{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T20:51:33Z","timestamp":1792788693841,"version":"4.5.0"},"reference-count":31,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T00:00:00Z","timestamp":1632355200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004663","name":"Ministry of Science and Technology, Taiwan","doi-asserted-by":"publisher","id":[{"id":"10.13039\/501100004663","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:p><jats:bold>Background:<\/jats:bold> The use of focused assessment with sonography in trauma (FAST) enables clinicians to rapidly screen for injury at the bedsides of patients. Pre-hospital FAST improves diagnostic accuracy and streamlines patient care, leading to dispositions to appropriate treatment centers. In this study, we determine the accuracy of artificial intelligence model-assisted free-fluid detection in FAST examinations, and subsequently establish an automated feedback system, which can help inexperienced sonographers improve their interpretation ability and image acquisition skills.<\/jats:p><jats:p><jats:bold>Methods:<\/jats:bold> This is a single-center study of patients admitted to the emergency room from January 2020 to March 2021. We collected 324 patient records for the training model, 36 patient records for validation, and another 36 patient records for testing. We balanced positive and negative Morison's pouch free-fluid detection groups in a 1:1 ratio. The deep learning (DL) model Residual Networks 50-Version 2 (ResNet50-V2) was used for training and validation.<\/jats:p><jats:p><jats:bold>Results:<\/jats:bold> The accuracy, sensitivity, and specificity of the model performance for ascites prediction were 0.961, 0.976, and 0.947, respectively, in the validation set and 0.967, 0.985, and 0.913, respectively, in the test set. Regarding feedback prediction, the model correctly classified qualified and non-qualified images with an accuracy of 0.941 in both the validation and test sets.<\/jats:p><jats:p><jats:bold>Conclusions:<\/jats:bold> The DL algorithm in ResNet50-V2 is able to detect free fluid in Morison's pouch with high accuracy. The automated feedback and instruction system could help inexperienced sonographers improve their interpretation ability and image acquisition skills.<\/jats:p>","DOI":"10.3389\/fmed.2021.707437","type":"journal-article","created":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T06:26:08Z","timestamp":1632378368000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":38,"title":["Deep Learning Assisted Detection of Abdominal Free Fluid in Morison's Pouch During Focused Assessment With Sonography in Trauma"],"prefix":"10.3389","volume":"8","author":[{"given":"Chi-Yung","family":"Cheng","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"I-Min","family":"Chiu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ming-Ya","family":"Hsu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hsiu-Yung","family":"Pan","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chih-Min","family":"Tsai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chun-Hung Richard","family":"Lin","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2021,9,23]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1016\/j.annemergmed.2006.01.008","article-title":"Randomized controlled clinical trial of point-of-care, limited ultrasonography for trauma in the emergency department: the first sonography outcomes assessment program trial","volume":"48","author":"Melniker","year":"2006","journal-title":"Ann Emerg Med"},{"key":"B2","unstructured":"National Center for Injury Prevention and Control"},{"key":"B3","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1080\/08998280.2013.11928934","article-title":"Timing and causes of death after injuries","volume":"26","author":"Sobrino","year":"2013","journal-title":"Proc (Bayl Univ Med Cent)"},{"key":"B4","doi-asserted-by":"publisher","first-page":"249","DOI":"10.1097\/MEJ.0b013e328336adce","article-title":"Does prehospital ultrasound improve treatment of the trauma patient? A systematic