{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T06:31:14Z","timestamp":1786516274350,"version":"build-2736575974"},"reference-count":63,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T00:00:00Z","timestamp":1786492800000},"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. Digit. Health"],"abstract":"<jats:sec>\n                    <jats:title>Context and objectives<\/jats:title>\n                    <jats:p>Lung ultrasound (LUS) is a safe and cost-effective diagnostic tool. B-lines are fundamental ultrasound (US) artifacts for LUS-based diagnosis of various pulmonary conditions, especially for the evaluation of the lung parenchyma. However, the challenges in their identification and interpretation, combined with a shortage of experts and training programs, restrict the use of this tool in routine clinical practice.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>To overcome these limitations, this study presents a computer vision (CV)-based system for automatic B-line detection. The proposed system integrates preprocessing and feature engineering stages with an object detection module. First, LUS images are normalized to ensure interoperability across different US devices and settings. Subsequently, the Radon and inverse Radon transforms are applied to generate a mask that highlights hyperechoic vertical structures, which is then fused with the preprocessed LUS image. Finally, the resulting image serves as input for a convolutional neural network (CNN) based on the You Only Look Once (YOLO) architecture, enabling the automatic localization of B-lines.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The results obtained on the test set demonstrate satisfactory performance, achieving a precision of 89.13%, a recall of 80.39%, and an average precision (AP) of 0.82 at an Intersection over Union (IoU) of 0.5.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>A clinical decision support tool is proposed, aimed at improving efficiency and consistency in LUS interpretation, as well as facilitating its integration into clinical practice. Although the system is still in its conceptual stage, these findings lay the groundwork for future clinical validation processes directed toward its future implementation.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fdgth.2026.1917310","type":"journal-article","created":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T05:57:16Z","timestamp":1786514236000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Computer vision-based support system for B-line detection in lung ultrasound"],"prefix":"10.3389","volume":"8","author":[{"given":"Julia","family":"L\u00f3pez-Canay","sequence":"first","affiliation":[{"name":"NeumoVigo i+I Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO","place":["Vigo, Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Manuel","family":"Casal-Guisande","sequence":"additional","affiliation":[{"name":"NeumoVigo i+I Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO","place":["Vigo, Spain"]},{"name":"Department of Design in Engineering, University of Vigo","place":["Vigo, Spain"]},{"name":"Centro de Investigaci\u00f3n Biom\u00e9dica en Red, CIBERES ISCIII","place":["Madrid, Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cristina","family":"Ramos-Hern\u00e1ndez","sequence":"additional","affiliation":[{"name":"NeumoVigo i+I Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO","place":["Vigo, Spain"]},{"name":"Pulmonary Department, Hospital \u00c1lvaro Cunqueiro","place":["Vigo, Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maribel","family":"Botana-Rial","sequence":"additional","affiliation":[{"name":"NeumoVigo i+I Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO","place":["Vigo, Spain"]},{"name":"Centro de Investigaci\u00f3n Biom\u00e9dica en Red, CIBERES ISCIII","place":["Madrid, Spain"]},{"name":"Pulmonary Department, Hospital \u00c1lvaro