{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T03:23:10Z","timestamp":1792466590614,"version":"4.5.0"},"reference-count":26,"publisher":"Ovid Technologies (Wolters Kluwer Health)","issue":"5","license":[{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-sa\/4.0\/"}],"content-domain":{"domain":["lww.com","ovid.com"],"crossmark-restriction":true},"short-container-title":["Endosc Ultrasound"],"published-print":{"date-parts":[[2023,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n          <jats:sec>\n            <jats:title>Background and Objectives<\/jats:title>\n            <jats:p>EUS is a crucial diagnostic and therapeutic method for many anatomical regions, especially in the evaluation of mediastinal diseases and related pathologies. Rapidly finding the standard stations is the key to achieving efficient and complete mediastinal EUS imaging. However, it requires substantial technical skills and extensive knowledge of mediastinal anatomy. We constructed a system, named EUS-MPS (EUS\u2013mediastinal position system), for real-time mediastinal EUS station recognition.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Methods<\/jats:title>\n            <jats:p>The standard scanning of mediastinum EUS was divided into 7 stations. There were 33 010 images in mediastinum EUS examination collected to construct a station classification model. Then, we used 151 videos clips for video validation and used 1212 EUS images from 2 other hospitals for external validation. An independent data set containing 230 EUS images was applied for the man-machine contest. We conducted a crossover study to evaluate the effectiveness of this system in reducing the difficulty of mediastinal ultrasound image interpretation.<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Results<\/jats:title>\n            <jats:p>For station classification, the model achieved an accuracy of 90.49% in image validation and 83.80% in video validation. At external validation, the models achieved 89.85% accuracy. In the man-machine contest, the model achieved an accuracy of 84.78%, which was comparable to that of expert (83.91%). The accuracy of the trainees' station recognition was significantly improved in the crossover study, with an increase of 13.26% (95% confidence interval, 11.04%\u201315.48%; <jats:italic toggle=\"yes\">P<\/jats:italic> &lt; 0.05).<\/jats:p>\n          <\/jats:sec>\n          <jats:sec>\n            <jats:title>Conclusions<\/jats:title>\n            <jats:p>This deep learning\u2013based system shows great performance in mediastinum station localization, having the potential to play an important role in shortening the learning curve and establishing standard mediastinal scanning in the future.<\/jats:p>\n          <\/jats:sec>","DOI":"10.1097\/eus.0000000000000011","type":"journal-article","created":{"date-parts":[[2023,10,31]],"date-time":"2023-10-31T09:00:42Z","timestamp":1698742842000},"page":"417-423","update-policy":"https:\/\/doi.org\/10.1097\/lww.0000000000001000","source":"Crossref","is-referenced-by-count":5,"title":["A deep learning\u2013based system for mediastinum station localization in linear EUS (with video)"],"prefix":"10.1097","volume":"12","author":[{"given":"Liwen","family":"Yao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chenxia","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Xu","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Wuhan Fourth Hospital, Wuhan, Hubei Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shanshan","family":"Yi","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Wuhan Fourth Hospital, Wuhan, Hubei Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Juan","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Wuhan Fourth Hospital, Wuhan, Hubei Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiangwu","family":"Ding","sequence":"additional","affiliation":[{"name":"Department of Gastroenterology, Wuhan Fourth Hospital, Wuhan, Hubei