Peer-reviewed veterinary case report
AI helps detect lung fluid in dogs using ultrasound
By Jourdan, Aurélie et al.·Published in Frontiers in veterinary science·2025·Centre Hospitalier Vé, France·View original on PubMed →
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Original publication title: Sonographic machine-assisted recognition and tracking of B-lines in dogs: the SMARTDOG study.
- Species:
- dog
Plain-English summary
A group of 40 dogs, including 20 with suspected heart failure and fluid in the lungs (cardiogenic pulmonary edema), underwent lung ultrasound to check for B-lines, which indicate fluid presence. The study tested an artificial intelligence (AI) tool against experienced veterinarians to see how well it could identify these B-lines. The AI performed well, matching the veterinarians' assessments most of the time, suggesting it could be a helpful tool for diagnosing this serious condition. This could lead to quicker and more accurate diagnoses for dogs with breathing problems related to heart issues.
People also search for: dog breathing problems heart failure · ultrasound for dog lung fluid · AI in veterinary medicine
Abstract
INTRODUCTION: Cardiogenic pulmonary edema (CPE) is a serious complication of heart failure in dogs, commonly characterized by excess fluid within the lung interstitium and alveoli. Point-of-care ultrasound (POCUS) allows for the prompt identification of pulmonary alterations through the presence of B-lines. However, interpretation remains subjective and operator dependent. Artificial intelligence (AI) may offer standardized, real-time analysis, but its application in veterinary medicine is largely unexplored. OBJECTIVE: To assess the performance of an AI-based ultrasound algorithm in detecting B-lines in dogs and to evaluate its agreement with manual quantification by experienced operators. METHODS: In this prospective study conducted at a single center, 40 dogs were enrolled: 20 with suspected CPE and 20 healthy controls. CPE suspicion was based on respiratory distress, a left atrium-to-aorta ratio (La:Ao) ≥1.6, >3 B-lines per field at thoracic POCUS, and clinical improvement following furosemide administration. Lung ultrasound was performed according to the Vet BLUE protocol. Cine loops were analyzed using the Butterfly Auto B-line Counter and reviewed independently by two POCUS-trained clinicians, each blinded to the AI results and to the other's evaluation. RESULTS: The AI algorithm failed to provide a B-line count in 14.2% of cineloops overall, with failures occurring in 11.8% of the suspected CPE group and 2.4% of the non-CPE group. Quantification failures were significantly more frequent in the suspected CPE group (OR 4.88;< 0.0001). Intraclass correlation coefficients showed excellent agreement for B-line counts (ICC = 0.88) and strong concordance for pathological classification (>3 B-lines; ICC = 0.85) between operators and AI. AI accuracy compared to clinicians was 84 and 86%. CONCLUSION: The AI algorithm demonstrated excellent agreement with experienced operators both for precise B-line counting and for the classification of pathological lung patterns. These findings support the potential of AI as a valuable decision-support tool for detecting clinically relevant cardiogenic pulmonary edema in veterinary critical care.
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Search related cases →Original publication on PubMed: https://pubmed.ncbi.nlm.nih.gov/40860925/