ASTAI
ASTAI provides an intelligent antimicrobial system (iAMS) that applies machine learning to MALDI-TOF mass spectrometry data to rapidly predict antimicrobial resistance profiles from clinical isolates. The platform enables clinical microbiologists to identify resistant pathogens like methicillin-resistant Staphylococcus aureus within hours rather than days, supporting earlier targeted therapy. The system has been validated on over 20,000 clinical isolates and shown to improve outcomes in bacteraemia patients.
- Healthcare Technology
- Artificial Intelligence
- Digital Health
- Data & Analytics
- Software Only
Funding
Founders
Founder details are not available yet.
Product
Problem
Clinical microbiologists face significant delays and uncertainty in identifying antimicrobial resistance, particularly for pathogens like methicillin-resistant Staphylococcus aureus and carbapenem-resistant Klebsiella pneumoniae. Traditional culture-based methods are slow, and interpreting MALDI-TOF MS data for resistance prediction requires specialized expertise, leading to delayed targeted therapy and worse patient outcomes.
Solution
ASTAI provides an intelligent antimicrobial system (iAMS) that applies machine learning to MALDI-TOF mass spectrometry data to rapidly predict antimicrobial resistance profiles. The platform analyzes clinical isolates to identify resistant pathogens within hours rather than days, enabling earlier targeted antimicrobial therapy. By combining rapid Sepsityper MALDI-TOF MS with proprietary machine learning models, ASTAI delivers actionable intelligence directly to clinical microbiologists. The system has been validated on over 20,000 clinical isolates and shown to improve clinical outcomes in bacteraemia patients.
Target Audience
Primary customers are clinical microbiology laboratories in hospitals and reference centers that perform MALDI-TOF MS testing and need faster, more accurate antimicrobial resistance predictions for bloodstream infections.
Features
- Machine learning-based MALDI-TOF MS analysis for rapid prediction of methicillin-resistant Staphylococcus aureus and carbapenem-resistant Klebsiella pneumoniae
- Validated on large clinical cohorts exceeding 20,000 isolates for robust resistance pattern identification
- Integration with rapid Sepsityper workflow to flag blood cultures within hours of positivity
- Clinical outcomes evidence showing reduced mortality in bacteraemia caused by resistant organisms after implementation
- Peer-reviewed publications supporting the technology's accuracy and clinical utility
- Ongoing research extending the approach to Mycobacterium tuberculosis complex detection