{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,10]],"date-time":"2026-07-10T12:50:56Z","timestamp":1783687856550,"version":"3.55.0"},"reference-count":75,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2023,5,27]],"date-time":"2023-05-27T00:00:00Z","timestamp":1685145600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,5,27]],"date-time":"2023-05-27T00:00:00Z","timestamp":1685145600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/100000057","name":"National Institute of General Medical Sciences","doi-asserted-by":"publisher","award":["R44GM123821"],"award-info":[{"award-number":["R44GM123821"]}],"id":[{"id":"10.13039\/100000057","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100006108","name":"National Center for Advancing Translational Sciences","doi-asserted-by":"publisher","award":["1UL1TR001430"],"award-info":[{"award-number":["1UL1TR001430"]}],"id":[{"id":"10.13039\/100006108","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1007\/s11517-023-02855-6","type":"journal-article","created":{"date-parts":[[2023,5,27]],"date-time":"2023-05-27T06:01:46Z","timestamp":1685167306000},"page":"1947-1959","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Automated detection and localization of pericardial effusion from point-of-care cardiac ultrasound examination"],"prefix":"10.1007","volume":"61","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2827-7672","authenticated-orcid":false,"given":"\u0130lkay","family":"Y\u0131ld\u0131z Potter","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Megan M.","family":"Leo","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ashkan","family":"Vaziri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"James A.","family":"Feldman","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2023,5,27]]},"reference":[{"issue":"1","key":"2855_CR1","doi-asserted-by":"publisher","first-page":"15","DOI":"10.1016\/j.jemermed.2005.01.004","volume":"29","author":"JS Rose","year":"2005","unstructured":"Rose JS et al (2005) The fast is positive, now what? Derivation of a clinical decision rule to determine the need for therapeutic laparotomy in adults with blunt torso trauma and a positive trauma ultrasound. J Emerg Med 29(1):15\u201321","journal-title":"J Emerg Med"},{"issue":"3","key":"2855_CR2","doi-asserted-by":"publisher","first-page":"265","DOI":"10.1016\/j.jemermed.2007.02.030","volume":"33","author":"M Moylan","year":"2007","unstructured":"Moylan M et al (2007) Association between a positive ED FAST examination and therapeutic laparotomy in normotensive blunt trauma patients. J Emerg Med 33(3):265\u201371","journal-title":"J Emerg Med"},{"issue":"6","key":"2855_CR3","doi-asserted-by":"publisher","first-page":"728","DOI":"10.1016\/j.amjsurg.2007.08.012","volume":"194","author":"TS Helling","year":"2007","unstructured":"Helling TS, Wilson J, Augustosky K (2007) The utility of focused abdominal ultrasound in blunt abdominal trauma: a reappraisal. Am J Surg 194(6):728\u201332 (discussion 732-3)","journal-title":"Am J Surg"},{"issue":"5","key":"2855_CR4","doi-asserted-by":"publisher","first-page":"482","DOI":"10.1016\/j.injury.2010.07.249","volume":"42","author":"AC Quinn","year":"2011","unstructured":"Quinn AC, Sinert R (2011) What is the utility of the Focused Assessment with Sonography in Trauma (FAST) exam in penetrating torso trauma? Injury 42(5):482\u2013487","journal-title":"Injury"},{"issue":"3","key":"2855_CR5","doi-asserted-by":"publisher","first-page":"227","DOI":"10.1016\/j.annemergmed.2006.01.008","volume":"48","author":"LA Melniker","year":"2006","unstructured":"Melniker LA et al (2006) Randomized controlled clinical trial of point-of-care, limited ultrasonography for trauma in the emergency department: the first sonography outcomes assessment program trial. Ann Emerg Med 48(3):227\u2013235","journal-title":"Ann