Deep Learning Meets Biosensing For Virus Detection

Scientists at the Gwangju Institute of Science and Technology have developed a tool, DeepGT, which combines a Gires-Tournois immunosensor with deep learning algorithms to count viral particles with enhanced accuracy. DeepGT refines visual artefacts and colour deviations, offering substantial improvements over traditional rule-based algorithms, and could revolutionise the early screening and triage of emerging viruses. The tool is based on the Gires-Tournois immunosensor, a photonic resonator that uses light and colour to detect the presence of viruses, and deep learning, a subset of machine learning that uses algorithms to mimic the way humans learn.