We engineered a classification model pipeline to translate nanomotion signals into accurate predictions of bacterial susceptibility. Our algorithms are trained to recognise subtle shifts in vibrational variance that occur when bacteria are exposed to antibiotics.

WAAT’s models have been deployed for a range of pathogens, including E. coli, K. pneumoniae, and Mycobacterium tuberculosis. For tuberculosis, our ML system achieves 97–100% sensitivity and specificity for classifying isoniazid and rifampicin resistance.

Using advanced feature extraction techniques and leave-one-out cross-validation (LOOCV), our team developed high-performance models that can distinguish subtle signal behaviours in nanomotion recordings. These predictive models adapt to different bacteria-antibiotic combinations, and can be tuned for faster turnaround and new diagnostic categories.

We designed the ML system to work seamlessly within Resistell’s cloud-based data analysis platform. From signal acquisition to classification output, our algorithms operate in a scalable infrastructure that supports both clinical and research workflows.