Emerg Med"},{"key":"2855_CR6","volume-title":"advanced trauma life support course for physicians","author":"American college of surgeons committee on trauma","year":"1997","unstructured":"American college of surgeons committee on trauma (1997) advanced trauma life support course for physicians. American College of Surgeons, Chicago"},{"issue":"4","key":"2855_CR7","doi-asserted-by":"publisher","first-page":"467","DOI":"10.7863\/jum.2004.23.4.467","volume":"23","author":"VS Tayal","year":"2004","unstructured":"Tayal VS et al (2004) FAST (focused assessment with sonography in trauma) accurate for cardiac and intraperitoneal injury in penetrating anterior chest trauma. J Ultrasound Med 23(4):467\u2013472","journal-title":"J Ultrasound Med"},{"issue":"6","key":"2855_CR8","doi-asserted-by":"publisher","first-page":"879","DOI":"10.1097\/00005373-199506000-00009","volume":"38","author":"OJ Ma","year":"1995","unstructured":"Ma OJ et al (1995) Prospective analysis of a rapid trauma ultrasound examination performed by emergency physicians. J Trauma 38(6):879\u2013885","journal-title":"J Trauma"},{"issue":"5","key":"2855_CR9","doi-asserted-by":"publisher","first-page":"580","DOI":"10.1136\/emj.2003.006932","volume":"21","author":"A Brooks","year":"2004","unstructured":"Brooks A et al (2004) Prospective evaluation of non-radiologist performed emergency abdominal ultrasound for haemoperitoneum. Emerg Med J 21(5):580\u2013581","journal-title":"Emerg Med J"},{"issue":"3","key":"2855_CR10","doi-asserted-by":"publisher","first-page":"492","DOI":"10.1097\/00005373-199509000-00016","volume":"39","author":"GS Rozycki","year":"1995","unstructured":"Rozycki GS et al (1995) A prospective study of surgeon-performed ultrasound as the primary adjuvant modality for injured patient assessment. J Trauma 39(3):492\u20138 (discussion 498-500)","journal-title":"J Trauma"},{"issue":"8","key":"2855_CR11","doi-asserted-by":"publisher","first-page":"970","DOI":"10.1016\/j.injury.2005.02.026","volume":"36","author":"SV Soundappan","year":"2005","unstructured":"Soundappan SV et al (2005) Diagnostic accuracy of surgeon-performed focused abdominal sonography (FAST) in blunt paediatric trauma. Injury 36(8):970\u2013975","journal-title":"Injury"},{"issue":"1","key":"2855_CR12","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1097\/00005373-199101000-00004","volume":"31","author":"A Kimura","year":"1991","unstructured":"Kimura A, Otsuka T (1991) Emergency center ultrasonography in the evaluation of hemoperitoneum: a prospective study. J Trauma 31(1):20\u201323","journal-title":"J Trauma"},{"issue":"5","key":"2855_CR13","first-page":"1294","volume":"66","author":"WL Biffl","year":"2009","unstructured":"Biffl WL et al (2009) Management of patients with anterior abdominal stab wounds: a Western Trauma Association multicenter trial. J Trauma 66(5):1294\u20131301","journal-title":"J Trauma"},{"issue":"9","key":"2855_CR14","doi-asserted-by":"publisher","first-page":"778","DOI":"10.1136\/emermed-2012-201336","volume":"30","author":"G O\u2019Connor","year":"2013","unstructured":"O\u2019Connor G et al (2013) Looking beyond Morison\u2019s pouch in focused assessment with sonography for trauma: penetrating hepatobiliary trauma and a new sign for emergency physicians. Emerg Med J 30(9):778\u2013779","journal-title":"Emerg Med J"},{"issue":"9","key":"2855_CR15","doi-asserted-by":"publisher","first-page":"1259","DOI":"10.7863\/jum.2009.28.9.1259","volume":"28","author":"B Hoffmann","year":"2009","unstructured":"Hoffmann B, Nguyen H, Hill HF (2009) Diaphragmatic laceration after penetrating trauma: direct visualization and indirect findings on focused assessment with sonography for trauma in the emergency department. J Ultrasound Med 28(9):1259\u20131263","journal-title":"J Ultrasound