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Filtration & fluid control


Better together


Microfluidics requires high levels of expertise, so simplifying it would promote its broader use in healthcare and life sciences. Integrated machine learning and AI could allow people who are not experts in physics and engineering to run next-generation diagnostic tests, potentially improving treatment for many diseases, not least cancer. Jim Banks talks convergence with DeepCell co-founders Maddison Masaeli and Mahyar Salek, and Ali Lashkaripour, a research assistant in Boston University’s Cross-disciplinary Integration of Design Automation Research (CIDAR) group.


he ability to manipulate fluids at the micron scale has opened up a new realm of analytical processes, enabling relatively cheap devices to perform several functions simultaneously with tiny volumes of reagents. In a device the size of a computer memory stick, it is now possible to simulate an entire human organ by lining microfluidic channels with organ-specific cells. Microfluidics provide an unprecedented wealth of data, but it has always required a high degree of technical skill. Most obviously, microfluidics presents an engineering challenge, as it requires tiny amounts of liquid to pass through channels that might only span 10µm. The technical expertise required has made microfluidic devices hard to replicate at scale, hence the drive to make them simpler to use and to add layers of digital technology to glean more insight from the data they can generate. “We believe that leapfrog advances happen more often when technologies converge,” says Mahyar Salek, CTO, president and co-founder of Deepcell, which is


T Medical Device Developments / www.nsmedicaldevices.com


launching a new platform for identifying and isolating cells based on morphological distinctions. “The next big advance in microfluidics will be its combination with AI and other technologies.”


AI is perfectly suited to identifying patterns in large volumes of data and automating repetitive tasks. Machine learning (ML) enables a system to learn from data and improve its accuracy over time without being programmed to do so. Both are part of the growing trend towards digital microfluidics (DMF) as a platform for lab-on-a-chip systems.


“I envision AI and ML greatly simplifying the design process, real-time control and data analysis of microfluidic devices,” says Ali Lashkaripour, a research assistant in Boston University’s Cross-disciplinary Integration of Design Automation Research (CIDAR) group. “Given enough data points, ML can take the engineering or the art out of any complex process. One of the most challenging and resource-intensive steps of developing microfluidic devices for diagnostics is coming up with a design that delivers the desired


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Andrii Symonenko/Shutterstock.com


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