Themed collection AI in Microfluidics
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Revolutionizing microfluidics with artificial intelligence: a new dawn for lab-on-a-chip technologies
Keisuke Goda, Hang Lu, Peng Fei, and Jochen Guck introduce the AI in Microfluidics themed collection, on revolutionizing microfluidics with artificial intelligence: a new dawn for lab-on-a-chip technologies.
Lab Chip, 2023,23, 3737-3740
https://doi.org/10.1039/D3LC90061D
High-throughput microfluidic systems accelerated by artificial intelligence for biomedical applications
This review outlines the current advances of high-throughput microfluidic systems accelerated by AI. Furthermore, the challenges and opportunities in this field are critically discussed as well.
Lab Chip, 2024,24, 1307-1326
https://doi.org/10.1039/D3LC01012K
Functions and applications of artificial intelligence in droplet microfluidics
This review summarizes the implementations of droplet microfluidics based on AI, including droplet generation, biological analysis, and material synthesis.
Lab Chip, 2023,23, 2497-2513
https://doi.org/10.1039/D3LC00224A
Optofluidic imaging meets deep learning: from merging to emerging
We discuss the recent trends in integrating deep-learning (DL) and optofluidic imaging. A holistic understanding of them could incentivize DL-powered optofluidic imaging for advancing a wide range of novel applications in science and biomedicine.
Lab Chip, 2023,23, 1011-1033
https://doi.org/10.1039/D2LC00813K
![Open Access](https://www.rsc-cdn.org/pubs-core/2022.0.147/content/NewImages/open-access-icon-orange.png)
Machine learning for microfluidic design and control
In this review article, we surveyed the applications of machine learning in microfluidic design and microfluidic control.
Lab Chip, 2022,22, 2925-2937
https://doi.org/10.1039/D2LC00254J
Utilizing ChatGPT to assist CAD design for microfluidic devices
GPT-4 was utilized to generate 3D and 2D CAD designs for common microfluidic device components.
Lab Chip, 2023,23, 3778-3784
https://doi.org/10.1039/D3LC00518F
Assessing red blood cell deformability from microscopy images using deep learning
A microfluidic ratchet sorting device is used to separate RBCs based on deformability. Sorted cells are imaged using optical microscopy and are used to train and test a deep learning network to classify the cells based on deformability.
Lab Chip, 2022,22, 26-39
https://doi.org/10.1039/D1LC01006A
Versatility and stability optimization of flow-focusing droplet generators via quality metric-driven design automation
This work presents two new quality metrics for droplet generation, versatility and stability.
Lab Chip, 2023,23, 4997-5008
https://doi.org/10.1039/D3LC00189J
Is AI essential? Examining the need for deep learning in image-activated sorting of Saccharomyces cerevisiae
We experimentally justify the advantages of jumping on the deep learning trend for image-activated budding yeast sorting and validate its applicability towards morphology-based yeast mutant screening.
Lab Chip, 2023,23, 4232-4244
https://doi.org/10.1039/D3LC00556A
Real-time fluorescence imaging flow cytometry enabled by motion deblurring and deep learning algorithms
A real-time single-cell imaging and classification system can directly identify cell types from motion-blur images using a deep learning algorithm.
Lab Chip, 2023,23, 3615-3627
https://doi.org/10.1039/D3LC00194F
Moving perfusion culture and live-cell imaging from lab to disc: proof of concept toxicity assay with AI-based image analysis
We developed a compact perfusion cell culture with integrated wireless detection device for real-time optical monitoring. The platform enables long-term cell growth and cytotoxicity assay where cell viability is quantified using AI software.
Lab Chip, 2023,23, 1603-1612
https://doi.org/10.1039/D2LC00984F
Meta optimization based on real-time benchmarking of multiple surrogate models for autonomous flow synthesis
Introducing meta-optimizer as a new multi-model Bayesian optimization algorithm, consisting of multiple surrogate models addressing the challenge of model selection for autonomous chemical experimentation.
Lab Chip, 2023,23, 1613-1621
https://doi.org/10.1039/D2LC00938B
Analyzing angiogenesis on a chip using deep learning-based image processing
A new algorithm based on deep learning analyzes angiogenic morphogenesis images taken from angiogenesis on a chip. This method can assess the morphology of angiogenesis in great depth using multiple indicators and extract 3D indices from 2D images.