Med"},{"issue":"2","key":"2855_CR16","doi-asserted-by":"publisher","first-page":"320","DOI":"10.1097\/00005373-200108000-00015","volume":"51","author":"BR Boulanger","year":"2001","unstructured":"Boulanger BR et al (2001) The routine use of sonography in penetrating torso injury is beneficial. J Trauma 51(2):320\u2013325","journal-title":"J Trauma"},{"issue":"5","key":"2855_CR17","doi-asserted-by":"publisher","first-page":"660","DOI":"10.1016\/j.amjsurg.2004.02.003","volume":"187","author":"AW Kirkpatrick","year":"2004","unstructured":"Kirkpatrick AW et al (2004) The hand-held ultrasound examination for penetrating abdominal trauma. Am J Surg 187(5):660\u2013665","journal-title":"Am J Surg"},{"issue":"5","key":"2855_CR18","doi-asserted-by":"publisher","first-page":"953","DOI":"10.1097\/01.TA.0000127806.39852.4E","volume":"56","author":"D Soffer","year":"2004","unstructured":"Soffer D et al (2004) A prospective evaluation of ultrasonography for the diagnosis of penetrating torso injury. J Trauma 56(5):953\u20137 (discussion 957-9)","journal-title":"J Trauma"},{"issue":"2","key":"2855_CR19","doi-asserted-by":"publisher","first-page":"375","DOI":"10.1097\/00005373-199508000-00032","volume":"39","author":"SW Branney","year":"1995","unstructured":"Branney SW et al (1995) Quantitative sensitivity of ultrasound in detecting free intraperitoneal fluid. J Trauma 39(2):375\u2013380","journal-title":"J Trauma"},{"issue":"1","key":"2855_CR20","doi-asserted-by":"publisher","first-page":"27","DOI":"10.1097\/00005373-199301000-00005","volume":"34","author":"PJ Bode","year":"1993","unstructured":"Bode PJ et al (1993) Abdominal ultrasound as a reliable indicator for conclusive laparotomy in blunt abdominal trauma. J Trauma 34(1):27\u201331","journal-title":"J Trauma"},{"issue":"4","key":"2855_CR21","doi-asserted-by":"publisher","first-page":"342","DOI":"10.1016\/0735-6757(93)90164-7","volume":"11","author":"D Jehle","year":"1993","unstructured":"Jehle D, Guarino J, Karamanoukian H (1993) Emergency department ultrasound in the evaluation of blunt abdominal trauma. Am J Emerg Med 11(4):342\u2013346","journal-title":"Am J Emerg Med"},{"issue":"10","key":"2855_CR22","doi-asserted-by":"publisher","first-page":"653","DOI":"10.7863\/jum.1997.16.10.653","volume":"16","author":"JP McGahan","year":"1997","unstructured":"McGahan JP et al (1997) Use of ultrasonography in the patient with acute abdominal trauma. J Ultrasound Med 16(10):653\u201362 (quiz 663-4)","journal-title":"J Ultrasound Med"},{"issue":"3","key":"2855_CR23","doi-asserted-by":"publisher","first-page":"165","DOI":"10.1179\/174313408X322750","volume":"16","author":"S Maitra","year":"2008","unstructured":"Maitra S et al (2008) When FAST is a FAFF: is FAST scanning useful in non-trauma patients? Ultrasound 16(3):165\u2013168","journal-title":"Ultrasound"},{"issue":"8","key":"2855_CR24","doi-asserted-by":"publisher","first-page":"755","DOI":"10.1197\/j.aem.2007.04.010","volume":"14","author":"C Moore","year":"2007","unstructured":"Moore C et al (2007) Free fluid in Morison\u2019s pouch on bedside ultrasound predicts need for operative intervention in suspected ectopic pregnancy. Acad Emerg Med 14(8):755\u2013758","journal-title":"Acad Emerg Med"},{"issue":"7","key":"2855_CR25","doi-asserted-by":"publisher","first-page":"1290","DOI":"10.1007\/s00134-013-2919-7","volume":"39","author":"G Volpicelli","year":"2013","unstructured":"Volpicelli G et al (2013) Point-of-care multiorgan ultrasonography for the evaluation of undifferentiated hypotension in the emergency department. Intensive Care Med 39(7):1290\u20131298","journal-title":"Intensive Care Med"},{"key":"2855_CR26","doi-asserted-by":"publisher","first-page":"33","DOI":"10.1016\/j.resuscitation.2016.09.018","volume":"109","author":"R Gaspari","year":"2016","unstructured":"Gaspari R, Weekes A, Adhikari S et al (2016) Emergency department point-of-care ultrasound