Revolutionising diagnostics with real-time nanomotion analysis
Resistell’s Phenotech platform is redefining antibiotic susceptibility testing (AST) with a breakthrough method that detects cellular vibrations at the nanoscale. WAAT plays a key role in the platform’s success by designing and engineering the machine learning algorithms that power real-time classification of bacterial responses to antibiotics. Our contribution enables a predictive modelling layer that is core to delivering results in hours rather than days or weeks.
The brief: Create ML algorithms to power a diagnostic platform with real-time insight
WAAT was tasked with developing the machine learning backbone of Resistell’s diagnostic platform. The goal: create a robust pipeline for analysing cellular nanomotion signals and classifying antimicrobial responses across diverse bacterial strains and conditions. The challenge was to deliver a solution that could meet stringent clinical standards in speed, accuracy, and reproducibility—within the real-time constraints of a live diagnostic device.
Digital solutions: ML-powered signal interpretation for nanomotion diagnostics
01
Custom-built ML algorithms for AST
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.
02
Predictive models for multiple pathogens and 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.
03
Feature selection and model tuning
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.
04
Built-in scalability and cloud integration
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.
Outcome
With WAAT’s machine learning integration, Resistell’s Phenotech platform transforms the diagnostic timeline for antibiotic susceptibility from days to mere hours. The technology has demonstrated over 95% predictive accuracy for bloodstream infections, and near-perfect classification performance for tuberculosis susceptibility. Our ML systems also enable real-time phage susceptibility testing, opening new frontiers for personalised treatments and AMR research.
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