Artificial intelligence to identify antifungal susceptibility in the clinical diagnostic laboratory

Project Code

MRCIIAR26Br Dowsey

Project Type

Wet lab

Research Theme

Infection, Immunity, Antimicrobial Resistance and Repair

Project Summary Download

Summary

Fungal infections have become a major and growing global health crisis, causing an estimated 3.8 million deaths each year particularly among immunocompromised populations. The problem is compounded by the rise of drug-resistant fungal strains and the lack of effective diagnostics. Working with clinical experts in the NHS and abroad, you will adapt data science models and lead development of new deep learning approaches to diagnose critical fungal infections such as azole-resistant Aspergillus fumigatus and echinocandin-resistant Candida glabrata using mass spectrometry techniques available to the diagnostic microbiology lab.

Lead Supervisor

Professor Andrew Dowsey

Lead Supervisor Email

andrew.dowsey@bristol.ac.uk

University Affiliation

Bristol