Bhaiyya M, Panigrahi D, Rewatkar P, Haick H. Role of Machine Learning Assisted Biosensors in Point-of-Care-Testing For Clinical Decisions. ACS Sens 2024. [PMID: 39145721 DOI: 10.1021/acssensors.4c01582] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 08/16/2024]
Abstract
Point-of-Care-Testing (PoCT) has emerged as an essential component of modern healthcare, providing rapid, low-cost, and simple diagnostic options. The integration of Machine Learning (ML) into biosensors has ushered in a new era of innovation in the field of PoCT. This article investigates the numerous uses and transformational possibilities of ML in improving biosensors for PoCT. ML algorithms, which are capable of processing and interpreting complicated biological data, have transformed the accuracy, sensitivity, and speed of diagnostic procedures in a variety of healthcare contexts. This review explores the multifaceted applications of ML models, including classification and regression, displaying how they contribute to improving the diagnostic capabilities of biosensors. The roles of ML-assisted electrochemical sensors, lab-on-a-chip sensors, electrochemiluminescence/chemiluminescence sensors, colorimetric sensors, and wearable sensors in diagnosis are explained in detail. Given the increasingly important role of ML in biosensors for PoCT, this study serves as a valuable reference for researchers, clinicians, and policymakers interested in understanding the emerging landscape of ML in point-of-care diagnostics.
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Affiliation(s)
- Manish Bhaiyya
- Department of Chemical Engineering and the Russell Berrie Nanotechnology Institute, Technion, Israel Institute of Technology, Haifa 3200003, Israel
- School of Electrical and Electronics Engineering, Ramdeobaba University, Nagpur 440013, India
| | - Debdatta Panigrahi
- Department of Chemical Engineering and the Russell Berrie Nanotechnology Institute, Technion, Israel Institute of Technology, Haifa 3200003, Israel
| | - Prakash Rewatkar
- Department of Mechanical Engineering, Israel Institute of Technology, Haifa 3200003, Israel
| | - Hossam Haick
- Department of Chemical Engineering and the Russell Berrie Nanotechnology Institute, Technion, Israel Institute of Technology, Haifa 3200003, Israel
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2
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Qi X, Zhou Q, Li X, Hu G. Generation of Multiple Concentration Gradients Using a Two-Dimensional Pyramid Array. Anal Chem 2024; 96:856-865. [PMID: 38104274 DOI: 10.1021/acs.analchem.3c04496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
Concentration heterogeneity of diffusible reactants is a prevalent phenomenon in biochemical processes, requiring the generation of concentration gradients for the relevant experiments. In this study, we present a high-density pyramid array microfluidic network for the effective and precise generation of multiple concentration gradients. The complex gradient distribution in the 2D array can be adaptively adjusted by modulating the reactant velocities and concentrations at the inlets. In addition, the unique design of each reaction chamber and mixing block in the array ensures uniform concentrations within each chamber during dynamic changes, enabling large-scale reactions with low reactant volumes. Through detailed numerical simulation of mass transport within the complex microchannel networks, the proposed method allows researchers to determine the desired number of reaction chambers within a given concentration range based on experimental requirements and to quickly obtain the operating conditions with the help of machine learning-based prediction. The effectiveness in generating a multiple concentration gradient environment was further demonstrated by concentration-dependent calcium carbonate crystallization experiments. This device provides a highly efficient mixing and adaptable concentration platform that is well suited for high-throughput and multiplexed reactions.
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Affiliation(s)
- Xinlei Qi
- Department of Engineering Mechanics, State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
| | - Qin Zhou
- Department of Engineering Mechanics, State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
| | - Xuejin Li
- Department of Engineering Mechanics, State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
| | - Guoqing Hu
- Department of Engineering Mechanics, State Key Laboratory of Fluid Power and Mechatronic Systems, Zhejiang University, Hangzhou 310027, China
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3
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Smeraldo A, Ponsiglione AM, Netti PA, Torino E. Artificial neural network modelling hydrodenticity for optimal design by microfluidics of polymer nanoparticles to apply in magnetic resonance imaging. Acta Biomater 2023; 171:440-450. [PMID: 37775077 DOI: 10.1016/j.actbio.2023.09.029] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 09/11/2023] [Accepted: 09/17/2023] [Indexed: 10/01/2023]
Abstract
The engineering of nanoparticles impacts the control of their nano-bio interactions at each level of the delivery pathway. Therefore, optimal nanoparticle physicochemical properties should be identified to favour on-target interactions and deliver efficiently active compounds to a specific target. To date, traditional batch processes do not guarantee the reproducibility of results and low polydispersity index of the nanostructures, while microfluidics has emerged as cost effectiveness, short-production time approach to control the nanoparticle size and size distribution. Several thermodynamic processes have been implemented in microfluidics, such as nanoprecipitation, ionotropic gelation, self-assembly, etc., to produce nanoparticles in a continuous mode and high throughput way. In this work, we show how the Artificial Neural Network (ANN) can be adopted to model the impact of microfluidic parameters (namely, flow rates and polymer concentrations) on the size of the nanoparticles. Promising results have been obtained, with the highest model accuracy reaching 98.9 %, thus confirming the proposed approach's potential applicability for an ANN-guided biopolymer nanoparticle design for biomedical applications. Nanostructures with different degrees of complexity are analysed, and a proof-of-concept machine learning approach is proposed to evaluate Hydrodenticity in biopolymer matrices. STATEMENT OF SIGNIFICANCE: Size, shape and surface charge determine nano-bio interactions of nanoparticles and their ability to target diseases. The ideal nanoparticle design avoids off-target interactions and favours on-target interactions. So, tools enabling the identification of the optimal nanoparticle physicochemical properties for delivery to a specific target are required. In this work, we evaluate the use of Artificial Neural Network (ANN) to analyse the role of microfluidic parameters in predicting the optimal size of the different hydrogel nanoparticles and their ability to trigger Hydrodenticity.
