Sonographic machine-assisted recognition and tracking of B-lines in dogs: the SMARTDOG study
IntroductionCardiogenic 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-...
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Frontiers Media S.A.
2025-08-01
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| Series: | Frontiers in Veterinary Science |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fvets.2025.1647547/full |
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| author | Aurélie Jourdan Aurélie Jourdan Caroline Dania Caroline Dania Maxime Cambournac Maxime Cambournac |
| author_facet | Aurélie Jourdan Aurélie Jourdan Caroline Dania Caroline Dania Maxime Cambournac Maxime Cambournac |
| author_sort | Aurélie Jourdan |
| collection | DOAJ |
| description | IntroductionCardiogenic 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.ObjectiveTo 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.MethodsIn 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.ResultsThe 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; p < 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%.ConclusionThe 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. |
| format | Article |
| id | doaj-art-919dd2aaa45344fba6d5919b31bacdfe |
| institution | DOAJ |
| issn | 2297-1769 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Veterinary Science |
| spelling | doaj-art-919dd2aaa45344fba6d5919b31bacdfe2025-08-20T02:55:07ZengFrontiers Media S.A.Frontiers in Veterinary Science2297-17692025-08-011210.3389/fvets.2025.16475471647547Sonographic machine-assisted recognition and tracking of B-lines in dogs: the SMARTDOG studyAurélie Jourdan0Aurélie Jourdan1Caroline Dania2Caroline Dania3Maxime Cambournac4Maxime Cambournac5Centre Hospitalier Vétérinaire Frégis, Paris, FranceIVC Evidensia France, Courbevoie, FranceCentre Hospitalier Vétérinaire Frégis, Paris, FranceIVC Evidensia France, Courbevoie, FranceCentre Hospitalier Vétérinaire Frégis, Paris, FranceIVC Evidensia France, Courbevoie, FranceIntroductionCardiogenic 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.ObjectiveTo 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.MethodsIn 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.ResultsThe 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; p < 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%.ConclusionThe 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.https://www.frontiersin.org/articles/10.3389/fvets.2025.1647547/fullartificial intelligencelung ultrasoundB-lines detectioncanine pulmonary edemapoint of care ultrasound (POCUS) |
| spellingShingle | Aurélie Jourdan Aurélie Jourdan Caroline Dania Caroline Dania Maxime Cambournac Maxime Cambournac Sonographic machine-assisted recognition and tracking of B-lines in dogs: the SMARTDOG study Frontiers in Veterinary Science artificial intelligence lung ultrasound B-lines detection canine pulmonary edema point of care ultrasound (POCUS) |
| title | Sonographic machine-assisted recognition and tracking of B-lines in dogs: the SMARTDOG study |
| title_full | Sonographic machine-assisted recognition and tracking of B-lines in dogs: the SMARTDOG study |
| title_fullStr | Sonographic machine-assisted recognition and tracking of B-lines in dogs: the SMARTDOG study |
| title_full_unstemmed | Sonographic machine-assisted recognition and tracking of B-lines in dogs: the SMARTDOG study |
| title_short | Sonographic machine-assisted recognition and tracking of B-lines in dogs: the SMARTDOG study |
| title_sort | sonographic machine assisted recognition and tracking of b lines in dogs the smartdog study |
| topic | artificial intelligence lung ultrasound B-lines detection canine pulmonary edema point of care ultrasound (POCUS) |
| url | https://www.frontiersin.org/articles/10.3389/fvets.2025.1647547/full |
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