Cunqueiro","place":["Vigo, Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alberto","family":"Fern\u00e1ndez-Villar","sequence":"additional","affiliation":[{"name":"NeumoVigo i+I Research Group, Galicia Sur Health Research Institute (IIS Galicia Sur), SERGAS-UVIGO","place":["Vigo, Spain"]},{"name":"Centro de Investigaci\u00f3n Biom\u00e9dica en Red, CIBERES ISCIII","place":["Madrid, Spain"]},{"name":"Pulmonary Department, Hospital \u00c1lvaro Cunqueiro","place":["Vigo, Spain"]},{"name":"Department of Functional Biology and Health Sciences, University of Vigo","place":["Vigo, Spain"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,12]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"2001519","DOI":"10.1183\/13993003.01519-2020","article-title":"European Respiratory Society statement on thoracic ultrasound","volume":"57","author":"Laursen","year":"2021","journal-title":"Eur Respir J"},{"key":"B2","doi-asserted-by":"publisher","first-page":"e200564","DOI":"10.1148\/ryct.2021200564","article-title":"Lung ultrasound: the essentials","volume":"3","author":"Marini","year":"2021","journal-title":"Radiol Cardiothorac Imaging"},{"key":"B3","doi-asserted-by":"publisher","first-page":"462","DOI":"10.3390\/app10020462","article-title":"Lung ultrasound imaging, a technical review","volume":"10","author":"Demi","year":"2020","journal-title":"Appl Sci"},{"key":"B4","doi-asserted-by":"publisher","first-page":"1179","DOI":"10.1148\/rg.294085199","article-title":"US artifacts","volume":"29","author":"Feldman","year":"2009","journal-title":"Radiographics"},{"key":"B5","doi-asserted-by":"publisher","first-page":"39","DOI":"10.1093\/bjaceaccp\/mkv012","article-title":"Practical approach to lung ultrasound","volume":"16","author":"Miller","year":"2016","journal-title":"BJA Educ"},{"key":"B6","author":"Ramos Hern\u00e1ndez","year":"2021"},{"key":"B7","doi-asserted-by":"publisher","first-page":"102776","DOI":"10.1016\/j.aprim.2023.102776","article-title":"Uso de la ecograf\u00eda cl\u00ednica en atenci\u00f3n Primaria. Estudio prospectivo multic\u00e9ntrico","volume":"56","author":"Ju\u00e1rez Castillo","year":"2024","journal-title":"Aten Primaria"},{"key":"B8","doi-asserted-by":"publisher","first-page":"38","DOI":"10.1186\/s13089-021-00240-8","article-title":"Results from a Spanish national survey on the application of ultrasound in pulmonology services","volume":"13","author":"Ramos-Hern\u00e1ndez","year":"2021","journal-title":"Ultrasound J"},{"key":"B9","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1016\/j.anpedi.2024.02.012","article-title":"Ecograf\u00eda cl\u00ednica en atenci\u00f3n Primaria. Y si hablamos de ecograf\u00eda pulmonar\u00bf porqu\u00e9 seguimos haciendo radiograf\u00edas?","volume":"100","author":"Viver G\u00f3mez","year":"2024","journal-title":"An Pediatr (Engl Ed)"},{"key":"B10","doi-asserted-by":"publisher","first-page":"1356","DOI":"10.21037\/jtd.2016.04.55","article-title":"Lung B-line artefacts and their use","volume":"8","author":"Dietrich","year":"2016","journal-title":"J Thorac Dis"},{"key":"B11","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":"B12","doi-asserted-by":"publisher","first-page":"318","DOI":"10.3390\/diagnostics15030318","article-title":"Predicting COPD readmission: an intelligent clinical decision support system","volume":"15","author":"L\u00f3pez-Canay","year":"2025","journal-title":"Diagnostics"},{"key":"B13","doi-asserted-by":"publisher","first-page":"213","DOI":"10.3390\/diagnostics16020213","article-title":"Development of an intelligent clinical decision support system for spirometry quality control","volume":"16","author":"L\u00f3pez-Canay","year":"2026","journal-title":"Diagnostics"},{"key":"B14","doi-asserted-by":"publisher","first-page":"128","DOI":"10.3390\/asi8050128","article-title":"Image-based formalization of tabular data for threshold-based prediction of hospital stay using convolutional neural networks: an intelligent decision support system applied in