Integrating Machine Learning and Sensors for the Development of Organ-on-Chip Platforms for Medical Diagnosis

The development of microfluidics-based devices has opened the way to tremendous advances in many different biomedical contexts, as for example, organ-on-chip (OOC) experiments. However, to exploit the full potential of this technology, the integration with sensors and the analysis of experimental data are also necessary. In this paper, some examples of how we can improve the OOC performances through the development of ad-hoc sensors and the application of machine learning algorithms to process the huge amount of data collected in the OOC experiments are shown.