in out-of-hospital and in-ED cardiac arrest. Resuscitation 109:33\u201339","journal-title":"Resuscitation"},{"issue":"4","key":"2855_CR27","doi-asserted-by":"publisher","first-page":"467","DOI":"10.7863\/jum.2004.23.4.467","volume":"23","author":"VS Tayal","year":"2004","unstructured":"Tayal VS, Beatty MA, Marx JA, Tomaszewski CA, Thomason MH (2004) FAST (focused assessment with sonography in trauma) accurate for cardiac and intraperitoneal injury in penetrating anterior chest trauma. J Ultrasound Med 23(4):467\u2013472. https:\/\/doi.org\/10.7863\/jum.2004.23.4.467","journal-title":"J Ultrasound Med"},{"issue":"6","key":"2855_CR28","doi-asserted-by":"publisher","first-page":"727","DOI":"10.1017\/cem.2019.381","volume":"21","author":"S Netherton","year":"2019","unstructured":"Netherton S, Milenkovic V, Taylor M, Davis P (2019) Diagnostic accuracy of eFAST in the trauma patient: A systematic review and meta-analysis. Can J Emerg Med 21(6):727\u2013738. https:\/\/doi.org\/10.1017\/cem.2019.381","journal-title":"Can J Emerg Med"},{"issue":"4","key":"2855_CR29","doi-asserted-by":"publisher","first-page":"415","DOI":"10.1111\/acem.12936","volume":"23","author":"MK Hall","year":"2016","unstructured":"Hall MK, Omer T, Moore CL, Taylor RA (2016) Cost-effectiveness of the cardiac component of the focused assessment of sonography in trauma examination in blunt trauma. Acad Emerg Med 23(4):415\u2013423. https:\/\/doi.org\/10.1111\/acem.12936","journal-title":"Acad Emerg Med"},{"issue":"2","key":"2855_CR30","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1016\/j.annemergmed.2005.08.023","volume":"47","author":"CL Moore","year":"2006","unstructured":"Moore CL, Molina AA, Lin H (2006) Ultrasonography in community emergency departments in the United States: access to ultrasonography performed by consultants and status of emergency physician-performed ultrasonography. Ann Emerg Med 47(2):147\u2013153","journal-title":"Ann Emerg Med"},{"issue":"Suppl 2","key":"2855_CR31","doi-asserted-by":"publisher","first-page":"S32","DOI":"10.1111\/j.1553-2712.2009.00589.x","volume":"16","author":"S Akhtar","year":"2009","unstructured":"Akhtar S et al (2009) Resident training in emergency ultrasound: consensus recommendations from the 2008 Council of Emergency Medicine Residency Directors Conference. Acad Emerg Med 16(Suppl 2):S32\u2013S36","journal-title":"Acad Emerg Med"},{"issue":"1","key":"2855_CR32","doi-asserted-by":"publisher","first-page":"37","DOI":"10.1197\/aemj.10.1.37","volume":"10","author":"FL Counselman","year":"2003","unstructured":"Counselman FL et al (2003) The status of bedside ultrasonography training in emergency medicine residency programs. Acad Emerg Med 10(1):37\u201342","journal-title":"Acad Emerg Med"},{"issue":"3","key":"2855_CR33","doi-asserted-by":"publisher","first-page":"453","DOI":"10.1016\/j.jamcollsurg.2005.10.023","volume":"202","author":"ML Freitas","year":"2006","unstructured":"Freitas ML, Frangos SG, Frankel HL (2006) The status of ultrasonography training and use in general surgery residency programs. J Am Coll Surg 202(3):453\u2013458","journal-title":"J Am Coll Surg"},{"issue":"5","key":"2855_CR34","doi-asserted-by":"publisher","first-page":"477","DOI":"10.1001\/jamacardio.2022.0193","volume":"7","author":"RM Wehbe","year":"2022","unstructured":"Wehbe RM, Thomas JD (2022) Validating deep learning to distinguish Takotsubo syndrome from acute myocardial infarction-beware of shortcuts, human supervision required. JAMA Cardiol 7(5):477\u2013479. https:\/\/doi.org\/10.1001\/jamacardio.2022.0193","journal-title":"JAMA Cardiol"},{"key":"2855_CR35","doi-asserted-by":"crossref","unstructured":"Pokaprakarn T, Prieto JC, Price JT, Kasaro MP, Sindano N, Shah HR, Peterson M, Akapelwa MM, Kapilya FM, Sebasti\u00e3o YV, Goodnight W (2022) AI estimation of gestational age from blind