COPD","volume":"8","author":"Pinheira","year":"2025","journal-title":"Appl Syst Innov"},{"key":"B15","doi-asserted-by":"publisher","first-page":"264","DOI":"10.1016\/j.arbres.2024.10.010","article-title":"Clinical and social characterization of patients hospitalized for COPD exacerbation using machine learning tools","volume":"61","author":"Casal-Guisande","year":"2025","journal-title":"Arch Bronconeumol"},{"key":"B16","doi-asserted-by":"publisher","first-page":"854","DOI":"10.3390\/biomedicines12040854","article-title":"Proposal and definition of an intelligent clinical decision support system applied to the prediction of dyspnea after 12 months of an acute episode of COVID-19","volume":"12","author":"Casal-Guisande","year":"2024","journal-title":"Biomedicines"},{"key":"B17","volume-title":"Redes Neuronales & Deep Learning","author":"Berzal","year":"2018"},{"key":"B18","first-page":"775","volume-title":"Deep Learning","author":"Goodfellow","year":"2016"},{"key":"B19","doi-asserted-by":"publisher","first-page":"107","DOI":"10.1007\/s12559-023-10189-6","article-title":"A Yolo-based model for breast cancer detection in mammograms","volume":"16","author":"Prinzi","year":"2024","journal-title":"Cognit Comput"},{"key":"B20","doi-asserted-by":"publisher","first-page":"792","DOI":"10.1016\/j.ipha.2024.05.007","article-title":"Computer vision to enhance healthcare domain: an overview of features, implementation, and opportunities","volume":"2","author":"Javaid","year":"2024","journal-title":"Intell Pharm"},{"key":"B21","doi-asserted-by":"publisher","first-page":"1286","DOI":"10.3390\/bioengineering12121286","article-title":"Artificial intelligence for spirometry quality evaluation: a systematic review","volume":"12","author":"L\u00f3pez-Canay","year":"2025","journal-title":"Bioengineering"},{"key":"B22","first-page":"1899103","article-title":"Artificial intelligence for lung ultrasound interpretation: a systematic review","volume-title":"Front Radiol","author":"L\u00f3pez-Canay","year":"2026"},{"key":"B23","doi-asserted-by":"publisher","first-page":"107251","DOI":"10.5518\/1485","article-title":"Deep learning for real-time multi-class segmentation of artefacts in lung ultrasound","volume":"140","author":"Howell","year":"2024","journal-title":"Ultrasonics"},{"key":"B24","doi-asserted-by":"publisher","first-page":"571","DOI":"10.1109\/TMI.2021.3117246","article-title":"Integrating domain knowledge into deep networks for lung ultrasound with applications to COVID-19","volume":"41","author":"Frank","year":"2022","journal-title":"IEEE Trans Med Imaging"},{"key":"B25","doi-asserted-by":"publisher","first-page":"7176","DOI":"10.3390\/app15137176","article-title":"Clinical characterization of patients with syncope of unclear cause using unsupervised machine-learning tools: a pilot study","volume":"15","author":"Mu\u00f1oz-Mart\u00ednez","year":"2025","journal-title":"Appl Sci"},{"key":"B26","doi-asserted-by":"publisher","first-page":"e34700","DOI":"10.1016\/j.heliyon.2024.e34700","article-title":"Automatic detection of pleural line and lung sliding in lung ultrasonography using convolutional neural networks","volume":"10","author":"Uchida","year":"2024","journal-title":"Heliyon"},{"key":"B27","doi-asserted-by":"publisher","first-page":"311","DOI":"10.3390\/bioengineering12030311","article-title":"Improved A-line and B-line detection in lung ultrasound using deep learning with boundary-aware dice loss","volume":"12","author":"Abbasi","year":"2025","journal-title":"Bioengineering"},{"key":"B28","doi-asserted-by":"publisher","first-page":"4352","DOI":"10.1109\/JBHI.2023.3282596","article-title":"Deep learning for detection and localization of B-lines in lung ultrasound","volume":"27","author":"Lucassen","year":"2023","journal-title":"IEEE J Biomed Health Inform"},{"key":"B29","doi-asserted-by":"publisher","first-page":"6976","DOI":"10.3390\/app11156976","article-title":"Detecting the absence of lung sliding in lung