Artificial intelligence in smartphone video analysis for equine asthma diagnostic support.
Abstract: Equine asthma is a prevalent respiratory disease that negatively impacts horses' health and athletic performance. Traditional diagnostic methods are invasive and require specialised equipment. There is a need for a non-invasive, cost-effective screening tool that can be used by veterinarians and horse handlers in ambulatory settings. Objective: To assess the willingness of veterinarians and horse handlers to adopt such a tool (Questionnaire 1) and the challenges associated with visually recognising equine asthma (Questionnaire 2) and to develop EquiBreathe, an artificial intelligence (AI)-powered, non-invasive diagnostic tool designed to enhance equine asthma detection. Methods: Cross sectional survey and AI model development. Methods: Two Google Forms questionnaires were distributed. Video recordings of 23 horses (12 diagnosed with asthma and 11 healthy controls) were collected, focusing on nostril and abdominal movements. AI models were trained using feature engineering and image subtraction techniques. Results: Questionnaire 1 was completed by 18 veterinarians, 24 veterinary students and 121 horse handlers, while Questionnaire 2 involved 10 veterinarians, 23 students and 13 handlers. Respondents showed strong interest in the tool, emphasising its potential to improve communication and diagnostic precision (Questionnaire 1). However, relying solely on visual assessment for asthma detection proved difficult for veterinarians (Questionnaire 2), underscoring the value of AI support. The best-performing AI model achieved 89% accuracy in distinguishing asthmatic from healthy horses using nostril data. Conclusions: The study demonstrated the need for a field-friendly diagnostic solution. EquiBreathe was shown to have promising potential as a non-invasive, cost-effective screening tool.
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Publication Date: 2025-07-21 PubMed ID: 40686060DOI: 10.1111/evj.14559Google Scholar: Lookup
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- Journal Article
Summary
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The article presents a study on the development of an artificial intelligence-powered diagnostic tool for equine asthma called EquiBreathe. This tool uses smartphone video analysis to detect signs of asthma in horses, providing a non-invasive, cost-effective and user-friendly solution to traditional invasive methods.
Objectives of the Research
- The main aim was to design a diagnostic method that could be easily adopted by veterinarians and horse handlers for detecting asthma in horses.
- The researchers also aimed to explore the challenges usually faced while visually recognising equine asthma.
- They aimed to exploit artificial intelligence technology to provide a solution to these challenges.
Methodology
- Two surveys were conducted: one for assessing the willingness of veterinarians and horse handlers to adopt this new tool and the other to understand the difficulties they face while visually diagnosing asthma.
- They then developed an AI model, EquiBreathe. This AI technology was trained using feature engineering and image subtraction techniques on video recordings focusing on nostril and abdominal movements of horses diagnosed with asthma and those who were healthy.
Results
- The studies showed strong interest from respondents in adopting the tool. It was appreciated for its potential in enhancing communication and diagnostic accuracy.
- The results also suggested that visual diagnosis was challenging for veterinarians, reinforcing the need for the AI tool.
- The AI model developed — EquiBreathe — showed a high accuracy rate of 89% in distinguishing asthmatic horses from healthy ones using nostril data.
Conclusion
- The research demonstrated the urgent need for a more field-friendly diagnostic tool and the great potential of the developed tool, EquiBreathe in meeting this demand.
- The study showed that EquiBreathe can serve as an accurate, non-invasive, and cost-effective method for screening equine asthma.
Cite This Article
APA
Gomes C, Coheur L, Tilley P.
(2025).
Artificial intelligence in smartphone video analysis for equine asthma diagnostic support.
Equine Vet J.
https://doi.org/10.1111/evj.14559 Publication
Researcher Affiliations
- Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.
- INESC-ID, Lisbon, Portugal.
- AL4AnimalS, CIISA, Lisbon, Portugal.
- Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.
- INESC-ID, Lisbon, Portugal.
- AL4AnimalS, CIISA, Lisbon, Portugal.
- Faculdade Medicina Veterinária, Universidade de Lisboa, Lisbon, Portugal.
Grant Funding
- UIDB/00276/2020 / Foundation for Science and Technology
- LA/P/0059/2020 / Foundation for Science and Technology
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