review","volume":"17","author":"J\u00f8rgensen","year":"2010","journal-title":"Eur J Emerg Med"},{"key":"B5","doi-asserted-by":"publisher","first-page":"238","DOI":"10.1002\/bjs.5213","article-title":"Prehospital ultrasound imaging improves management of abdominal trauma","volume":"93","author":"Walcher","year":"2006","journal-title":"Br J Surg"},{"key":"B6","doi-asserted-by":"publisher","first-page":"512","DOI":"10.1016\/j.injury.2007.11.010","article-title":"The F.A.S.T.E.R. trial. Focused assessment by sonography in trauma during emergency retrieval: a feasibility study","volume":"39","author":"Mazur","year":"2008","journal-title":"Injury"},{"key":"B7","doi-asserted-by":"publisher","first-page":"67","DOI":"10.1016\/j.jmu.2015.02.006","article-title":"Physician-performed Focused Ultrasound: an update on its role and performance","volume":"23","author":"Limchareon","year":"2015","journal-title":"J Med Ultrasound"},{"key":"B8","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1016\/j.eng.2018.11.020","article-title":"Deep learning in medical ultrasound analysis: a review","volume":"5","author":"Liu","year":"2019","journal-title":"Engineering"},{"key":"B9","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/s10278-016-9929-2","article-title":"Transfer learning with convolutional neural networks for classification of abdominal ultrasound images","volume":"30","author":"Cheng","year":"2017","journal-title":"J Digit Imaging"},{"key":"B10","doi-asserted-by":"publisher","first-page":"014502","DOI":"10.1117\/1.JMI.4.1.014502","article-title":"Automated detection of coarctation of aorta in neonates from two-dimensional echocardiograms","volume":"4","author":"Pereira","year":"2017","journal-title":"J Med Imaging"},{"key":"B11","doi-asserted-by":"publisher","first-page":"1678","DOI":"10.1002\/mp.12134","article-title":"Cascade convolutional neural networks for automatic detection of thyroid nodules in ultrasound images","volume":"44","author":"Ma","year":"2017","journal-title":"Med Phys"},{"key":"B12","doi-asserted-by":"publisher","first-page":"1218","DOI":"10.1109\/JBHI.2017.2731873","article-title":"Automated breast ultrasound lesions detection using convolutional neural networks","volume":"22","author":"Yap","year":"2018","journal-title":"IEEE J Biomed Health Inform"},{"key":"B13","doi-asserted-by":"publisher","first-page":"4057","DOI":"10.1016\/j.ijleo.2014.01.114","article-title":"Deep learning based classification of focal liver lesions with contrast-enhanced ultrasound","volume":"125","author":"Wu","year":"2014","journal-title":"Optik"},{"key":"B14","doi-asserted-by":"publisher","first-page":"1293","DOI":"10.1007\/s11548-017-1627-0","article-title":"Detection and grading of prostate cancer using temporal enhanced ultrasound: combining deep neural networks and tissue mimicking simulations","volume":"12","author":"Azizi","year":"2017","journal-title":"Int J Comput Assist Radiol Surg"},{"key":"B15","first-page":"770","article-title":"Deep residual learning for image recognition","volume-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition","author":"He","year":"2016"},{"key":"B16","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1109\/CVPR.2009.5206848","article-title":"ImageNet: a large-scale hierarchical image database","volume-title":"2009 IEEE Conference on Computer Vision and Pattern Recognition","author":"Deng","year":"2009"},{"key":"B17","doi-asserted-by":"publisher","first-page":"400","DOI":"10.3109\/10903127.2012.664242","article-title":"Diagnostic accuracy of focused assessment with sonography for trauma (FAST) examinations performed by emergency medical technicians","volume":"16","author":"Kim","year":"2012","journal-title":"Prehosp Emerg Care"},{"key":"B18","doi-asserted-by":"publisher","first-page":"e5","DOI":"10.1136\/emj.2003.006932","article-title":"Prospective evaluation of non-radiologist performed emergency abdominal ultrasound for haemoperitoneum","volume":"21","author":"Brooks","year":"2004","journal-title":"Emerg Med J"},{"key":"B19","doi-asserted-by":"publisher","first-page":"492","DOI":"10.1097\/00005373-199509000-00016","article-title":"A