Province, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Honggang","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"276","published-online":{"date-parts":[[2023,10,16]]},"reference":[{"issue":"40","key":"bib1-20250826","doi-asserted-by":"crossref","first-page":"6874","DOI":"10.3748\/wjg.v27.i40.6874","article-title":"Endoscopic ultrasound-guided vascular interventions: current insights and emerging techniques","volume":"27","year":"2021","journal-title":"World J Gastroenterol"},{"issue":"6","key":"bib2-20250826","doi-asserted-by":"crossref","first-page":"406","DOI":"10.4103\/EUS-D-21-00019","article-title":"Practical approach to linear EUS examination of the mediastinum","volume":"10","year":"2021","journal-title":"Endosc Ultrasound"},{"issue":"10","key":"bib3-20250826","doi-asserted-by":"crossref","first-page":"1277","DOI":"10.6004\/jnccn.2012.0131","article-title":"Use of endobronchial ultrasound and endoscopic ultrasound to stage the mediastinum in early-stage lung cancer","volume":"10","year":"2012","journal-title":"J Natl Compr Canc Netw"},{"issue":"1","key":"bib4-20250826","doi-asserted-by":"crossref","first-page":"64","DOI":"10.1016\/j.gie.2009.07.027","article-title":"Implementation of endoscopic ultrasound for lung cancer staging","volume":"71","year":"2010","journal-title":"Gastrointest Endosc"},{"issue":"suppl 1","key":"bib5-20250826","first-page":"S84","article-title":"State of the art lecture: mediastinal EUS","volume":"38","year":"2006","journal-title":"Endoscopy"},{"issue":"1","key":"bib6-20250826","doi-asserted-by":"crossref","first-page":"99","DOI":"10.1097\/LBR.0b013e318207e6d5","article-title":"Standard techniques of imaging of IASLC Borders by endoscopic ultrasound","volume":"18","year":"2011","journal-title":"J Bronchol Interv Pulmonol"},{"key":"bib7-20250826","first-page":"S180","article-title":"Standard imaging techniques of endoscopic ultrasound-guided fine-needle aspiration using a curved linear array echoendoscope","volume":"19","year":"2007","journal-title":"Dig Endosc"},{"issue":"5","key":"bib8-20250826","doi-asserted-by":"crossref","first-page":"1116","DOI":"10.1016\/j.gie.2011.08.043","article-title":"Learning EUS tricks from the masters","volume":"74","year":"2011","journal-title":"Gastrointest Endosc"},{"issue":"17","key":"bib9-20250826","doi-asserted-by":"crossref","first-page":"5176","DOI":"10.3748\/wjg.v21.i17.5176","article-title":"Learning models for endoscopic ultrasonography in gastrointestinal endoscopy","volume":"21","year":"2015","journal-title":"World J Gastroenterol"},{"issue":"2","key":"bib10-20250826","doi-asserted-by":"crossref","first-page":"298","DOI":"10.1111\/den.13880","article-title":"Current status of artificial intelligence analysis for endoscopic ultrasonography","volume":"33","year":"2021","journal-title":"Dig Endosc"},{"issue":"7","key":"bib11-20250826","doi-asserted-by":"crossref","first-page":"1335","DOI":"10.1136\/gutjnl-2020-322821","article-title":"Utilisation of artificial intelligence for the development of an EUS\u2013convolutional neural network model trained to enhance the diagnosis of autoimmune pancreatitis","volume":"70","year":"2021","journal-title":"Gut"},{"issue":"4","key":"bib12-20250826","doi-asserted-by":"crossref","first-page":"874","DOI":"10.1016\/j.gie.2020.04.071","article-title":"Deep learning\u2013based pancreas segmentation and station recognition system in EUS: development and validation of a useful training tool (with video)","volume":"92","year":"2020","journal-title":"Gastrointest Endosc"},{"key":"bib13-20250826","doi-asserted-by":"crossref","first-page":"103650","DOI":"10.1016\/j.ebiom.2021.103650","article-title":"Corrigendum to \u201cA deep learning\u2013based system for bile duct annotation and station recognition in linear endoscopic ultrasound\u201d [eBioMedicine 65(2021) 103238]","volume":"73","year":"2021","journal-title":"EBioMedicine"},{"issue":"4","key":"bib16-20250826","doi-asserted-by":"crossref","first-page":"761","DOI":"10.1016\/S0893-6080(98)00010-0","article-title":"Automatic early stopping using cross validation: quantifying the criteria","volume":"11","year":"1998","journal-title":"Neural Netw"},{"key":"bib17-20250826","doi-asserted-by":"crossref","first-page":"78","DOI":"10.1016\/j.artint.2014.02.004","article-title":"The