Project description
Mass spectrometry aims to spot hospital pathogens earlier using AI
Resistant and hypervirulent bacteria are increasingly causing hospital-acquired infections that threaten patients’ lives. Whole genome sequencing (WGS) remains the gold standard for tracing these outbreaks but it is costly, slow and inaccessible to many. Matrix-assisted laser desorption/ionisation time-of-flight (MALDI-TOF) mass spectrometry – a rapid, widely available identification technique – has untapped potential for outbreak detection. With the support of the Marie Skłodowska-Curie Actions programme, the OUTBRAID project aims to develop elevating MALDI-TOF technology to nearly the power of WGS. Leveraging an unprecedented international spectral dataset and synthetic data, it will train multimodal machine learning models to enable rapid, affordable early outbreak detection to mitigate this growing global public health threat.
Objective
Hospital-acquired infections (HAIs) from resistant and hypervirulent bacteria—such as Methicillin-resistant Staphylococcus aureus, Vancomycin-resistant Enterococcus faecium, Carbapenemase-producing Pseudomonas aeruginosa, Carbapenemase-producing Acinetobacter baumannii, and Clostridioides difficile—pose a growing threat to patient safety worldwide. Outbreaks often go undetected until severe harm occurs. While whole genome sequencing (WGS) is the gold standard for outbreak tracing, it is costly, slow and not globally accessible. MALDI-TOF mass spectrometry, in contrast, is rapid and widely used, but its outbreak detection potential remains unexploited.
The OUTBReak AI-driven Detection (OUTBRAID) project will transform hospital outbreak surveillance by leveraging an unprecedented international dataset—over 1.2 million MALDI-TOF spectra already collected at the IMM Zurich, plus new contributions from 9 countries across Europe, Asia, and Australia, including Low and Median Income Countries (LMICs), each providing at least 100 samples per pathogen for rigorous, 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.
Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA)
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Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
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Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European Fellowships
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Call for proposal
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(opens in new window) HORIZON-MSCA-2025-PF
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8006 Zurich
Switzerland
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