Lab Chip, 2023,23, 475-484
https://doi.org/10.1039/D2LC00983H
![Open Access](https://www.rsc-cdn.org/pubs-core/2022.0.147/content/NewImages/open-access-icon-orange.png)
Effect of capillary fluid flow on single cancer cell cycle dynamics, motility, volume and morphology
Using microfluidics, we isolate cancer cells under fluid flow mimicking sinusoidal capillaries. With deep-learning and FUCCItrack, we analyze 2D/3D time-lapse multi-channel images to study cell cycle dynamics, motility, volume, and morphology.
Lab Chip, 2023,23, 92-105
https://doi.org/10.1039/D2LC00322H
Integrating machine learning and digital microfluidics for screening experimental conditions
A new approach to combine digital microfluidics and machine learning algorithms to enable applications that require high throughput analysis.
Lab Chip, 2023,23, 81-91
https://doi.org/10.1039/D2LC00764A
A machine learning-based framework to design capillary-driven networks
We present a novel approach for the design of capillary-driven microfluidic networks using a machine learning genetic algorithm (ML-GA).
Lab Chip, 2022,22, 4860-4870
https://doi.org/10.1039/D2LC00843B
![Open Access](https://www.rsc-cdn.org/pubs-core/2022.0.147/content/NewImages/open-access-icon-orange.png)
Cell deformability heterogeneity recognition by unsupervised machine learning from in-flow motion parameters
This work demonstrates how a small set of motion parameters uniquely measures a wide range of cell deformability in microfluidics.
Lab Chip, 2022,22, 4871-4881
https://doi.org/10.1039/D2LC00902A
Deep learning-assisted sensitive detection of fentanyl using a bubbling-microchip
Deep learning-enabled smartphone-based image processing has significant advantages in the development of point-of-care diagnostics.
Lab Chip, 2022,22, 4531-4540
https://doi.org/10.1039/D2LC00478J
Deep learning detector for high precision monitoring of cell encapsulation statistics in microfluidic droplets
A dual model object detection system for high precision monitoring of cell encapsulation statistics in microfluidic droplets with comparisons from YOLOv3 and YOLOv5 performance.
Lab Chip, 2022,22, 4067-4080
https://doi.org/10.1039/D2LC00462C
D-CryptO: deep learning-based analysis of colon organoid morphology from brightfield images
D-CryptO is a deep learning-based tool that can be used to analyze colon organoid structural maturity directly from brightfield images. D-CryptO can be applied in many cases such as analyzing organoids following chemotherapeutic drug treatment.
Lab Chip, 2022,22, 4118-4128
https://doi.org/10.1039/D2LC00596D
![Open Access](https://www.rsc-cdn.org/pubs-core/2022.0.147/content/NewImages/open-access-icon-orange.png)
Surfactant-laden droplet size prediction in a flow-focusing microchannel: a data-driven approach
Improving surfactant-laden microdroplet size prediction using data-driven methods.
Lab Chip, 2022,22, 3848-3859
https://doi.org/10.1039/D2LC00416J
Similar color analysis based on deep learning (SCAD) for multiplex digital PCR via a single fluorescent channel
We proposed an explainable deep learning-based method to classify similar fluorescence colors for multiplex digital PCR in a single fluorescent channel.
Lab Chip, 2022,22, 3837-3847
https://doi.org/10.1039/D2LC00637E
Point-of-care SARS-CoV-2 sensing using lens-free imaging and a deep learning-assisted quantitative agglutination assay
To address the persistence of the COVID-19 pandemic, we have developed a novel point-of-care SARS-CoV-2 biosensor. This sensor has a limit of detection within an order of magnitude of traditional PCR and can provide an accurate measure of viral load.
Lab Chip, 2022,22, 3744-3754
https://doi.org/10.1039/D2LC00289B
![Open Access](https://www.rsc-cdn.org/pubs-core/2022.0.147/content/NewImages/open-access-icon-orange.png)
Automated biophysical classification of apoptotic pancreatic cancer cell subpopulations by using machine learning approaches with impedance cytometry
Machine learning applied to impedance cytometry data enables biophysical recognition of cellular subpopulations over the apoptotic progression after gemcitabine treatment of pancreatic cancer cells from tumor xenografts.
