{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T21:09:15Z","timestamp":1790888955005,"version":"4.1.0"},"reference-count":27,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T00:00:00Z","timestamp":1754611200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Vet. Sci."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Objective<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>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) \u22651.6, &amp;gt;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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>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; <jats:italic>p<\/jats:italic> &amp;lt; 0.0001). Intraclass correlation coefficients showed excellent agreement for B-line counts (ICC = 0.88) and strong concordance for pathological classification (&amp;gt;3 B-lines; ICC = 0.85) between operators and AI. AI accuracy compared to clinicians was 84 and 86%.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fvets.2025.1647547","type":"journal-article","created":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T07:53:31Z","timestamp":1754639611000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Sonographic machine-assisted recognition and tracking of B-lines in dogs: the SMARTDOG study"],"prefix":"10.3389","volume":"12","author":[{"given":"Aur\u00e9lie","family":"Jourdan","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caroline","family":"Dania","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maxime","family":"Cambournac","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2025,8,8]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"1058","DOI":"10.2460\/javma.235.9.1058","article-title":"Radiographic features of cardiogenic pulmonary edema in dogs with mitral regurgitation: 61 cases (1998\u20132007)","volume":"235","author":"Diana","year":"2009","journal-title":"J Am Vet Med Assoc"},{"key":"B2","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1111\/jsap.13851","article-title":"Radiographic features of cardiogenic pulmonary oedema in dogs with dilated cardiomyopathy","volume":"66","author":"Lee","year":"2025","journal-title":"J Small Anim Pract."},{"key":"B3","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1111\/vec.12750","article-title":"Distribution of alveolar-interstitial syndrome in dogs and cats with respiratory distress as assessed by lung ultrasound versus thoracic radiographs","volume":"28","author":"Ward","year":"2018","journal-title":"J Vet Emerg Crit Care."},{"key":"B4","doi-asserted-by":"publisher","first-page":"1125","DOI":"10.1016\/j.cvsm.2021.07.012","article-title":"Lung ultrasound fundamentals, \u201cwet versus dry\u201d lung, signs of consolidation in dogs and cats","volume":"51","author":"Lisciandro","year":"2021","journal-title":"Vet Clin North Am Small Anim Pract."},{"key":"B5","doi-asserted-by":"publisher","first-page":"700","DOI":"10.1111\/jvim.14692","article-title":"Assessment of lung ultrasound B lines in dogs with different stages of chronic valvular heart disease","volume":"31","author":"Vezzosi","year":"2017","journal-title":"J Vet Intern Med."},{"key":"B6","doi-asserted-by":"publisher","first-page":"666","DOI":"10.2460\/javma.250.6.666","article-title":"Accuracy of point-of-care lung ultrasonography for the diagnosis of cardiogenic pulmonary edema in dogs and cats with acute dyspnea","volume":"250","author":"Ward","year":"2017","journal-title":"J Am Vet Med Assoc."},{"key":"B7","doi-asserted-by":"publisher","first-page":"447","DOI":"10.1111\/vru.12151","article-title":"Transthoracic lung ultrasound in normal dogs and dogs with cardiogenic pulmonary edema: a pilot study","volume":"55","author":"Rademacher","year":"2014","journal-title":"Vet Radiol Ultrasound."},{"key":"B8","doi-asserted-by":"publisher","first-page":"106","DOI":"10.3390\/ani15010106","article-title":"Point of care lung ultrasound in small animal emergency and critical care medicine: a clinical review","volume":"15","author":"Armenise","year":"2025","journal-title":"Animals."},{"key":"B9","doi-asserted-by":"publisher","first-page":"178","DOI":"10.1111\/jsap.13271","article-title":"Diagnostic accuracy of a lung ultrasound protocol (Vet BLUE) for detection of pleural fluid, pneumothorax and lung pathology in dogs and cats","volume":"62","author":"Cole","year":"2021","journal-title":"J Small Anim Pract."},{"key":"B10","first-page":"47","volume-title":"The Essentials of Veterinary Point of Care Ultrasound: Pleural Space and Lung.","author":"Boysen","year":"2022"},{"key":"B11","doi-asserted-by":"publisher","first-page":"1787","DOI":"10.1111\/jvim.15257","article-title":"Inter-rater agreement and reliability of thoracic ultrasonographic findings in feedlot calves, with or without naturally occurring bronchopneumonia","volume":"32","author":"Buczinski","year":"2018","journal-title":"J Vet Intern Med."