AI/ML-Enhanced LAMP Diagnostics for Rapid Veterinary Pathogen Detection
Rapid detection of infectious diseases is critical for protecting animal health, food security, and One Health. While loop-mediated isothermal amplification (LAMP) has become a valuable molecular diagnostic tool, developing reliable assays remains time-consuming and largely dependent on trial-and-error primer design. This presentation demonstrates how artificial intelligence (AI) and machine learning (ML) can transform this process by predicting amplification performance before laboratory validation. Drawing from a series of computational and experimental studies, the session will showcase AI-driven primer-template modeling, simulation of LAMP amplification, and temperature-aware machine learning for improved assay accuracy. Attendees will gain practical insights into how predictive computational biology can accelerate the development of rapid diagnostics for bacterial, viral, and parasitic pathogens, ultimately supporting faster outbreak response, precision veterinary medicine, and global One Health surveillance.
- Learning Outcomes
- After attending this session, participants will be able to:
- Explain how AI and machine learning can improve LAMP assay design and reduce reliance on trial-and-error laboratory optimization.
- Describe the role of primer-template modeling and predictive algorithms in enhancing molecular diagnostic performance.
- Recognize the applications of AI-enhanced LAMP diagnostics for the rapid detection of veterinary, wildlife, and zoonotic pathogens.
- Appreciate how predictive molecular diagnostics can strengthen disease surveillance, outbreak preparedness, and One Health initiatives.
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