prospective study of surgeon-performed ultrasound as the primary adjuvant modality for injured patient assessment","volume":"39","author":"Rozycki","year":"1995","journal-title":"J Trauma"},{"key":"B20","doi-asserted-by":"publisher","first-page":"970","DOI":"10.1016\/j.injury.2005.02.026","article-title":"Diagnostic accuracy of surgeon-performed focused abdominal sonography (FAST) in blunt paediatric trauma","volume":"36","author":"Soundappan","year":"2005","journal-title":"Injury"},{"key":"B21","doi-asserted-by":"publisher","first-page":"1294","DOI":"10.1097\/TA.0b013e31819dc688","article-title":"Management of patients with anterior abdominal stab wounds: a Western Trauma Association multicenter trial","volume":"66","author":"Biffl","year":"2009","journal-title":"J Trauma"},{"key":"B22","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1097\/00005373-200108000-00015","article-title":"The routine use of sonography in penetrating torso injury is beneficial","volume":"51","author":"Boulanger","year":"2001","journal-title":"J Trauma"},{"key":"B23","doi-asserted-by":"publisher","first-page":"953","DOI":"10.1097\/01.TA.0000127806.39852.4E","article-title":"A prospective evaluation of ultrasonography for the diagnosis of penetrating torso injury","volume":"56","author":"Soffer","year":"2004","journal-title":"J Trauma"},{"key":"B24","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1179\/174313408X322750","article-title":"When FAST is a FAFF: is FAST scanning useful in non-trauma patients?","volume":"16","author":"Maitra","year":"2008","journal-title":"Ultrasound"},{"key":"B25","article-title":"Deep learning with spatiotemporal consistency for nerve segmentation in ultrasound images","author":"Hafiane","year":"2017"},{"key":"B26","doi-asserted-by":"publisher","first-page":"574","DOI":"10.1111\/acem.12653","article-title":"Learning curves in emergency ultrasound education","volume":"22","author":"Blehar","year":"2015","journal-title":"Acad Emerg Med"},{"key":"B27","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1111\/j.1553-2712.2011.01242.x","article-title":"The technical errors of physicians learning to perform focused assessment with sonography in trauma","volume":"19","author":"Jang","year":"2012","journal-title":"Acad Emerg Med"},{"key":"B28","first-page":"432","article-title":"The \u201cAirmedical F.A.S.T.\u201d for trauma patients\u2013the initial report of a novel application for sonography","volume":"72","author":"Polk","year":"2001","journal-title":"Aviat Space Environ Med"},{"key":"B29","doi-asserted-by":"publisher","first-page":"476","DOI":"10.1016\/S0735-6757(03)00162-1","article-title":"Sensitivity in detecting free intraperitoneal fluid with the pelvic views of the FAST exam","volume":"21","author":"Von Kuenssberg Jehle","year":"2003","journal-title":"Am J Emerg Med"},{"key":"B30","doi-asserted-by":"publisher","first-page":"270","DOI":"10.5811\/westjem.2016.11.30435","article-title":"Caudal edge of the liver in the Right Upper Quadrant (RUQ) view is the most sensitive area for free fluid on the FAST exam","volume":"18","author":"Lobo","year":"2017","journal-title":"West J Emerg Med"},{"key":"B31","doi-asserted-by":"publisher","first-page":"299","DOI":"10.1053\/ajem.2001.24481","article-title":"The UHP ultrasound protocol: a novel ultrasound approach to the empiric evaluation of the undifferentiated hypotensive patient","volume":"19","author":"Rose","year":"2001","journal-title":"Am J Emerg Med"}],"container-title":["Frontiers in Medicine"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2021.707437\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,9,23]],"date-time":"2021-09-23T06:26:12Z","timestamp":1632378372000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fmed.2021.707437\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9,23]]},"references-count":31,"alternative-id":["10.3389\/fmed.2021.707437"],"URL":"https:\/\/doi.org\/10.3389\/fmed.2021.707437","relation":{},"ISSN":["2296-858X"],"issn-type":[{"value":"2296-858X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,9,23]]},"article-number":"707437"}}