In this project, we are investigating whether it is possible to train deep learning models to identify bacterial species growing in a microfluidic trap based on their spatiotemporal division patterns. This has important implications for selecting appropriate antibiotics, reducing the use of broad-spectrum agents, and improving patient outcomes. We have previously demonstrated this approach using laboratory isolates in several publications. In this final year, we aim to extend the method to clinical patient isolates. Alvis has been properly acknowledged in all our previous publications (mentioned in the activity reports).