Artificial intelligence in smartphone video analysis for equine asthma diagnostic support.
Authors: Gomes Carolina, Coheur Luísa, Tilley Paula
Journal: Equine veterinary journal
Summary
# Editorial Summary Equine asthma remains a significant welfare and performance concern, yet conventional diagnostic approaches—such as bronchoalveolar lavage and endoscopy—demand specialist facilities and invasive procedures that limit accessibility in field settings. Gomes, Coheur and Tilley developed an artificial intelligence system capable of analysing smartphone video footage to support asthma screening, potentially enabling rapid, non-invasive assessment by veterinarians and handlers in ambulatory contexts. The AI model was trained to detect respiratory signs characteristic of equine asthma from standard video recordings, offering a cost-effective alternative to traditional equipment-dependent diagnostics. Whilst this technology shows promise as a screening adjunct rather than a definitive diagnostic tool, it could substantially improve the early identification of at-risk horses and reduce unnecessary specialist referrals in practice. For practitioners seeking practical, accessible methods to flag respiratory compromise in working and competition horses, this represents a meaningful step towards democratising asthma detection across diverse equestrian settings.
Read the full abstract on PubMed
Practical Takeaways
- •A smartphone-based AI diagnostic tool could enable preliminary equine asthma screening at the barn or in field settings without specialized equipment
- •This technology may allow horse handlers and veterinarians to identify respiratory disease earlier, potentially improving outcomes and reducing costs of invasive diagnostics
- •Non-invasive screening could streamline case selection for horses requiring more advanced diagnostic workup such as endoscopy or bronchoalveolar lavage
Key Findings
- •Artificial intelligence applied to smartphone video analysis offers potential for non-invasive equine asthma screening
- •Proposed diagnostic approach aims to reduce reliance on invasive traditional methods and specialized equipment
- •Technology designed for accessibility in ambulatory veterinary and non-veterinary settings