ultrasounds using deep learning","volume":"11","author":"Ja\u0161\u010dur","year":"2021","journal-title":"Appl Sci"},{"key":"B30","doi-asserted-by":"publisher","first-page":"274","DOI":"10.1186\/s12911-023-02362-6","article-title":"Automatic deep learning-based pleural effusion segmentation in lung ultrasound images","volume":"23","author":"Vukovic","year":"2023","journal-title":"BMC Med Inform Decis Mak"},{"key":"B31","doi-asserted-by":"publisher","first-page":"095008","DOI":"10.1088\/1361-6560\/ad3888","article-title":"Combining quantitative and qualitative analysis for scoring pleural line in lung ultrasound","volume":"69","author":"Xing","year":"2024","journal-title":"Phys Med Biol"},{"key":"B32","doi-asserted-by":"publisher","first-page":"410","DOI":"10.1186\/s12882-022-03044-7","article-title":"Automated lung ultrasound image assessment using artificial intelligence to identify fluid overload in dialysis patients","volume":"23","author":"Tan","year":"2022","journal-title":"BMC Nephrol"},{"key":"B33","doi-asserted-by":"publisher","first-page":"957","DOI":"10.1109\/JBHI.2019.2936151","article-title":"Localizing B-lines in lung ultrasonography by weakly supervised deep learning, in-vivo results","volume":"24","author":"Van Sloun","year":"2020","journal-title":"IEEE J Biomed Health Inform"},{"key":"B34","doi-asserted-by":"publisher","first-page":"142","DOI":"10.1016\/j.ajem.2025.01.041","article-title":"Diagnostic accuracy of an automated classifier for the detection of pleural effusions in patients undergoing lung ultrasound","volume":"90","author":"Chaudhary","year":"2025","journal-title":"Am J Emerg Med"},{"key":"B35","doi-asserted-by":"publisher","first-page":"105953","DOI":"10.1016\/j.compbiomed.2022.105953","article-title":"Accurate assessment of the lung sliding artefact on lung ultrasonography using a deep learning approach","volume":"148","author":"VanBerlo","year":"2022","journal-title":"Comput Biol Med"},{"key":"B36","doi-asserted-by":"publisher","first-page":"47","DOI":"10.12700\/APH.20.6.2023.6.3","article-title":"Detecting the absence of lung sliding in ultrasound videos using 3D convolutional neural networks","volume":"20","author":"Kol\u00e1rik","year":"2023","journal-title":"Acta Polytechnica Hungarica"},{"key":"B37","doi-asserted-by":"publisher","first-page":"102454","DOI":"10.1016\/j.compmedimag.2024.102454","article-title":"Active learning based on multi-enhanced views for classification of multiple patterns in lung ultrasound images","volume":"118","author":"Ni","year":"2024","journal-title":"Comput Med Imaging Graph"},{"key":"B38","doi-asserted-by":"publisher","first-page":"104228","DOI":"10.1016\/j.bspc.2022.104228","article-title":"Intelligent interpretation of four lung ultrasonographic features with split attention based deep learning model","volume":"81","author":"Chen","year":"2023","journal-title":"Biomed Signal Process Control"},{"key":"B39","doi-asserted-by":"publisher","first-page":"2049","DOI":"10.3390\/diagnostics11112049","article-title":"Automation of lung ultrasound interpretation via deep learning for the classification of normal versus abnormal lung parenchyma: a multicenter study","volume":"11","author":"Arntfield","year":"2021","journal-title":"Diagnostics"},{"key":"B40","doi-asserted-by":"publisher","first-page":"945","DOI":"10.1016\/j.ultrasmedbio.2022.01.023","article-title":"Evaluation of pulmonary edema using ultrasound imaging in patients with COVID-19 pneumonia based on a non-local channel attention ResNet","volume":"48","author":"Huang","year":"2022","journal-title":"Ultrasound Med Biol"},{"key":"B41","doi-asserted-by":"publisher","first-page":"825","DOI":"10.1016\/j.ultrasmedbio.2024.02.004","article-title":"Transfer learning-based B-line assessment of lung ultrasound for acute heart failure","volume":"50","author":"Pare","year":"2024","journal-title":"Ultrasound Med Biol"},{"key":"B42","doi-asserted-by":"publisher","first-page":"100687","DOI":"10.1016\/j.imu.2021.100687","article-title":"Automated