ultrasound sweeps in low-resource settings. N Engl J Med Evid 1(5):EVIDoa2100058","DOI":"10.1056\/EVIDoa2100058"},{"issue":"9","key":"2855_CR36","doi-asserted-by":"publisher","first-page":"964","DOI":"10.1001\/jamaophthalmol.2021.2273","volume":"139","author":"M Wilson","year":"2021","unstructured":"Wilson M, Chopra R, Wilson MZ et al (2021) Validation and clinical applicability of whole-volume automated segmentation of optical coherence tomography in retinal disease using deep learning. JAMA Ophthalmol 139(9):964\u2013973. https:\/\/doi.org\/10.1001\/jamaophthalmol.2021.2273","journal-title":"JAMA Ophthalmol"},{"issue":"1","key":"2855_CR37","doi-asserted-by":"publisher","first-page":"11","DOI":"10.1109\/JPROC.2019.2932116","volume":"108","author":"RJ Van Sloun","year":"2019","unstructured":"Van Sloun RJ, Cohen R, Eldar YC (2019) Deep learning in ultrasound imaging. Proc IEEE 108(1):11\u201329","journal-title":"Proc IEEE"},{"issue":"1","key":"2855_CR38","first-page":"73","volume":"6","author":"PHB Diniz","year":"2020","unstructured":"Diniz PHB, Yin Y, Collins S (2020) Deep learning strategies for ultrasound in pregnancy. Eur Med J Reprod Health 6(1):73\u201380","journal-title":"Eur Med J Reprod Health"},{"issue":"Generic","key":"2855_CR39","doi-asserted-by":"publisher","first-page":"261","DOI":"10.1016\/j.eng.2018.11.020","volume":"5","author":"S Liu","year":"2019","unstructured":"Liu S, Wang Y, Yang X et al (2019) Deep learning in medical ultrasound analysis: a review. Engineering 5(Generic):261\u2013275. https:\/\/doi.org\/10.1016\/j.eng.2018.11.020","journal-title":"Engineering"},{"issue":"9 Pt B","key":"2855_CR40","doi-asserted-by":"publisher","first-page":"1318","DOI":"10.1016\/j.jacr.2019.06.004","volume":"16","author":"Z Akkus","year":"2019","unstructured":"Akkus Z, Cai J, Boonrod A et al (2019) A survey of deep-learning applications in ultrasound: artificial intelligence-powered ultrasound for improving clinical workflow. J Am Coll Radiol 16(9 Pt B):1318\u20131328. https:\/\/doi.org\/10.1016\/j.jacr.2019.06.004","journal-title":"J Am Coll Radiol"},{"issue":"1","key":"2855_CR41","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1038\/s41746-017-0013-1","volume":"1","author":"A Madani","year":"2018","unstructured":"Madani A, Arnaout R, Mofrad M, Arnaout R (2018) Fast and accurate view classification of echocardiograms using deep learning. NPJ Digit Med 1(1):6","journal-title":"NPJ Digit Med"},{"key":"2855_CR42","doi-asserted-by":"publisher","first-page":"5682365","DOI":"10.1155\/2018\/5682365","volume":"2018","author":"S Dong","year":"2018","unstructured":"Dong S, Luo G, Wang K, Cao S, Li Q, Zhang H (2018) A combined fully convolutional networks and deformable model for automatic left ventricle segmentation based on 3d echocardiography. Biomed Res Int 2018:5682365","journal-title":"Biomed Res Int"},{"key":"2855_CR43","doi-asserted-by":"crossref","unstructured":"Moradi M, Guo Y, Gur Y, Negahdar M, Syeda-Mahmood T (2016) A cross-modality neural network transform for semi-automatic medical image annotation. In Medical Image Computing and Computer-Assisted Intervention\u2013MICCAI 2016: 19th International Conference, Athens, Greece. Proceedings, Part II 19 2016 (pp. 300\u2013307). Springer International Publishing","DOI":"10.1007\/978-3-319-46723-8_35"},{"issue":"5","key":"2855_CR44","doi-asserted-by":"publisher","first-page":"494","DOI":"10.1001\/jamacardio.2022.0183","volume":"7","author":"F Laumer","year":"2022","unstructured":"Laumer F, Di Vece D, Cammann VL et al (2022) Assessment of artificial intelligence in echocardiography diagnostics in differentiating Takotsubo syndrome from myocardial infarction. JAMA Cardiol 7(5):494\u2013503. https:\/\/doi.org\/10.1001\/jamacardio.2022.0183","journal-title":"JAMA Cardiol"},{"issue":"8 Part 1","key":"2855_CR45","first-page":"1549","volume":"12","author":"G