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Affiliation(s)
- Alessio Smeraldo
- Department of Chemical, Materials and Production Engineering, University of Naples "Federico II", Piazzale Tecchio 80, 80125 Naples, Italy; Interdisciplinary Research Center on Biomaterials, CRIB, Piazzale Tecchio 80, 80125 Naples, Italy; Center for Advanced Biomaterials for Health Care, CABHC, Istituto Italiano di Tecnologia, IIT@CRIB, Largo Barsanti e Matteucci 53, 80125 Naples, Italy
| | - Alfonso Maria Ponsiglione
- Department of Chemical, Materials and Production Engineering, University of Naples "Federico II", Piazzale Tecchio 80, 80125 Naples, Italy
| | - Paolo Antonio Netti
- Department of Chemical, Materials and Production Engineering, University of Naples "Federico II", Piazzale Tecchio 80, 80125 Naples, Italy; Interdisciplinary Research Center on Biomaterials, CRIB, Piazzale Tecchio 80, 80125 Naples, Italy; Center for Advanced Biomaterials for Health Care, CABHC, Istituto Italiano di Tecnologia, IIT@CRIB, Largo Barsanti e Matteucci 53, 80125 Naples, Italy
| | - Enza Torino
- Department of Chemical, Materials and Production Engineering, University of Naples "Federico II", Piazzale Tecchio 80, 80125 Naples, Italy; Interdisciplinary Research Center on Biomaterials, CRIB, Piazzale Tecchio 80, 80125 Naples, Italy; Center for Advanced Biomaterials for Health Care, CABHC, Istituto Italiano di Tecnologia, IIT@CRIB, Largo Barsanti e Matteucci 53, 80125 Naples, Italy.
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4
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Sun H, Xie W, Mo J, Huang Y, Dong H. Deep learning with microfluidics for on-chip droplet generation, control, and analysis. Front Bioeng Biotechnol 2023; 11:1208648. [PMID: 37351472 PMCID: PMC10282949 DOI: 10.3389/fbioe.2023.1208648] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2023] [Accepted: 05/25/2023] [Indexed: 06/24/2023] Open
Abstract
Droplet microfluidics has gained widespread attention in recent years due to its advantages of high throughput, high integration, high sensitivity and low power consumption in droplet-based micro-reaction. Meanwhile, with the rapid development of computer technology over the past decade, deep learning architectures have been able to process vast amounts of data from various research fields. Nowadays, interdisciplinarity plays an increasingly important role in modern research, and deep learning has contributed greatly to the advancement of many professions. Consequently, intelligent microfluidics has emerged as the times require, and possesses broad prospects in the development of automated and intelligent devices for integrating the merits of microfluidic technology and artificial intelligence. In this article, we provide a general review of the evolution of intelligent microfluidics and some applications related to deep learning, mainly in droplet generation, control, and analysis. We also present the challenges and emerging opportunities in this field.
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Affiliation(s)
- Hao Sun
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China
- Fujian Provincial Collaborative Innovation Center of High-End Equipment Manufacturing, Fuzhou, China
| | - Wantao Xie
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China
- Fujian Provincial Collaborative Innovation Center of High-End Equipment Manufacturing, Fuzhou, China
| | - Jin Mo
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China
- Fujian Provincial Collaborative Innovation Center of High-End Equipment Manufacturing, Fuzhou, China
| | - Yi Huang
- Centre for Experimental Research in Clinical Medicine, Fujian Provincial Hospital, Fuzhou, China
| | - Hui Dong
- School of Mechanical Engineering and Automation, Fuzhou University, Fuzhou, China
- Fujian Provincial Collaborative Innovation Center of High-End Equipment Manufacturing, Fuzhou, China
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5
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Durve M, Orsini S, Tiribocchi A, Montessori A, Tucny JM, Lauricella M, Camposeo A, Pisignano D, Succi S. Benchmarking YOLOv5 and YOLOv7 models with DeepSORT for droplet tracking applications. THE EUROPEAN PHYSICAL JOURNAL. E, SOFT MATTER 2023; 46:32. [PMID: 37154834 PMCID: PMC10167152 DOI: 10.1140/epje/s10189-023-00290-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/21/2023] [Accepted: 04/15/2023] [Indexed: 05/10/2023]
Abstract
Tracking droplets in microfluidics is a challenging task. The difficulty arises in choosing a tool to analyze general microfluidic videos to infer physical quantities. The state-of-the-art object detector algorithm You Only Look Once (YOLO) and the object tracking algorithm Simple Online and Realtime Tracking with a Deep Association Metric (DeepSORT) are customizable for droplet identification and tracking. The customization includes training YOLO and DeepSORT networks to identify and track the objects of interest. We trained several YOLOv5 and YOLOv7 models and the DeepSORT network for droplet identification and tracking from microfluidic experimental videos. We compare the performance of the droplet tracking applications with YOLOv5 and YOLOv7 in terms of training time and time to analyze a given video across various hardware configurations. Despite the latest YOLOv7 being 10% faster, the real-time tracking is only achieved by lighter YOLO models on RTX 3070 Ti GPU machine due to additional significant droplet tracking costs arising from the DeepSORT algorithm. This work is a benchmark study for the YOLOv5 and YOLOv7 networks with DeepSORT in terms of the training time and inference time for a custom dataset of microfluidic droplets.
