Related Experiment Video
Updated: Mar 27, 2026

04:17
DNA Virus Detection System Based on RPA-CRISPR/Cas12a-SPM and Deep Learning
Published on: May 10, 2024
1.7K
Real-Time Detection of a Virus Using Detection Dogs
T Craig Angle1, Thomas Passler2, Paul L Waggoner1
1Canine Performance Sciences Program, Auburn University , Auburn, AL , USA.
Frontiers in Veterinary Science
|January 19, 2016
Summary
Trained dogs can detect viral infections in cell cultures by identifying unique volatile organic compounds (VOCs). This study shows dogs can differentiate between bovine viral diarrhea virus (BVDV) and other viruses, offering a novel real-time pathogen detection method.
Area of Science:
- Veterinary Virology
- Animal Behavior
- Biosensing Technology
Background:
- Real-time viral infection detection methods are limited, especially in challenging environments.
- Volatile organic compounds (VOCs) are released by tissues and change during disease states.
- Pathogen-specific VOC patterns may enable odor-based disease detection.
Purpose of the Study:
- To investigate trained dogs' ability to detect and discriminate cell cultures infected with bovine viral diarrhea virus (BVDV).
- To assess dogs' capability in distinguishing BVDV-infected cultures from uninfected ones and those infected with other bovine viruses (BHV-1, BPIV-3).
Main Methods:
- Two trained dogs were used to detect BVDV-infected cell cultures.
- Dogs were trained to identify BVDV in various cell culture media.
- Detection trials involved a scent wheel with one target and seven distractors, with handlers unaware of target locations.
Main Results:
- Dog 1 achieved a diagnostic sensitivity of 0.850 for BVDV detection; Dog 2 achieved 0.967.
- Both dogs demonstrated high diagnostic specificity: 0.981 for Dog 1 and 0.993 for Dog 2.
- Dogs successfully differentiated between cell cultures infected with BVDV, BHV-1, and BPIV-3.
Conclusions:
- Trained dogs can effectively discriminate between cell cultures infected with different bovine viruses.
- This study validates dogs as a realistic, real-time mobile pathogen sensing technology for viral detection.
- Discrimination is likely based on unique VOC patterns emitted by infected and uninfected cells.

