AI on a chip

Abstract

<p>The excellent synergy between artificial intelligence and lab-on-a-chip technology is described with applications.</p>

Microfluidic formation of crystal-like structures

“…Finally, the advent of machine learning and artificial intelligence can provide a significant boost to the field (see the recent review by Isozaki et al 141 ). The complexity of solving a multi-body problem associated to crystal formation could be tackled by using machine learning techniques with predictive outcomes, thus leading to a AI-aided technology for crystal design.…”

Droplet flow cytometry for single-cell analysis

“…Moreover, imaging ow cytometry (IFC) has become an attractive tool for single-cell analysis in diverse biomedical elds, and a two-dimensional image signal has been employed for real-time classication and sorting of droplets encapsulating with cancer 131,132 and planktonic cells. 133 Combining with high-speed image-based sorting, [134][135][136][137][138][139] this will eventually be used in the process of isolating individual cells exhibiting properties of interest. With further advances in sensitivity and specicity of label-free optical detection techniques, we expect that droplet encapsulation and compartmentalization will expand into a more versatile platform technology for highthroughput single-cell analysis.…”

The Synergy between Organ-on-a-Chip and Artificial Intelligence for the Study of NAFLD: From Basic Science to Clinical Research

“…In the past years, various lines of investigation have proved that NAFLD extends beyond the liver paving the way to multiorgan failure, and eventually death (Figure 2). data such as images, drug-target interactions and computational biology [16][17][18][19]. These AI-based techniques employ algorithms that learn without direct programming overcoming the limitation of human recognition boosting human knowledge in various fields.…”

“…One of the most promising approaches is to employ machine learning and artificial intelligence to metabolize these unprecedent amount of data. Although traditional machine learning offers advanced data processing capabilities, the advent of its most important component, the deep learning, made possible to analyze massive unstructured data such as images, drug-target interactions and computational biology [16][17][18][19]. These AI-based techniques employ algorithms that learn without direct programming overcoming the limitation of human recognition boosting human knowledge in various fields.…”