Lab Chip, 2022,22, 3708-3720
https://doi.org/10.1039/D2LC00304J
On the robustness of machine learning algorithms toward microfluidic distortions for cell classification via on-chip fluorescence microscopy
Machine learning algorithms for cell classification via on-chip fluorescence microscopy are shown to be robust to microfluidic distortions due to cell displacement during acquisition.
Lab Chip, 2022,22, 3453-3463
https://doi.org/10.1039/D2LC00482H
Artificial intelligence-based classification of peripheral blood nucleated cells using label-free imaging flow cytometry
We developed a method for label-free image identification and classification of peripheral blood nucleated cells flowing in a microfluidic channel, based on the subcellular structures of quantitative phase microscopy images.
Lab Chip, 2022,22, 3464-3474
https://doi.org/10.1039/D2LC00166G
![Open Access](https://www.rsc-cdn.org/pubs-core/2022.0.147/content/NewImages/open-access-icon-orange.png)
Ensemble latent assimilation with deep learning surrogate model: application to drop interaction in a microfluidics device
Upper: predictions using the machine learning surrogate model with ensemble latent assimilation; bottom: recorded experimental images of each corresponding timestep.
Lab Chip, 2022,22, 3187-3202
https://doi.org/10.1039/D2LC00303A
Intelligent nanoscope for rapid nanomaterial identification and classification
Microspheres array based intelligent nanoscope processed data collection for deep learning training. The trained convolutional neural network model classified the different sizes of nanoparticle samples.
Lab Chip, 2022,22, 2978-2985
https://doi.org/10.1039/D2LC00206J
![Open Access](https://www.rsc-cdn.org/pubs-core/2022.0.147/content/NewImages/open-access-icon-orange.png)
Optical feedback control loop for the precise and robust acoustic focusing of cells, micro- and nanoparticles
Replacing a human operator by an open source optical feedback control loop for acoustofluidic focusing of biological cells (e.g. cancer cells in different resonance modes), micro- and nanometer particles results in an improved device performance.
Lab Chip, 2022,22, 2810-2819
https://doi.org/10.1039/D2LC00376G
High-throughput digital pathology via a handheld, multiplexed, and AI-powered ptychographic whole slide scanner
The handheld, do-it-yourself ptychographic whole slide scanner for high-throughput digital pathology applications.
Lab Chip, 2022,22, 2657-2670
https://doi.org/10.1039/D2LC00084A
Integration of a microfluidic multicellular coculture array with machine learning analysis to predict adverse cutaneous drug reactions
Our multicellular coculture array with the integration of machine learning analysis is able to predict adverse cutaneous drug reactions.
Lab Chip, 2022,22, 1890-1904
https://doi.org/10.1039/D1LC01140E
Deciphering impedance cytometry signals with neural networks
A successful outcome of the coupling between microfluidics and AI: neural networks tackle the signal processing challenges of single-cell microfluidic impedance cytometry.
Lab Chip, 2022,22, 1714-1722
https://doi.org/10.1039/D2LC00028H
Deep imaging flow cytometry
A deep-learning-based image restoration method enhances the performance of imaging flow cytometry.
Lab Chip, 2022,22, 876-889
https://doi.org/10.1039/D1LC01043C
![Open Access](https://www.rsc-cdn.org/pubs-core/2022.0.147/content/NewImages/open-access-icon-orange.png)
Speeding up reconstruction of 3D tomograms in holographic flow cytometry via deep learning
Lightweight and reliable deep-CNN for speeding up the computation of the quantitative phase maps of flowing/rolling cells and for retrieving the 3D tomograms of each cell by holographic flow cytometry modality.
Lab Chip, 2022,22, 793-804
https://doi.org/10.1039/D1LC01087E
Neural network-enhanced real-time impedance flow cytometry for single-cell intrinsic characterization
We propose to employ NN-enhanced IFC to achieve both real-time single-cell intrinsic characterization and intrinsic metric-based cell classification at high throughput.
Lab Chip, 2022,22, 240-249
https://doi.org/10.1039/D1LC00755F
Quantitative particle agglutination assay for point-of-care testing using mobile holographic imaging and deep learning
A quantitative particle agglutination assay using mobile holographic imaging and deep learning is demonstrated for point-of-care testing.
Lab Chip, 2021,21, 3550-3558
https://doi.org/10.1039/D1LC00467K