A new AI- and proteomics-based tool could improve disease surveillance accuracy on poultry farms
Researchers at the University of California are developing an innovative early detection system for highly pathogenic avian influenza (HPAI) that combines protein analysis (proteomics) with artificial intelligence. The project has received a US$1.8 million grant from the U.S. Department of Agriculture’s Animal and Plant Health Inspection Service (USDA APHIS).
Led by virologist Rong Hai, the project aims to create a next-generation disease surveillance tool capable of detecting and tracking the presence and spread of the virus on poultry farms with greater accuracy than current diagnostic methods.
Limitations of Current Diagnostic Methods
Today, avian influenza surveillance primarily relies on tests that detect the virus’s genetic material (DNA or RNA). Although these methods are highly sensitive, they can produce ambiguous results, including false positives caused by sample contamination or the detection of viral fragments that remain after an infection has already cleared.
According to the researchers, these tests do not always distinguish between an active infection and residual viral material, nor can they reliably identify the exact source of an outbreak.
A New Approach: Analyzing Viral Proteins
Instead of searching for viral DNA or RNA, the new system focuses on proteins produced during infection.
These protein biomarkers provide a more reliable indication of an active infection and may also help researchers trace how the virus moves between different animal species.
Because viruses rely on host cells to replicate, they leave behind a unique biological signature composed of both viral and host proteins. By analyzing these protein patterns, scientists hope to reconstruct transmission pathways between species, farms, and production systems.
The Role of Artificial Intelligence
To process the enormous volume of biological data generated by proteomic analysis, the research team is using artificial intelligence.
AI algorithms can identify specific protein signatures among millions of possible combinations, significantly improving the speed and accuracy of disease detection.
According to the researchers, while conventional proteomics typically analyzes thousands of proteins, the new platform processes far more complex datasets, making AI an essential component of the system.
The technology also incorporates multiple cross-validation mechanisms to reduce false-positive results and improve the overall reliability of diagnostics.
Potential Impact on the Poultry Industry
The new system has the potential to transform how infectious diseases are monitored in the poultry sector.
Earlier and more accurate detection of HPAI outbreaks would enable producers and veterinary authorities to respond more quickly, contain outbreaks more effectively, and reduce the economic losses associated with large-scale flock depopulation and quarantine measures.
According to project leader Rong Hai, understanding both the origin of the virus and the routes by which it spreads is essential for implementing targeted control measures and minimizing the impact of future outbreaks.
Although the technology is still under development, it is viewed as a promising advancement in biological surveillance that could strengthen the resilience of the poultry industry against future epidemic threats.
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