SOURCE-LINKED INTELLIGENCE

OUTBreak AI-driven Detection: Enhancing MALDI-TOF outbreak detection with multimodal AI integrating epidemiological and genomic data

CORDIS · observation · Publication date unknown

s, external validation. OUTBRAID will develop two breakthrough approaches: (1) diffusion models to generate synthetic MALDI-TOF spectra, ensuring robust, cross-center performance; and (2) multimodal machine learning that combines MALDI-TOF, epidemiological metadata, and WGS to enable automated, high-resolution cluster detection. These models will be benchmarked not only at species level but also at sequence type (ST), cgMLST, and single-nucleotide polymorphism (SNP) resolution—pushing MALDI-TOF as close as possible to the granularity of WGS. All data, models, and code will be open access. OUTBRAID’s participation in workshops and training will reach and train 150+ scientists and clinicians (e.g., ESCMID), ensuring broad impact. By braiding together international data streams and AI, OUTBRAID aims to deliver scalable tools for early outbreak detection, supporting hospitals and public health globally. bacterial typing, MALDI-TOF, machine learning, anomaly detection, outbreak surveillance, hospital-acquired infections

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recordType

award

status

SIGNED

region

EU

value

307958.88

unit

EUR