},{"key":"B12","doi-asserted-by":"publisher","first-page":"336","DOI":"10.1111\/jvim.16956","article-title":"Lung ultrasound score in dogs and cats: A reliability study","volume":"38","author":"Oricco","year":"2024","journal-title":"J Vet Intern Med."},{"key":"B13","doi-asserted-by":"publisher","first-page":"257","DOI":"10.2139\/ssrn.4304741","article-title":"Clinical benefit of AI-assisted lung ultrasound in a resource-limited intensive care unit","volume":"27","author":"Phung Tran","year":"2023","journal-title":"Crit Care"},{"key":"B14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.5114\/ait.2024.136784","article-title":"Overview of artificial intelligence in point-of-care ultrasound. New horizons for respiratory system diagnoses","volume":"56","author":"Mika","year":"2024","journal-title":"Anaesthesiol Intensive Ther."},{"key":"B15","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1186\/s13089-021-00234-6","article-title":"B-line quantification: comparing learners novice to lung ultrasound assisted by machine artificial intelligence technology to expert review","volume":"13","author":"Russell","year":"2021","journal-title":"Ultrasound J."},{"key":"B16","doi-asserted-by":"publisher","first-page":"45","DOI":"10.11152\/mu-1885","article-title":"Visual versus automatic ultrasound scoring of lung B-lines: reliability and consistency between systems","volume":"21","author":"Short","year":"2019","journal-title":"Med Ultrason."},{"key":"B17","doi-asserted-by":"publisher","first-page":"1259","DOI":"10.1007\/s10877-019-00440-7","article-title":"Reliability of B-line quantification by different-level observers and a software algorithm using point-of-care lung ultrasound","volume":"34","author":"Pi\u010duljan","year":"2020","journal-title":"J Clin Monit Comput."},{"key":"B18","doi-asserted-by":"publisher","first-page":"109","DOI":"10.1016\/j.ajem.2023.05.029","article-title":"Comparison of artificial intelligence versus real-time physician assessment of pulmonary edema with lung ultrasound","volume":"70","author":"Gottlieb","year":"2023","journal-title":"Am J Emerg Med."},{"key":"B19","doi-asserted-by":"publisher","first-page":"1395","DOI":"10.1016\/j.jcrc.2015.08.021","article-title":"Interobserver agreement in the evaluation of B-lines using bedside ultrasound","volume":"30","author":"Gullett","year":"2015","journal-title":"J Crit Care"},{"key":"B20","doi-asserted-by":"publisher","first-page":"368","DOI":"10.4103\/ija.ija_1008_21","article-title":"Automated versus manual B-lines counting, left ventricular outflow tract velocity time integral and inferior vena cava collapsibility index in COVID-19 patients","volume":"66","author":"Damodaran","year":"2022","journal-title":"Indian J Anaesth."},{"key":"B21","doi-asserted-by":"publisher","first-page":"29","DOI":"10.1016\/j.jvc.2019.11.001","article-title":"Two-dimensional echocardiographic left-atrial-to-aortic ratio in healthy adult dogs: a reexamination of reference intervals","volume":"26","author":"Rishniw","year":"2019","journal-title":"J Vet Cardiol."},{"key":"B22","doi-asserted-by":"publisher","first-page":"1038","DOI":"10.2460\/javma.253.8.1038","article-title":"Diagnostic value of vertebral left atrial size as determined from thoracic radiographs for assessment of left atrial size in dogs with myxomatous mitral valve disease","volume":"253","author":"Malcolm","year":"2018","journal-title":"J Am Vet Med Assoc."},{"key":"B23","author":"Lisciandro","year":"2017","journal-title":"Vet BLUE"},{"key":"B24","volume-title":"Ultrasound-on-Chip\u2122 White Paper: CMUT Technology and System Design.","year":"2023"},{"key":"B25","volume-title":"Clinical Imaging Presets User Guide","year":"2024"},{"key":"B26","doi-asserted-by":"publisher","first-page":"1321","DOI":"10.3390\/app13031321","article-title":"Inter-rater variability in the evaluation of lung ultrasound in videos acquired from COVID-19 patients","volume":"13","author":"Herraiz","year":"2023","journal-title":"Appl Sci"},{"key":"B27","unstructured":"Lung Auto B-Line Counter\n          \n          \n          \n          2025"}],"container-title":["Frontiers in Veterinary Science"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fvets.2025.1647547\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T07:53:34Z","timestamp":1754639614000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fvets.2025.1647547\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,8]]},"references-count":27,"alternative-id":["10.3389\/fvets.2025.1647547"],"URL":"https:\/\/doi.org\/10.3389\/fvets.2025.1647547","relation":{},"ISSN":["2297-1769"],"issn-type":[{"value":"2297-1769","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,8,8]]},"article-number":"1647547"}}