The COVID-19 pandemic has shown the importance of medical testing for an early detection of regional disease hot spots and for monitoring the course of the pandemic. In particular, the coupling of medical biosensors with concepts of machine learning has the potential to meet the requirements for efficient and robust detection of current and future pathogens. The lecture illustrates this with the example of the plasmon-assisted microscopy sensor that can make nanometer-sized particles (e.g., viruses) visible. The principle of operation of the sensor and the concept for the detection of nanometer-sized particles is explained. The challenge is that the analysis is carried out on the basis of data-intensive and very noisy or artefact afflicted image sequences and that the processing of the image sequences should be done in (soft) real-time while minimising resource consumption, e.g., of energy and memory. The lecture is thus at the same time an introduction to the hackathon.