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Affiliation(s)
- Mihir Durve
- Center for Life Nano- & Neuro-Science, Fondazione Istituto Italiano di Tecnologia (IIT), viale Regina Elena 295, 00161, Rome, Italy.
| | - Sibilla Orsini
- NEST, Istituto Nanoscienze-CNR and Scuola Normale Superiore, Piazza San Silvestro 12, 56127, Pisa, Italy
- Istituto per le Applicazioni del Calcolo del Consiglio Nazionale delle Ricerche, via dei Taurini 19, 00185, Rome, Italy
| | - Adriano Tiribocchi
- Istituto per le Applicazioni del Calcolo del Consiglio Nazionale delle Ricerche, via dei Taurini 19, 00185, Rome, Italy
| | - Andrea Montessori
- Dipartimento di Ingegneria Civile, Informatica e delle Tecnologie Aeronautiche, Università degli Studi Roma TRE, via Vito Volterra 62, Rome, 00146, Italy
| | - Jean-Michel Tucny
- Center for Life Nano- & Neuro-Science, Fondazione Istituto Italiano di Tecnologia (IIT), viale Regina Elena 295, 00161, Rome, Italy
- Dipartimento di Ingegneria Civile, Informatica e delle Tecnologie Aeronautiche, Università degli Studi Roma TRE, via Vito Volterra 62, Rome, 00146, Italy
| | - Marco Lauricella
- Istituto per le Applicazioni del Calcolo del Consiglio Nazionale delle Ricerche, via dei Taurini 19, 00185, Rome, Italy
| | - Andrea Camposeo
- NEST, Istituto Nanoscienze-CNR and Scuola Normale Superiore, Piazza San Silvestro 12, 56127, Pisa, Italy
| | - Dario Pisignano
- NEST, Istituto Nanoscienze-CNR and Scuola Normale Superiore, Piazza San Silvestro 12, 56127, Pisa, Italy
- Dipartimento di Fisica, Università di Pisa, Largo B. Pontecorvo 3, 56127, Pisa, Italy
| | - Sauro Succi
- Center for Life Nano- & Neuro-Science, Fondazione Istituto Italiano di Tecnologia (IIT), viale Regina Elena 295, 00161, Rome, Italy
- Physics Department, Harvard University, 17 Oxford Street, Cambridge, MA, 02138, USA
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6
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Tsai HF, Podder S, Chen PY. Microsystem Advances through Integration with Artificial Intelligence. MICROMACHINES 2023; 14:826. [PMID: 37421059 DOI: 10.3390/mi14040826] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2023] [Revised: 04/04/2023] [Accepted: 04/06/2023] [Indexed: 07/09/2023]
Abstract
Microfluidics is a rapidly growing discipline that involves studying and manipulating fluids at reduced length scale and volume, typically on the scale of micro- or nanoliters. Under the reduced length scale and larger surface-to-volume ratio, advantages of low reagent consumption, faster reaction kinetics, and more compact systems are evident in microfluidics. However, miniaturization of microfluidic chips and systems introduces challenges of stricter tolerances in designing and controlling them for interdisciplinary applications. Recent advances in artificial intelligence (AI) have brought innovation to microfluidics from design, simulation, automation, and optimization to bioanalysis and data analytics. In microfluidics, the Navier-Stokes equations, which are partial differential equations describing viscous fluid motion that in complete form are known to not have a general analytical solution, can be simplified and have fair performance through numerical approximation due to low inertia and laminar flow. Approximation using neural networks trained by rules of physical knowledge introduces a new possibility to predict the physicochemical nature. The combination of microfluidics and automation can produce large amounts of data, where features and patterns that are difficult to discern by a human can be extracted by machine learning. Therefore, integration with AI introduces the potential to revolutionize the microfluidic workflow by enabling the precision control and automation of data analysis. Deployment of smart microfluidics may be tremendously beneficial in various applications in the future, including high-throughput drug discovery, rapid point-of-care-testing (POCT), and personalized medicine. In this review, we summarize key microfluidic advances integrated with AI and discuss the outlook and possibilities of combining AI and microfluidics.
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Affiliation(s)
- Hsieh-Fu Tsai
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
- Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Keelung City 204, Taiwan
- Center for Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
| | - Soumyajit Podder
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
| | - Pin-Yuan Chen
- Department of Biomedical Engineering, Chang Gung University, Taoyuan City 333, Taiwan
- Department of Neurosurgery, Chang Gung Memorial Hospital, Keelung, Keelung City 204, Taiwan
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7
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Ahmadpour A, Isgor PK, Ural B, Eren BN, Sarabi MR, Muradoglu M, Tasoglu S. Microneedle arrays integrated with microfluidic systems: Emerging applications and fluid flow modeling. BIOMICROFLUIDICS 2023; 17:021501. [PMID: 37153866 PMCID: PMC10162023 DOI: 10.1063/5.0121578] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2022] [Accepted: 02/15/2023] [Indexed: 05/10/2023]
Abstract
Microneedle arrays are patches of needles at micro- and nano-scale, which are competent and versatile technologies that have been merged with microfluidic systems to construct more capable devices for biomedical applications, such as drug delivery, wound healing, biosensing, and sampling body fluids. In this paper, several designs and applications are reviewed. In addition, modeling approaches used in microneedle designs for fluid flow and mass transfer are discussed, and the challenges are highlighted.