dropout learning algorithm","volume":"210","year":"2014","journal-title":"Artif Intell"},{"issue":"2","key":"bib18-20250826","doi-asserted-by":"crossref","first-page":"241","DOI":"10.1016\/j.gie.2020.05.021","article-title":"GIE editorial board top 10 topics: advances in GI endoscopy in 2019","volume":"92","year":"2020","journal-title":"Gastrointest Endosc"},{"issue":"5","key":"bib19-20250826","doi-asserted-by":"crossref","first-page":"1016","DOI":"10.1016\/j.gie.2020.05.047","article-title":"The status of training in new technologies in advanced endoscopy: from defining competence to credentialing and privileging","volume":"92","year":"2020","journal-title":"Gastrointest Endosc"},{"issue":"1","key":"bib20-20250826","doi-asserted-by":"crossref","first-page":"496","DOI":"10.21037\/tlcr.2020.03.08","article-title":"Mediastinal staging for non\u2013small cell lung cancer","volume":"10","year":"2021","journal-title":"Transl Lung Cancer Res"},{"issue":"6","key":"bib21-20250826","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1055\/s-0034-1392040","article-title":"Combined endobronchial and esophageal endosonography for the diagnosis and staging of lung cancer: European Society of Gastrointestinal Endoscopy (ESGE) guideline, in cooperation with the European Respiratory Society (ERS) and the European Society of Thoracic Surgeons (ESTS)","volume":"47","year":"2015","journal-title":"Endoscopy"},{"issue":"3","key":"bib22-20250826","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1016\/j.hoc.2017.02.002","article-title":"Staging in esophageal and gastric cancers","volume":"31","year":"2017","journal-title":"Hematol Oncol Clin North Am"},{"issue":"3","key":"bib23-20250826","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1016\/j.gie.2020.04.026","article-title":"Core curriculum for EUS","volume":"92","year":"2020","journal-title":"Gastrointest Endosc"},{"issue":"10","key":"bib24-20250826","doi-asserted-by":"crossref","first-page":"1071","DOI":"10.1055\/a-1537-8999","article-title":"Curriculum for ERCP and endoscopic ultrasound training in Europe: European Society of Gastrointestinal Endoscopy (ESGE) position statement","volume":"53","year":"2021","journal-title":"Endoscopy"},{"issue":"2","key":"bib25-20250826","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1016\/j.gie.2016.10.036","article-title":"Guidelines for privileging, credentialing, and proctoring to perform GI endoscopy","volume":"85","year":"2017","journal-title":"Gastrointest Endosc"},{"issue":"7","key":"bib26-20250826","doi-asserted-by":"crossref","first-page":"1985","DOI":"10.1053\/j.gastro.2017.11.293","article-title":"Advances and challenges in endoscopic training","volume":"154","year":"2018","journal-title":"Gastroenterology"},{"issue":"4","key":"bib27-20250826","doi-asserted-by":"crossref","first-page":"352","DOI":"10.1016\/S2468-1253(19)30413-3","article-title":"Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): a randomised controlled study","volume":"5","year":"2020","journal-title":"Lancet Gastroenterol Hepatol"},{"issue":"12","key":"bib28-20250826","doi-asserted-by":"crossref","first-page":"2161","DOI":"10.1136\/gutjnl-2018-317366","article-title":"Randomised controlled trial of WISENSE, a real-time quality improving system for monitoring blind spots during esophagogastroduodenoscopy","volume":"68","year":"2019","journal-title":"Gut"}],"container-title":["Endoscopic Ultrasound"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.lww.com\/10.1097\/eus.0000000000000011","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,26]],"date-time":"2025-08-26T18:03:47Z","timestamp":1756231427000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.lww.com\/10.1097\/eus.0000000000000011"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,9]]},"references-count":26,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2023]]}},"URL":"https:\/\/doi.org\/10.1097\/eus.0000000000000011","relation":{},"ISSN":["2226-7190","2303-9027"],"issn-type":[{"value":"2226-7190","type":"electronic"},{"value":"2303-9027","type":"print"}],"subject":[],"published":{"date-parts":[[2023,9]]}}}