![]() ![]() Another platform is label-free cell sorting which relies primarily on forward- and side-scattered signals in the absence of fluorescence labels. Here, fluorescent labels are used to identify desired cells within a heterogeneous population. The most common platform in which ML enables microfluidics to automate cell sorting is fluorescence-activated cell sorting. Machine Learning enables live cell sorting, classification and visualization by automating data analysis. However, these advances produce large volumes of data and images to analyze. This platform enables new approache s in the diagnosis of diseases, single-cell characterization and analysis, cell imaging and pathogen detections. Cells are loaded in the cartridge and, using a reader, are automatically counted and classified. Microfluidics has revolutionized this process in small, easy-to-use and disposable cartridges. That’s a lot of data and images to be reviewed and analyzed. Characterizing these cells in 20 minutes, requires interrogating cells at the rate of 5000 cells per seconds. In 1 µL of blood from a healthy man, there are around 4 to 6 million cells, and around 4 to 5 million cells from a healthy woman. For example, blood cells size ranges from 2-10 um in diameter (diameter of human hair is around 70 um). This procedure is very tedious and subject to human error s. In cell culture laboratories, cells are often counted and classified in real-time using microscope and manually operated counters. These images are then analyzed by counting and classifying the characteristics of each specific cell. tagging with fluorescent materials) and different light sources. In biology, cell sorting and classification are traditionally implemented by taking pictures under the microscope with sample labeling (i.e. Microfluidic cell sorter and classifier.This is critical for medical devices that have to clear regulatory submissions and cannot be changed without new clinical validation studies. Importantly, software developers can also use Machine Learning to create an algorithm that can be ‘locked’ so that its function does not change in the field. The accuracy and efficacy of the algorithms depends on the training data the more data used in the process the more accurate the results will be. In general, Machine Learning in microfluidics is used in protocol processes, optical detection, and image sorting, classification and pattern recognition, large data analysis and comparison, forecasting and prediction. Cloud platforms can have the advantage of being linked to super computers to allow for faster processing and storage of reference data sets. Depending on the application, various detectors can be used (i.e., camera or photomultiplier tube (PMT), electronic sensors and biosensors) to obtain the target results after which the data is collected inside internal processors or by using cloud platforms. Furthermore, microfluidics is used to prepare the analytes for detection and quantification. Instead, the cartridge facilitates sample processing and implementation of assay protocols inside microfluidic channels and reservoirs. The microfluidic cartridge by itself does not generate data. Analyzing these large data sets would not be possible without ML thus enabling the use of microfluidics in rapid disease diagnosis. As the samples pass through the detection area in a microfluidic cartridge, the sensor captures data, and this information is processed in an internal processor or computer. In order to train the algorithms, they need to be fed large data sets (training data) to build models and make predictions or decisions without being explicitly programmed to do so.įor instance, a microfluidic point-of-care diagnostic instrument for detecting diseases by classifying biomarkers in blood, urine, saliva and sweat, generates huge quantities of images that need to be analyzed in a short period of time. Machine Learning covers a broad spectrum of activities such as algorithm development, predictive modelling, classification and learning, neural networks and others. It has been used in many applications particularly in miniaturizing laboratory processes and procedures. Microfluidics is a fluid handling technique which takes place inside micro-channels or pathways using nano/microliter volumes. Lorenzo Gutierrez in Biotech, Eastern Region, Microfluidics | No comments Five Applications Using Microfluidic Machine Learning Microfluidics is a powerful tool on its own, but over the past years, the combination of microfluidics and Machine Learning (ML) has enabled microfluidic technologies to push into new areas and applications.
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