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Affiliation(s)
- Abdollah Ahmadpour
- Department of Mechanical Engineering, College of Engineering, Koç University, Türkiye
| | - Pelin Kubra Isgor
- Department of Biomedical Sciences and Engineering, College of Engineering, Koç University, Türkiye
| | - Berk Ural
- Department of Mechanical Engineering, College of Engineering, Koç University, Türkiye
| | - Busra Nimet Eren
- Department of Mechanical Engineering, College of Engineering, Koç University, Türkiye
| | | | - Metin Muradoglu
- Department of Mechanical Engineering, College of Engineering, Koç University, Türkiye
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8
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Chagot L, Quilodrán-Casas C, Kalli M, Kovalchuk NM, Simmons MJH, Matar OK, Arcucci R, Angeli P. Surfactant-laden droplet size prediction in a flow-focusing microchannel: a data-driven approach. LAB ON A CHIP 2022; 22:3848-3859. [PMID: 36106479 DOI: 10.1039/d2lc00416j] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
The control of droplet formation and size using microfluidic devices is a critical operation for both laboratory and industrial applications, e.g. in micro-dosage. Surfactants can be added to improve the stability and control the size of the droplets by modifying their interfacial properties. In this study, a large-scale data set of droplet size was obtained from high-speed imaging experiments conducted on a flow-focusing microchannel where aqueous surfactant-laden droplets were generated in silicone oil. Three types of surfactants were used including anionic, cationic and non-ionic at concentrations below and above the critical micelle concentration (CMC). To predict the final droplet size as a function of flow rates, surfactant type and concentration of surfactant, two data-driven models were built. Using a Bayesian regularised artificial neural network and XGBoost, these models were initially based on four inputs (flow rates of the two phases, interfacial tension at equilibrium and the normalised surfactant concentration). The mean absolute percentage errors (MAPE) show that data-driven models are more accurate (MAPE = 3.9%) compared to semi-empirical models (MAPE = 11.4%). To overcome experimental difficulties in acquiring accurate interfacial tension values under some conditions, both models were also trained with reduced inputs by removing the interfacial tension. The results show again a very good prediction of the droplet diameter. Finally, over 10 000 synthetic data were generated, based on the initial data set, with a Variational Autoencoder (VAE). The high-fidelity of the extended synthetic data set highlights that this method can be a quick and low-cost alternative to study microdroplet formation in future lab on a chip applications, where experimental data may not be readily available.
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Affiliation(s)
- Loïc Chagot
- ThAMeS Multiphase, Department of Chemical Engineering, University College London, UK.
| | - César Quilodrán-Casas
- Data Science Institute, Imperial College London, UK.
- Department of Earth Science and Engineering, Imperial College London, UK
| | - Maria Kalli
- ThAMeS Multiphase, Department of Chemical Engineering, University College London, UK.
| | | | | | - Omar K Matar
- Department of Chemical Engineering, Imperial College London, UK
| | - Rossella Arcucci
- Data Science Institute, Imperial College London, UK.
- Department of Earth Science and Engineering, Imperial College London, UK
| | - Panagiota Angeli
- ThAMeS Multiphase, Department of Chemical Engineering, University College London, UK.
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9
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McIntyre D, Lashkaripour A, Fordyce P, Densmore D. Machine learning for microfluidic design and control. LAB ON A CHIP 2022; 22:2925-2937. [PMID: 35904162 PMCID: PMC9361804 DOI: 10.1039/d2lc00254j] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/19/2022] [Accepted: 06/28/2022] [Indexed: 05/24/2023]
Abstract
Microfluidics has developed into a mature field with applications across science and engineering, having particular commercial success in molecular diagnostics, next-generation sequencing, and bench-top analysis. Despite its ubiquity, the complexity of designing and controlling custom microfluidic devices present major barriers to adoption, requiring intuitive knowledge gained from years of experience. If these barriers were overcome, microfluidics could miniaturize biological and chemical research for non-experts through fully-automated platform development and operation. The intuition of microfluidic experts can be captured through machine learning, where complex statistical models are trained for pattern recognition and subsequently used for event prediction. Integration of machine learning with microfluidics could significantly expand its adoption and impact. Here, we present the current state of machine learning for the design and control of microfluidic devices, its possible applications, and current limitations.
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Affiliation(s)
- David McIntyre
- Biomedical Engineering Department, Boston University, MA, USA
- Biological Design Center, Boston University, Boston, MA, USA.
| | - Ali Lashkaripour
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Polly Fordyce
- Department of Bioengineering, Stanford University, Stanford, CA, USA
- Department of Genetics, Stanford University, Stanford, CA, USA
- Chan-Zuckerberg Biohub, San Francisco, CA, USA
| | - Douglas Densmore
- Biological Design Center, Boston University, Boston, MA, USA.
- Electrical & Computer Engineering Department, Boston University, Boston, MA, USA
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10
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Fukada K, Seyama M. Microfluidic Devices Controlled by Machine Learning with Failure Experiments. Anal Chem 2022; 94:7060-7065. [PMID: 35468282 DOI: 10.1021/acs.analchem.2c00378] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022]
Abstract
A critical microchannel technique is to isolate specific objects, such as cells, in a biological solution. Generally, this particle sorting is achieved by designing a microfluidic device and tuning its control values; however, unpredictable motions of the particle mixture make this approach time-consuming and labor intensive. Here, we show that microfluidic control with reinforced learning characterized by utilizing failure results can maximize the training effect with limited data. This method uses microscopic images of the separation process, including failed conditions (inappropriate flow speeds or dilution rates of biological samples), and insights for efficient learning are provided by setting gradient rewards according to the degree of failure. Once learning is performed in this manner, the optimal separating condition for other related samples can be automatically found. Failed experiments are not wasteful; they increase training data and make it easier to reach correct answers. This device control could be useful in automatic synthetic chemistry, biomedical analysis, and microfabrication robotics.