detection of pneumonia in lung ultrasound using deep video classification for COVID-19","volume":"25","author":"Erfanian Ebadi","year":"2021","journal-title":"Inform Med Unlocked"},{"key":"B43","doi-asserted-by":"publisher","first-page":"330","DOI":"10.1002\/mp.15908","article-title":"Automatic detection of A-line in lung ultrasound images using deep learning and image processing","volume":"50","author":"Xing","year":"2023","journal-title":"Med Phys"},{"key":"B44","first-page":"580","article-title":"Rich feature hierarchies for accurate object detection and semantic segmentation","author":"Girshick","year":"2014"},{"key":"B45","doi-asserted-by":"publisher","first-page":"985","DOI":"10.1007\/s40477-025-01077-w","article-title":"Automatic approach for B-lines detection in lung ultrasound images using you only look once algorithm","volume":"28","author":"Bottino","year":"2025","journal-title":"J Ultrasound"},{"key":"B46","doi-asserted-by":"publisher","first-page":"106345","DOI":"10.1016\/j.compbiomed.2022.106345","article-title":"Unsupervised landmark detection and classification of lung infection using transporter neural networks","volume":"152","author":"Tripathi","year":"2023","journal-title":"Comput Biol Med"},{"key":"B47","first-page":"779","article-title":"You only Look once: unified, real-time object detection","author":"Redmon","year":"2016"},{"key":"B48","doi-asserted-by":"publisher","first-page":"100094","DOI":"10.1016\/j.bea.2023.100094","article-title":"lungEcho - resource constrained lung ultrasound video analysis tool for faster triaging and active learning","volume":"6","author":"Joseph","year":"2023","journal-title":"Biomed Eng Adv"},{"key":"B49","author":"Barroso-Laguna","year":"2019"},{"key":"B50","doi-asserted-by":"publisher","first-page":"1647","DOI":"10.1038\/s41597-025-05854-4","article-title":"Lung ultrasound imaging dataset for accurate detection and localization of LUS vertical artifact","volume":"12","author":"Okila","year":"2025","journal-title":"Sci Data"},{"key":"B51","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4757-1463-0","volume-title":"The Radon Transform","author":"Helgason","year":"1999"},{"key":"B52","year":""},{"key":"B53","year":""},{"key":"B54","year":""},{"key":"B55","year":""},{"key":"B56","year":""},{"key":"B57","author":"Ge","year":"2021"},{"key":"B58","author":"Lin","year":"2015"},{"key":"B59","author":"Wang","year":"2019"},{"key":"B60","author":"Petsiuk","year":"2021"},{"key":"B61","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1007\/s00134-012-2513-4","article-title":"International evidence-based recommendations for point-of-care lung ultrasound","volume":"38","author":"Volpicelli","year":"2012","journal-title":"Intensive Care Med"},{"key":"B62","doi-asserted-by":"publisher","first-page":"1501","DOI":"10.1016\/j.jcms.2025.06.008","article-title":"Deep learning for orbital fracture detection and reconstruction: a systematic review on diagnostic accuracy and surgical planning","volume":"53","author":"Ni\u00f1o-Sandoval","year":"2025","journal-title":"J Craniomaxillofac Surg"},{"key":"B63","first-page":"618","article-title":"Grad-CAM: visual explanations from deep networks via gradient-based localization","author":"Selvaraju","year":"2017"}],"container-title":["Frontiers in Digital Health"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2026.1917310\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,8,12]],"date-time":"2026-08-12T05:57:18Z","timestamp":1786514238000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fdgth.2026.1917310\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8,12]]},"references-count":63,"alternative-id":["10.3389\/fdgth.2026.1917310"],"URL":"https:\/\/doi.org\/10.3389\/fdgth.2026.1917310","relation":{},"ISSN":["2673-253X"],"issn-type":[{"value":"2673-253X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,8,12]]},"article-number":"1917310"}}