Themed collection AI in Microfluidics
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.
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.
Machine learning for microfluidic design and control
In this review article, we surveyed the applications of machine learning in microfluidic design and microfluidic control.
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.
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.
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.
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.
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.
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.
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).
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.
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.
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.
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.
Surfactant-laden droplet size prediction in a flow-focusing microchannel: a data-driven approach
Improving surfactant-laden microdroplet size prediction using data-driven methods.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Deep imaging flow cytometry
A deep-learning-based image restoration method enhances the performance of imaging flow cytometry.
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.
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.
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.