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Affiliation(s)
- Kenta Fukada
- NTT Device Technology Laboratories, NTT Corporation, 3-1 Morinosato, Wakamiya, Atsugi, Kanagawa 243-0198, Japan
| | - Michiko Seyama
- NTT Device Technology Laboratories, NTT Corporation, 3-1 Morinosato, Wakamiya, Atsugi, Kanagawa 243-0198, Japan
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11
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Rutkowski GP, Azizov I, Unmann E, Dudek M, Grimes BA. Microfluidic droplet detection via region-based and single-pass convolutional neural networks with comparison to conventional image analysis methodologies. MACHINE LEARNING WITH APPLICATIONS 2022. [DOI: 10.1016/j.mlwa.2021.100222] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022] Open
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12
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Bhuiyan NH, Hong JH, Uddin MJ, Shim JS. Artificial Intelligence-Controlled Microfluidic Device for Fluid Automation and Bubble Removal of Immunoassay Operated by a Smartphone. Anal Chem 2022; 94:3872-3880. [PMID: 35179372 DOI: 10.1021/acs.analchem.1c04827] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
There have been tremendous innovations in microfluidic clinical diagnostics to facilitate novel point-of-care testing (POCT) over the past decades. However, the automatic operation of microfluidic devices that minimize user intervention still lacks reliability and repeatability because microfluidic errors such as bubbles and incomplete filling pose a major bottleneck in commercializing the microfluidic devices for clinical testing. In this work, for the first time, various states of microfluid were recognized to control immunodiagnostics by artificial intelligence (AI) technology. The developed AI-controlled microfluidic platform was operated via an Android smartphone, along with a low-cost polymer device to effectuate enzyme-linked immunosorbent assay (ELISA). To overcome the limited machine-learning capability of smartphones, the region-of-interest (ROI) cascading and conditional activation algorithms were utilized herein. The developed microfluidic chip was incorporated with a bubble trap to remove any bubbles detected by AI, which helps in preventing false signals during immunoassay, as well as controlling the reagents' movement with an on-chip micropump and valve. Subsequently, the developed immunosensing platform was tested for conducting real ELISA using a single microplate from the 96-well to detect the Human Cardiac Troponin I (cTnI) biomarker, with a detection limit as low as 0.98 pg/mL. As a result, the developed platform can be envisaged as an AI-based revolution in microfluidics for point-of-care clinical diagnosis.
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Affiliation(s)
- Nabil H Bhuiyan
- Bio-IT Convergence Laboratory, Department of Electronic Convergence Engineering, KwangWoon University, Seoul 01897, South Korea
| | - Jun H Hong
- Bio-IT Convergence Laboratory, Department of Electronic Convergence Engineering, KwangWoon University, Seoul 01897, South Korea
| | - M Jalal Uddin
- Bio-IT Convergence Laboratory, Department of Electronic Convergence Engineering, KwangWoon University, Seoul 01897, South Korea.,BioGeneSys Inc., 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, South Korea
| | - Joon S Shim
- Bio-IT Convergence Laboratory, Department of Electronic Convergence Engineering, KwangWoon University, Seoul 01897, South Korea.,BioGeneSys Inc., 20 Kwangwoon-ro, Nowon-gu, Seoul 01897, South Korea
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13
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Zhang Y, Zhou A, Chen S, Lum GZ, Zhang X. A perspective on magnetic microfluidics: Towards an intelligent future. BIOMICROFLUIDICS 2022; 16:011301. [PMID: 35069962 PMCID: PMC8769766 DOI: 10.1063/5.0079464] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Accepted: 01/02/2022] [Indexed: 05/09/2023]
Abstract
Magnetic microfluidics has been gradually recognized as an area of its own. Both conventional microfluidic platforms have incorporated magnetic actuation for microfluidic operation and microscale object manipulation. Nonetheless, there is still much room for improvement after decades of development. In this Perspective, we first provide a quick review of existing magnetic microfluidic platforms with a focus on the magnetic tools and actuation mechanisms. Next, we discuss several emerging technologies, including magnetic microrobots, additive manufacture, and artificial intelligence, and their potential application in the future development of magnetic microfluidics. We believe that these technologies can eventually inspire highly functional magnetic tools for microfluidic manipulation and coordinated microfluidic control at the system level, which eventually drives magnetic microfluidics into an intelligent system for automated experimentation.
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Affiliation(s)
- Yi Zhang
- School of Electronic Science and Engineering, University of Electronic Science and Technology of China Chengdu, China
- Authors to whom correspondence should be addressed:; ;
and
| | - Aiwu Zhou
- Singapore Centre for 3D Printing, School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
| | - Songlin Chen
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
| | - Guo Zhan Lum
- School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore
- Authors to whom correspondence should be addressed:; ;
and
| | - Xiaosheng Zhang
- School of Electronic Science and Engineering, University of Electronic Science and Technology of China Chengdu, China
- Authors to whom correspondence should be addressed:; ;
and
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14
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Zheng J, Cole T, Zhang Y, Kim J, Tang SY. Exploiting machine learning for bestowing intelligence to microfluidics. Biosens Bioelectron 2021; 194:113666. [PMID: 34600338 DOI: 10.1016/j.bios.2021.113666] [Citation(s) in RCA: 23] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Revised: 09/18/2021] [Accepted: 09/21/2021] [Indexed: 02/06/2023]
Abstract
Intelligent microfluidics is an emerging cross-discipline research area formed by combining microfluidics with machine learning. It uses the advantages of microfluidics, such as high throughput and controllability, and the powerful data processing capabilities of machine learning, resulting in improved systems in biotechnology and chemistry. Compared to traditional microfluidics using manual analysis methods, intelligent microfluidics needs less human intervention, and results in a more user-friendly experience with faster processing. There is a paucity of literature reviewing this burgeoning and highly promising cross-discipline. Therefore, we herein comprehensively and systematically summarize several aspects of microfluidic applications enabled by machine learning. We list the types of microfluidics used in intelligent microfluidic applications over the last five years, as well as the machine learning algorithms and the hardware used for training. We also present the most recent advances in key technologies, developments, challenges, and the emerging opportunities created by intelligent microfluidics.
