{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T14:18:24Z","timestamp":1779200304454,"version":"3.51.4"},"reference-count":58,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2025,1,5]],"date-time":"2025-01-05T00:00:00Z","timestamp":1736035200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The Liaison Committee for Education, Research, and Innovation in Central Norway (Samarbeidsorganet)","award":["46055500"],"award-info":[{"award-number":["46055500"]}]},{"name":"The Liaison Committee for Education, Research, and Innovation in Central Norway (Samarbeidsorganet)","award":["RO-NO2019-0138"],"award-info":[{"award-number":["RO-NO2019-0138"]}]},{"name":"The Liaison Committee for Education, Research, and Innovation in Central Norway (Samarbeidsorganet)","award":["19\/2020"],"award-info":[{"award-number":["19\/2020"]}]},{"name":"The Ministry of Health and Care Services of Norway through the Norwegian National Research Center for Minimally Invasive and Image-Guided Diagnostics and Therapy (MiDT) at St. Olavs hospital, Trondheim, Norway","award":["46055500"],"award-info":[{"award-number":["46055500"]}]},{"name":"The Ministry of Health and Care Services of Norway through the Norwegian National Research Center for Minimally Invasive and Image-Guided Diagnostics and Therapy (MiDT) at St. Olavs hospital, Trondheim, Norway","award":["RO-NO2019-0138"],"award-info":[{"award-number":["RO-NO2019-0138"]}]},{"name":"The Ministry of Health and Care Services of Norway through the Norwegian National Research Center for Minimally Invasive and Image-Guided Diagnostics and Therapy (MiDT) at St. Olavs hospital, Trondheim, Norway","award":["19\/2020"],"award-info":[{"award-number":["19\/2020"]}]},{"name":"The Norwegian Financial Mechanism","award":["46055500"],"award-info":[{"award-number":["46055500"]}]},{"name":"The Norwegian Financial Mechanism","award":["RO-NO2019-0138"],"award-info":[{"award-number":["RO-NO2019-0138"]}]},{"name":"The Norwegian Financial Mechanism","award":["19\/2020"],"award-info":[{"award-number":["19\/2020"]}]},{"name":"19\/2020 \u201cImproving Cancer Diagnostics in Flexible Endoscopy using Artificial Intelligence and Medical Robotics\u201d IDEAR","award":["46055500"],"award-info":[{"award-number":["46055500"]}]},{"name":"19\/2020 \u201cImproving Cancer Diagnostics in Flexible Endoscopy using Artificial Intelligence and Medical Robotics\u201d IDEAR","award":["RO-NO2019-0138"],"award-info":[{"award-number":["RO-NO2019-0138"]}]},{"name":"19\/2020 \u201cImproving Cancer Diagnostics in Flexible Endoscopy using Artificial Intelligence and Medical Robotics\u201d IDEAR","award":["19\/2020"],"award-info":[{"award-number":["19\/2020"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>Endobronchial ultrasound-guided transbronchial needle aspiration (EBUS-TBNA) is a cornerstone in minimally invasive thoracic lymph node sampling. In lung cancer staging, precise assessment of lymph node position is crucial for clinical decision-making. This study aimed to demonstrate a new deep learning method to classify thoracic lymph nodes based on their anatomical location using EBUS images. Bronchoscopists labeled lymph node stations in real-time according to the Mountain Dressler nomenclature. EBUS images were then used to train and test a deep neural network (DNN) model, with intraoperative labels as ground truth. In total, 28,134 EBUS images were acquired from 56 patients. The model achieved an overall classification accuracy of 59.5 \u00b1 5.2%. The highest precision, sensitivity, and F1 score were observed in station 4L, 77.6 \u00b1 13.1%, 77.6 \u00b1 15.4%, and 77.6 \u00b1 15.4%, respectively. The lowest precision, sensitivity, and F1 score were observed in station 10L. The average processing and prediction time for a sequence of ten images was 0.65 \u00b1 0.04 s, demonstrating the feasibility of real-time applications. In conclusion, the new DNN-based model could be used to classify lymph node stations from EBUS images. The method performance was promising with a potential for clinical use.<\/jats:p>","DOI":"10.3390\/jimaging11010010","type":"journal-article","created":{"date-parts":[[2025,1,6]],"date-time":"2025-01-06T04:37:10Z","timestamp":1736138230000},"page":"10","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["A New Deep Learning-Based Method for Automated Identification of Thoracic Lymph Node Stations in Endobronchial Ultrasound (EBUS): A Proof-of-Concept Study"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-5857-3295","authenticated-orcid":false,"given":"\u00d8yvind","family":"Ervik","sequence":"first","affiliation":[{"name":"Clinic of Medicine, Nord-Tr\u00f8ndelag Hospital Trust, Levanger Hospital, 7601 Levanger, Norway"},{"name":"Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7030 Trondheim, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mia","family":"R\u00f8dde","sequence":"additional","affiliation":[{"name":"Department of Health Research, SINTEF Digital, 7034 Trondheim, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Erlend Fagertun","family":"Hofstad","sequence":"additional","affiliation":[{"name":"Department of Health Research, SINTEF Digital, 7034 Trondheim, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9386-8639","authenticated-orcid":false,"given":"Ingrid","family":"Tveten","sequence":"additional","affiliation":[{"name":"Department of Health Research, SINTEF Digital, 7034 Trondheim, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Thomas","family":"Lang\u00f8","sequence":"additional","affiliation":[{"name":"Department of Health Research, SINTEF Digital, 7034 Trondheim, Norway"},{"name":"National Research Center for Minimally Invasive and Image-Guided Diagnostics and Therapy, St. Olavs Hospital, 7030 Trondheim, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"H\u00e5kon O.","family":"Leira","sequence":"additional","affiliation":[{"name":"Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7030 Trondheim, Norway"},{"name":"National Research Center for Minimally Invasive and Image-Guided Diagnostics and Therapy, St. Olavs Hospital, 7030 Trondheim, Norway"},{"name":"Department of Thoracic Medicine, St. Olavs Hospital, Trondheim University Hospital, 7030 Trondheim, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4714-1949","authenticated-orcid":false,"given":"Tore","family":"Amundsen","sequence":"additional","affiliation":[{"name":"Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7030 Trondheim, Norway"},{"name":"Department of Thoracic Medicine, St. Olavs Hospital, Trondheim University Hospital, 7030 Trondheim, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9968-3491","authenticated-orcid":false,"given":"Hanne","family":"Sorger","sequence":"additional","affiliation":[{"name":"Clinic of Medicine, Nord-Tr\u00f8ndelag Hospital Trust, Levanger Hospital, 7601 Levanger, Norway"},{"name":"Department of Circulation and Medical Imaging, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, 7030 Trondheim, Norway"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,1,5]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"iv1","DOI":"10.1093\/annonc\/mdx222","article-title":"Early and locally advanced non-small-cell lung cancer (NSCLC): ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up","volume":"28","author":"Postmus","year":"2017","journal-title":"Ann. 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