This article has been reviewed according to Science X's editorial process and policies. Editors have highlighted the following attributes while ensuring the content's credibility: Rabies virus detection in cell cultures could soon become faster and more accessible, thanks to the world's first AI system for this purpose, developed by researchers at the University of Surrey's Institute for People-Centered AI (PAI) in collaboration with the Rabies and Viral Zoonoses team at the Animal and Plant Health Agency (APHA). The system 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 following vaccination still rely on specialists manually examining thousands of microscope images—which can be slow, labor-intensive and open to differences in interpretation. In a study published in Scientific Reports, the research team created the first AI-based workflow capable of identifying rabies virus infection in cell cultures and accurately measuring antibody concentrations.
Using more than 2,300 fluorescence images containing over 36,000 annotated image sections, the team trained several deep learning models to recognize rabies virus infection within cells. The best-performing model correctly classified 99.6% of samples and almost perfectly distinguished infected from uninfected cells, avoiding false alarms. "Rabies is one of the world's oldest and deadliest infectious diseases, claiming tens of thousands of lives every year because timely diagnosis and surveillance remain challenging in many parts of the world.
Once clinical signs appear, it is almost always fatal, so prevention and early diagnosis are essential. "Our AI workflow provides laboratories with a faster and more consistent way to analyze samples while reducing the reliance on manual interpretation," said Joan Manuel Amaya Cuesta, viral zoonoses research scientist. The technology could help laboratories analyze 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. "This research shows how AI can support scientists by carrying out complex image analysis quickly, consistently and at scale. While we've demonstrated this approach for rabies, the same framework could be applied to other infectious diseases that rely on similar laboratory tests, helping researchers and diagnostic laboratories make better use of AI in the future," said Professor Miroslaw Bober, professor of video processing and machine intelligence.
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. "We hope this innovation will help strengthen rabies surveillance and, ultimately, support global efforts to eliminate human deaths from this disease. This technology is now being further developed to detect the virus in infected brain tissue, which we hope will transform international rabies diagnostic laboratories," said Dr.
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