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Affiliation(s)
- Jiahao Zheng
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Tim Cole
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Yuxin Zhang
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK
| | - Jeeson Kim
- Department of Intelligent Mechatronics Engineering, Sejong University, Seoul, 05006, South Korea.
| | - Shi-Yang Tang
- Department of Electronic, Electrical and Systems Engineering, University of Birmingham, Edgbaston, Birmingham, B15 2TT, UK.
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15
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Durve M, Bonaccorso F, Montessori A, Lauricella M, Tiribocchi A, Succi S. A fast and efficient deep learning procedure for tracking droplet motion in dense microfluidic emulsions. PHILOSOPHICAL TRANSACTIONS. SERIES A, MATHEMATICAL, PHYSICAL, AND ENGINEERING SCIENCES 2021; 379:20200400. [PMID: 34455844 DOI: 10.1098/rsta.2020.0400] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 03/02/2021] [Indexed: 06/13/2023]
Abstract
We present a deep learning-based object detection and object tracking algorithm to study droplet motion in dense microfluidic emulsions. The deep learning procedure is shown to correctly predict the droplets' shape and track their motion at competitive rates as compared to standard clustering algorithms, even in the presence of significant deformations. The deep learning technique and tool developed in this work could be used for the general study of the dynamics of biological agents in fluid systems, such as moving cells and self-propelled microorganisms in complex biological flows. This article is part of the theme issue 'Progress in mesoscale methods for fluid dynamics simulation'.
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Affiliation(s)
- Mihir Durve
- Center for Life Nano Science@La Sapienza, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161 Roma, Italy
- Quantitative Life Sciences Unit, The Abdus Salam International Centre for Theoretical Physics (ICTP), Trieste 34151, Italy
| | - Fabio Bonaccorso
- Center for Life Nano Science@La Sapienza, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161 Roma, Italy
- Istituto per le Applicazioni del Calcolo CNR, via dei Taurini 19, Rome, Italy
- Department of Physics and INFN, University of Rome Tor Vergata, Via della Ricerca Scientifica, 1 00133, Rome, Italy
| | - Andrea Montessori
- Center for Life Nano Science@La Sapienza, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161 Roma, Italy
| | - Marco Lauricella
- Center for Life Nano Science@La Sapienza, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161 Roma, Italy
| | - Adriano Tiribocchi
- Center for Life Nano Science@La Sapienza, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161 Roma, Italy
- Istituto per le Applicazioni del Calcolo CNR, via dei Taurini 19, Rome, Italy
| | - Sauro Succi
- Center for Life Nano Science@La Sapienza, Istituto Italiano di Tecnologia, Viale Regina Elena, 291, 00161 Roma, Italy
- Istituto per le Applicazioni del Calcolo CNR, via dei Taurini 19, Rome, Italy
- Institute for Applied Computational Science, John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, USA
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16
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Cai H, Ao Z, Wu Z, Song S, Mackie K, Guo F. Intelligent acoustofluidics enabled mini-bioreactors for human brain organoids. LAB ON A CHIP 2021; 21:2194-2205. [PMID: 33955446 PMCID: PMC8243411 DOI: 10.1039/d1lc00145k] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/03/2023]
Abstract
Acoustofluidics, by combining acoustics and microfluidics, provides a unique means to manipulate cells and liquids for broad applications in biomedical sciences and translational medicine. However, it is challenging to standardize and maintain excellent performance of current acoustofluidic devices and systems due to a multiplicity of factors including device-to-device variation, manual operation, environmental factors, sample variability, etc. Herein, to address these challenges, we propose "intelligent acoustofluidics" - an automated system that involves acoustofluidic device design, sensor fusion, and intelligent controller integration. As a proof-of-concept, we developed intelligent acoustofluidics based mini-bioreactors for human brain organoid culture. Our mini-bioreactors consist of three components: (1) rotors for contact-free rotation via an acoustic spiral phase vortex approach, (2) a camera for real-time tracking of rotational actions, and (3) a reinforcement learning-based controller for closed-loop regulation of rotational manipulation. After training the reinforcement learning-based controller in simulation and experimental environments, our mini-bioreactors can achieve the automated rotation of rotors in well-plates. Importantly, our mini-bioreactors can enable excellent control over rotational mode, direction, and speed of rotors, regardless of fluctuations of rotor weight, liquid volume, and operating temperature. Moreover, we demonstrated our mini-bioreactors can stably maintain the rotational speed of organoids during long-term culture, and enhance neural differentiation and uniformity of organoids. Comparing with current acoustofluidics, our intelligent system has a superior performance in terms of automation, robustness, and accuracy, highlighting the potential of novel intelligent systems in microfluidic experimentation.
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Affiliation(s)
- Hongwei Cai
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN 47405, USA.
| | - Zheng Ao
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN 47405, USA.
| | - Zhuhao Wu
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN 47405, USA.
| | - Sunghwa Song
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN 47405, USA.
| | - Ken Mackie
- Gill Center for Biomolecular Science, and, Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Feng Guo
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN 47405, USA.