Litjens","year":"2019","unstructured":"Litjens G, Ciompi F, Wolterink JM, de Vos BD, Leiner T, Teuwen J, I\u0161gum I (2019) State-of-the-art deep learning in cardiovascular image analysis. JACC: Cardiovasc Imaging 12(8 Part 1):1549\u20131565","journal-title":"JACC: Cardiovasc Imaging"},{"issue":"8","key":"2855_CR46","doi-asserted-by":"publisher","first-page":"707437","DOI":"10.3389\/fmed.2021.707437","volume":"23","author":"CY Cheng","year":"2021","unstructured":"Cheng CY, Chiu IM, Hsu MY, Pan HY, Tsai CM, Lin CR (2021) Deep learning assisted detection of abdominal free fluid in Morison\u2019s pouch during focused assessment with sonography in trauma. Front Med (Lausanne) 23(8):707437. https:\/\/doi.org\/10.3389\/fmed.2021.707437","journal-title":"Front Med (Lausanne)"},{"issue":"7","key":"2855_CR47","doi-asserted-by":"publisher","first-page":"e13695","DOI":"10.1002\/acm2.13695","volume":"23","author":"Z Lin","year":"2022","unstructured":"Lin Z, Li Z, Cao P et al (2022) Deep learning for emergency ascites diagnosis using ultrasonography images. J Appl Clin Med Phys 23(7):e13695. https:\/\/doi.org\/10.1002\/acm2.13695","journal-title":"J Appl Clin Med Phys"},{"issue":"11_Supplement_1","key":"2855_CR48","doi-asserted-by":"publisher","first-page":"1563","DOI":"10.1016\/S0735-1097(20)32190-2","volume":"75","author":"A Nayak","year":"2020","unstructured":"Nayak A, Ouyang D, Ashley EA (2020) A deep learning algorithm accurately detects pericardial effusion on echocardiography. J Am Coll Cardiol 75(11_Supplement_1):1563\u20131563","journal-title":"J Am Coll Cardiol"},{"key":"2855_CR49","doi-asserted-by":"crossref","unstructured":"Wu CC, Cheng CY, Chen HC, Hung CH, Chen TY, Lin CHR, Chiu IM (2022) Development and validation of an end-to-end deep learning pipeline to measure pericardial effusion in echocardiography. medRxiv pp.2022\u201308","DOI":"10.1101\/2022.08.13.22278732"},{"issue":"5","key":"2855_CR50","doi-asserted-by":"publisher","first-page":"e27","DOI":"10.1016\/j.annemergmed.2016.08.457","volume":"69","author":"U Guidelines","year":"2017","unstructured":"Guidelines U (2017) Emergency, Point-of-care and clinical ultrasound guidelines in medicine. Ann Emerg Med 69(5):e27\u2013e54. https:\/\/doi.org\/10.1016\/j.annemergmed.2016.08.457","journal-title":"Ann Emerg Med"},{"key":"2855_CR51","unstructured":"NCH Software Inc (2019) PhotoPad Image Editor [Computer software]. Retrieved from https:\/\/www.nchsoftware.com\/"},{"key":"2855_CR52","unstructured":"Redmon J, Farhadi A (2018) \u201cYOLOv3: An incremental improvement.\u201d arXiv preprint arxiv:1804.02767"},{"issue":"6","key":"2855_CR53","doi-asserted-by":"publisher","first-page":"9243","DOI":"10.1007\/s11042-022-13644-y","volume":"82","author":"T Diwan","year":"2023","unstructured":"Diwan T, Anirudh G, Tembhurne JV (2023) Object detection using YOLO: Challenges, architectural successors, datasets and applications. Multimed Tools Appl 82(6):9243\u201375","journal-title":"Multimed Tools Appl"},{"key":"2855_CR54","doi-asserted-by":"crossref","unstructured":"Han R, Liu X, Chen T (2022) Yolo-SG: Salience-Guided Detection Of Small Objects In Medical Images. In 2022 IEEE International Conference on Image Processing (ICIP) (pp. 4218\u20134222)","DOI":"10.1109\/ICIP46576.2022.9898077"},{"issue":"8","key":"2855_CR55","doi-asserted-by":"publisher","first-page":"1798","DOI":"10.1109\/TPAMI.2013.50","volume":"35","author":"Y Bengio","year":"2013","unstructured":"Bengio Y, Courville A, Vincent P (2013) Representation learning: a review and new perspectives. IEEE Trans Pattern Anal Mach Intell 35(8):1798\u20131828","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"2855_CR56","doi-asserted-by":"crossref","unstructured":"Lin TY, Maire M, Belongie S, Hays J, Perona P, Ramanan D, Doll\u00e1r P, Zitnick CL (2014) Microsoft coco: common objects in context. In Computer Vision\u2013ECCV 2014: 13th European Conference, Zurich, Switzerland, Proceedings, Part V 13 2014 (pp. 740\u2013755). Springer International Publishing","DOI":"10.1007\/978-3-319-10602-1_48"},{"key":"2855_CR57","unstructured":"Kingma DP, Ba J (2014) \u201cAdam: a method for stochastic optimization.