Artificial Intelligence Integration with Microfluidics
Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 4524
Interests: organ-on-chip; heterogenous microfluidics; microfluidics automation; machine learning for biomedical image analysis; clinical laboratory science
Interests: cell-based microanalysis; electrotaxis; microfluidic biochip development and applications; microarray technologies; laser micro machining
Special Issues, Collections and Topics in MDPI journals
Advances in micro- and nano-fabrication, as well as applying fundamental fluid dynamics in microscale to biomedical applications, have yielded great works under the discipline of microfluidics. Micro total analysis systems, also known as labs-on-a-chip systems, have been created through miniaturizing and integrating microfluidics, as well as control and detection components, providing high-throughput and multiplex measurements as well as manipulations of analytes in a small configuration. Microphysiological systems also enable the control of a microenvironment where fundamental biological processes can be studied in a quantitative manner, eventually providing reliable and clinically translatable results that would in turn relieve the need of animal models in drug discovery and fundamental research. However, as microsystems have become more complex, system design and fabrication have become more dependent on experience. Moreover, the increasing amount of data provided by advanced microfluidic platforms has made data analysis the bottleneck of applying microsystems in research.
In recent years, advances in deep convolutional neural networks in the field of deep learning has successfully solved many of the Big Data analytical problems, such as pattern recognition, classification, and segmentation of targets in conventionally complex data collected from microsystems, thereby integrating the fourth boom of artificial intelligence with microfluidics.
In this Special Issue, we would like to highlight the benefits and possibilities brought by a microfluidic system integrated with machine learning and deep learning techniques for fundamental biomedical discovery, as well as practical applications.
We look forward to receiving your submissions.
Dr. Paul Hsieh-Fu Tsai
Prof. Dr. Ji-Yen Cheng
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- artificial intelligence system
- computer-aided chip design
- smart micro- and nano-fabrication
- image analysis and metrology
- big data mining
- pattern recognition
- cell culture
- microphysiological system
- cell analysis and manipulation
- droplet generation and sorting
- biosensing applications