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17
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Ma S, Zhao H, Galan EA. Integrating Engineering, Automation, and Intelligence to Catalyze the Biomedical Translation of Organoids. Adv Biol (Weinh) 2021; 5:e2100535. [PMID: 33984193 DOI: 10.1002/adbi.202100535] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2021] [Revised: 04/21/2021] [Indexed: 12/13/2022]
Abstract
Organoid technology has developed at an impressive speed during the past decade. Still, organoids are not widely used in practical applications as expected. It is believed that this translation can be greatly accelerated with the integration of engineering and artificial intelligence into current research practices. It is proposed that this approach is the missing link to realize key milestones in organoid technology, namely, high-throughput, homogeneous, and standardized production, automated manipulation, and intelligent monitoring, evaluation, and control via integrated on-chip instrumentation and artificial intelligence. It is suggested that organoids-on-a-chip are the ideal platform to achieve these feats. Once these techniques are established and adopted by the scientific community, the rapid translation of organoids may be seen from laboratories to the clinics and pharmaceutical industry.
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Affiliation(s)
- Shaohua Ma
- Tsinghua-Berkeley Shenzhen Institute (TBSI), Shenzhen International Graduate School (SIGS), Tsinghua University, Shenzhen, 518055, China
| | - Haoran Zhao
- Tsinghua-Berkeley Shenzhen Institute (TBSI), Shenzhen International Graduate School (SIGS), Tsinghua University, Shenzhen, 518055, China
| | - Edgar A Galan
- Tsinghua-Berkeley Shenzhen Institute (TBSI), Shenzhen International Graduate School (SIGS), Tsinghua University, Shenzhen, 518055, China
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18
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Abe T, Oh-hara S, Ukita Y. Adoption of reinforcement learning for the intelligent control of a microfluidic peristaltic pump. BIOMICROFLUIDICS 2021; 15:034101. [PMID: 33986901 PMCID: PMC8106535 DOI: 10.1063/5.0032377] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 04/22/2021] [Indexed: 05/26/2023]
Abstract
We herein report a study on the intelligent control of microfluidic systems using reinforcement learning. Integrated microvalves are utilized to realize a variety of microfluidic functional modules, such as switching of flow pass, micropumping, and micromixing. The application of artificial intelligence to control microvalves can potentially contribute to the expansion of the versatility of microfluidic systems. As a preliminary attempt toward this motivation, we investigated the application of a reinforcement learning algorithm to microperistaltic pumps. First, we assumed a Markov property for the operation of diaphragms in the microperistaltic pump. Thereafter, components of the Markov decision process were defined for adaptation to the micropump. To acquire the pumping sequence, which maximizes the flow rate, the reward was defined as the obtained flow rate in a state transition of the microvalves. The present system successfully empirically determines the optimal sequence, which considers the physical characteristics of the components of the system that the authors did not recognize. Therefore, it was proved that reinforcement learning could be applied to microperistaltic pumps and is promising for the operation of larger and more complex microsystems.
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Affiliation(s)
- Takaaki Abe
- Department of Engineering, Integrated Graduate School of Medicine, Engineering, and Agricultural Sciences, University of Yamanashi, 4-3-11 Takeda, Kofu 400-8510, Japan
| | - Shinsuke Oh-hara
- Graduate Faculty of Interdisciplinary Research, University of Yamanashi, 4-3-11 Takeda, Kofu 400-8510, Japan
| | - Yoshiaki Ukita
- Graduate Faculty of Interdisciplinary Research, University of Yamanashi, 4-3-11 Takeda, Kofu 400-8510, Japan
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19
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Damiati SA, Rossi D, Joensson HN, Damiati S. Artificial intelligence application for rapid fabrication of size-tunable PLGA microparticles in microfluidics. Sci Rep 2020; 10:19517. [PMID: 33177577 PMCID: PMC7658240 DOI: 10.1038/s41598-020-76477-5] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 10/27/2020] [Indexed: 12/11/2022] Open
Abstract
In this study, synthetic polymeric particles were effectively fabricated by combining modern technologies of artificial intelligence (AI) and microfluidics. Because size uniformity is a key factor that significantly influences the stability of polymeric particles, therefore, this work aimed to establish a new AI application using machine learning technology for prediction of the size of poly(D,L-lactide-co-glycolide) (PLGA) microparticles produced by diverse microfluidic systems either in the form of single or multiple particles. Experimentally, the most effective factors for tuning droplet/particle sizes are PLGA concentrations and the flow rates of dispersed and aqueous phases in microfluidics. These factors were utilized to develop five different and simple in structure artificial neural network (ANN) models that are capable of predicting PLGA particle sizes produced by different microfluidic systems either individually or jointly merged. The systematic development of ANN models allowed ultimate construction of a single in silico model which consists of data for three different microfluidic systems. This ANN model eventually allowed rapid prediction of particle sizes produced using various microfluidic systems. This AI application offers a new platform for further rapid and economical exploration of polymer particles production in defined sizes for various applications including biomimetic studies, biomedicine, and pharmaceutics.
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Affiliation(s)
- Safa A Damiati
- Department of Pharmaceutics, Faculty of Pharmacy, King Abdulaziz University (KAU), Jeddah, 21589, Saudi Arabia.
| | - Damiano Rossi
- Blacktrace Holdings Ltd (Dolomite Microfluidics), Royston, SG8 5TW, UK
| | - Haakan N Joensson
- Division of Protein Science, KTH Royal Institute of Technology, Stockholm, Sweden
- Novo Nordisk Foundation Center for Biosustainability at KTH, Stockholm, Sweden
| | - Samar Damiati
- Department of Biochemistry, Faculty of Science, King Abdulaziz University (KAU), Jeddah, Saudi Arabia.