\u201d arXiv preprint arXiv:1412.6980"},{"issue":"2","key":"2855_CR58","doi-asserted-by":"publisher","first-page":"523","DOI":"10.1007\/s00521-017-3086-5","volume":"31","author":"P Dey","year":"2019","unstructured":"Dey P, Gopal M, Pradhan P, Pal T (2019) On robustness of radial basis function network with input perturbation. Neural Comput Appl 31(2):523\u2013537","journal-title":"Neural Comput Appl"},{"key":"2855_CR59","unstructured":"Krogh A, Hertz J (1991) A simple weight decay can improve generalization. Adv Neural Inf Process Syst 4"},{"key":"2855_CR60","unstructured":"Geirhos R, Rubisch P, Michaelis C, Bethge M, Wichmann FA, Brendel W (2019) ImageNet-trained CNNs are biased towards texture; increasing shape bias improves accuracy and robustness. Proceedings of the 7th International Conference on Learning Representations, New Orleans, LA"},{"key":"2855_CR61","unstructured":"Ford N, Gilmer J, Carlini N, Cubuk D (2019) Adversarial examples are a natural consequence of test error in noise. arXiv preprint arXiv:1901.10513"},{"key":"2855_CR62","doi-asserted-by":"crossref","unstructured":"Rusak E, Schott L, Zimmermann RS, Bitterwolf J, Bringmann O, Bethge M, Brendel W (2020) A simple way to make neural networks robust against diverse image corruptions. In Computer Vision\u2013ECCV 2020: 16th European Conference, Glasgow, UK, Proceedings, Part III 16 2020 (pp. 53\u201369). Springer International Publishing","DOI":"10.1007\/978-3-030-58580-8_4"},{"issue":"4","key":"2855_CR63","doi-asserted-by":"publisher","first-page":"754","DOI":"10.1109\/TUFFC.2011.1868","volume":"58","author":"GF Pinton","year":"2011","unstructured":"Pinton GF, Trahey GE, Dahl JJ (2011) Sources of image degradation in fundamental and harmonic ultrasound imaging using nonlinear, full-wave simulations. IEEE Trans Ultrason Ferroelectr Freq Control 58(4):754\u2013765","journal-title":"IEEE Trans Ultrason Ferroelectr Freq Control"},{"key":"2855_CR64","unstructured":"Ioffe S, Szegedy C (2015) Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning (pp. 448\u2013456). Proceedings of Machine Learning Research"},{"key":"2855_CR65","unstructured":"Eykholt K, Evtimov I, Fernandes E, Li B, Rahmati A, Tram\u00e8r F, Prakash A, Kohno T, Song D (2018) Physical adversarial examples for object detectors. In Proceedings of the 12th USENIX Conference on Offensive Technologies (pp. 1\u20131)"},{"key":"2855_CR66","doi-asserted-by":"crossref","unstructured":"Redmon J, Farhadi A (2017) YOLO9000: better, faster, stronger. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 7263\u20137271)","DOI":"10.1109\/CVPR.2017.690"},{"issue":"1","key":"2855_CR67","doi-asserted-by":"publisher","first-page":"5","DOI":"10.1136\/eb-2012-100645","volume":"18","author":"M Power","year":"2013","unstructured":"Power M, Fell G, Wright M (2013) Principles for high-quality, high-value testing. BMJ Evid-Based Med 18(1):5\u201310","journal-title":"BMJ Evid-Based Med"},{"key":"2855_CR68","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu CY, Berg AC. SSD: Single shot multibox detector. In Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part I 14 2016 (pp. 21\u201337). Springer International Publishing","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"2855_CR69","doi-asserted-by":"publisher","first-page":"550","DOI":"10.3389\/fmed.2020.00550","volume":"7","author":"M Elgendi","year":"2020","unstructured":"Elgendi M, Nasir MU, Tang Q, Fletcher RR, Howard N, Menon C, Ward R, Parker W, Nicolaou S (2020) The performance of deep neural