AI system could transform rabies testing around the world

The breakthrough is designed to help improve surveillance of one of the world’s deadliest infectious diseases and deliver practical, laboratory-ready tools that can be adopted in endemic countries.

Despite being almost entirely preventable through vaccination, rabies causes around 59,000 human deaths every year. Current practices for diagnosis and measuring protective antibodies through vaccination still rely on specialists manually examining thousands of microscope images – which can be slow, labour-intensive and open to differences in interpretation. 

In a DEFRA-funded study published in Nature Scientific Reports, the research team created the first AI-based workflow capable of identifying rabies virus infection in cell cultures and accurately measuring antibody concentration levels.  

Using more than 2,300 fluorescence images containing over 36,000 annotated image sections, the team trained several deep learning models to recognise rabies virus infection within cells. The best-performing model correctly classified 99.6 per cent of samples and almost perfectly distinguished infected from uninfected cells, avoiding false alarms. 

The technology could help laboratories analyse samples more quickly and consistently, particularly in countries where rabies remains endemic and specialist expertise is limited. It could also reliably measure antibody levels used to assess vaccine protection and support international pet travel requirements.

The results establish a framework that can be adapted for the detection, analysis, and surveillance of other pathogens, supporting broader applications of AI across infectious disease research and diagnostics. 

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Notes to editors 

• Professor Miroslaw Bober is available for interview; please contact mediarelations@surrey.ac.uk to arrange. 
• The full paper can be found here: https://www.nature.com/articles/s41598-026-57955-8#citeas