- Division of Nanobiotechnology, Department of Protein Science, Science for Life Laboratory, School of Engineering Sciences in Chemistry, Biotechnology and Health, KTH Royal Institute of Technology, Stockholm, Sweden.
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20
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Dabbagh SR, Rabbi F, Doğan Z, Yetisen AK, Tasoglu S. Machine learning-enabled multiplexed microfluidic sensors. BIOMICROFLUIDICS 2020; 14:061506. [PMID: 33343782 PMCID: PMC7733540 DOI: 10.1063/5.0025462] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2020] [Accepted: 12/01/2020] [Indexed: 05/02/2023]
Abstract
High-throughput, cost-effective, and portable devices can enhance the performance of point-of-care tests. Such devices are able to acquire images from samples at a high rate in combination with microfluidic chips in point-of-care applications. However, interpreting and analyzing the large amount of acquired data is not only a labor-intensive and time-consuming process, but also prone to the bias of the user and low accuracy. Integrating machine learning (ML) with the image acquisition capability of smartphones as well as increasing computing power could address the need for high-throughput, accurate, and automatized detection, data processing, and quantification of results. Here, ML-supported diagnostic technologies are presented. These technologies include quantification of colorimetric tests, classification of biological samples (cells and sperms), soft sensors, assay type detection, and recognition of the fluid properties. Challenges regarding the implementation of ML methods, including the required number of data points, image acquisition prerequisites, and execution of data-limited experiments are also discussed.
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Affiliation(s)
| | - Fazle Rabbi
- Department of Mechanical Engineering, Koç University, Sariyer, Istanbul 34450, Turkey
| | | | - Ali Kemal Yetisen
- Department of Chemical Engineering, Imperial College London, London SW7 2AZ, United Kingdom
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21
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Arjun A, Ajith RR, Kumar Ranjith S. Mixing characterization of binary-coalesced droplets in microchannels using deep neural network. BIOMICROFLUIDICS 2020; 14:034111. [PMID: 32549924 PMCID: PMC7274813 DOI: 10.1063/5.0008461] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 05/21/2020] [Indexed: 05/16/2023]
Abstract
Real-time object identification and classification are essential in many microfluidic applications especially in the droplet microfluidics. This paper discusses the application of convolutional neural networks to detect the merged microdroplet in the flow field and classify them in an on-the-go manner based on the extent of mixing. The droplets are generated in PMMA microfluidic devices employing flow-focusing and cross-flow configurations. The visualization of binary coalescence of droplets is performed by a CCD camera attached to a microscope, and the sequence of images is recorded. Different real-time object localization and classification networks such as You Only Look Once and Singleshot Multibox Detector are deployed for droplet detection and characterization. A custom dataset to train these deep neural networks to detect and classify is created from the captured images and labeled manually. The merged droplets are segregated based on the degree of mixing into three categories: low mixing, intermediate mixing, and high mixing. The trained model is tested against images taken at different ambient conditions, droplet shapes, droplet sizes, and binary-fluid combinations, which indeed exhibited high accuracy and precision in predictions. In addition, it is demonstrated that these schemes are efficient in localization of coalesced binary droplets from the recorded video or image and classify them based on grade of mixing irrespective of experimental conditions in real time.
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Affiliation(s)
- A Arjun
- Micro/nanofluidics Research Laboratory, Department of Mechanical Engineering, College of Engineering Trivandrum, Thiruvananathapuram 695016, Kerala, India
| | - R R Ajith
- Micro/nanofluidics Research Laboratory, Department of Mechanical Engineering, College of Engineering Trivandrum, Thiruvananathapuram 695016, Kerala, India
| | - S Kumar Ranjith
- Micro/nanofluidics Research Laboratory, Department of Mechanical Engineering, College of Engineering Trivandrum, Thiruvananathapuram 695016, Kerala, India
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22
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Shi Z, Lai X, Sun C, Zhang X, Zhang L, Pu Z, Wang R, Yu H, Li D. Step emulsification in microfluidic droplet generation: mechanisms and structures. Chem Commun (Camb) 2020; 56:9056-9066. [DOI: 10.1039/d0cc03628e] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Step emulsification for micro- and nano-droplet generation is reviewed in brief, including the emulsion mechanisms and microfluidic devices.
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Affiliation(s)
- Zhi Shi
- State Key Laboratory of Precision Measuring Technology and Instruments
- Tianjin University
- Tianjin
- China
| | - Xiaochen Lai
- State Key Laboratory of Precision Measuring Technology and Instruments
- Tianjin University
- Tianjin
- China
| | - Chengtao Sun
- State Key Laboratory of Precision Measuring Technology and Instruments
- Tianjin University
- Tianjin
- China
| | - Xingguo Zhang
- State Key Laboratory of Precision Measuring Technology and Instruments
- Tianjin University
- Tianjin
- China
| | - Lei Zhang
- State Key Laboratory of Precision Measuring Technology and Instruments
- Tianjin University
- Tianjin
- China
| | - Zhihua Pu
- State Key Laboratory of Precision Measuring Technology and Instruments
- Tianjin University
- Tianjin
- China
| | - Ridong Wang
- State Key Laboratory of Precision Measuring Technology and Instruments
- Tianjin University
- Tianjin
- China
| | - Haixia Yu
- Tianjin Key Laboratory of Biomedical Detecting Techniques and Instruments
- Tianjin University
- Tianjin
- China
| | - Dachao Li
- State Key Laboratory of Precision Measuring Technology and Instruments
- Tianjin University
- Tianjin
- China
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