networks in differentiating chest X-rays of COVID-19 patients from other bacterial and viral pneumonias. Front Med 7:550","journal-title":"Front Med"},{"issue":"4","key":"2855_CR70","first-page":"042086","volume":"1529","author":"SS Sumit","year":"2020","unstructured":"Sumit SS, Watada J, Roy A, Rambli DRA (2020) In object detection deep learning methods, YOLO shows supremum to Mask R-CNN. J Phys: Conf Ser 1529(4):042086 (IOP Publishing)","journal-title":"J Phys: Conf Ser"},{"key":"2855_CR71","doi-asserted-by":"crossref","unstructured":"Liu J, Cai J, Chellamuthu K, Bagheri M, Lu L, Summers RM (2018) Cascaded coarse-to-fine convolutional neural networks for pericardial effusion localization and segmentation on CT scans. In 2018 IEEE 15th international symposium on biomedical imaging (ISBI 2018) (pp. 1092\u20131095)","DOI":"10.1109\/ISBI.2018.8363761"},{"issue":"5","key":"2855_CR72","doi-asserted-by":"publisher","first-page":"1045","DOI":"10.3390\/diagnostics12051045","volume":"12","author":"AJ Wilder-Smith","year":"2022","unstructured":"Wilder-Smith AJ, Yang S, Weikert T, Bremerich J, Haaf P, Segeroth M, Ebert LC, Sauter A, Sexauer R (2022) Automated detection, segmentation, and classification of pericardial effusions on chest ct using a deep convolutional neural network. Diagnostics 12(5):1045","journal-title":"Diagnostics"},{"key":"2855_CR73","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1016\/j.ccl.2017.07.005","volume":"35","author":"A Azarbal","year":"2017","unstructured":"Azarbal A, LeWinter MM (2017) Pericardial effusion. Cardiol Clin 35:515\u2013524. https:\/\/doi.org\/10.1016\/j.ccl.2017.07.005","journal-title":"Cardiol Clin"},{"key":"2855_CR74","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1016\/j.pcad.2016.12.009","volume":"59","author":"S Vakamudi","year":"2017","unstructured":"Vakamudi S, Ho N, Cremer PC (2017) Pericardial effusions: causes, diagnosis, and management. Prog Cardiovasc Dis 59:380\u2013388. https:\/\/doi.org\/10.1016\/j.pcad.2016.12.009","journal-title":"Prog Cardiovasc Dis"},{"issue":"3","key":"2855_CR75","doi-asserted-by":"publisher","first-page":"844","DOI":"10.1016\/j.athoracsur.2019.12.038","volume":"110","author":"HV Tran","year":"2020","unstructured":"Tran HV, Charles M, Garrett RC, Kempe PW, Howard CA, Khorgami Z (2020) Ten-year trends in traumatic cardiac injury and outcomes: a trauma registry analysis. Ann Thorac Surg 110(3):844\u2013848. https:\/\/doi.org\/10.1016\/j.athoracsur.2019.12.038","journal-title":"Ann Thorac Surg"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02855-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-023-02855-6\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-023-02855-6.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,7,12]],"date-time":"2023-07-12T03:27:35Z","timestamp":1689132455000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-023-02855-6"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,5,27]]},"references-count":75,"journal-issue":{"issue":"8","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["2855"],"URL":"https:\/\/doi.org\/10.1007\/s11517-023-02855-6","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,5,27]]},"assertion":[{"value":"8 February 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"17 May 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"27 May 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"This retrospective study was deemed Exempt from review by the Institutional Review Board of Boston Medical Center\/ Boston University Medical Campus.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Dr. Megan M. Leo and Dr. Ilkay Yildiz Potter declare that they have no financial interests. Dr. Ashkan Vaziri and Dr. James Feldman received the research grants funding this work as investigators.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}]}}