{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,22]],"date-time":"2026-09-22T20:41:52Z","timestamp":1790109712349,"version":"4.0.1"},"reference-count":29,"publisher":"Elsevier BV","issue":"3","license":[{"start":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T00:00:00Z","timestamp":1630454400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2021,9,1]],"date-time":"2021-09-01T00:00:00Z","timestamp":1630454400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2023,9,1]],"date-time":"2023-09-01T00:00:00Z","timestamp":1693526400000},"content-version":"vor","delay-in-days":730,"URL":"http:\/\/www.elsevier.com\/open-access\/userlicense\/1.0\/"}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","jvascsurg.org","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Journal of Vascular Surgery"],"published-print":{"date-parts":[[2021,9]]},"DOI":"10.1016\/j.jvs.2021.02.050","type":"journal-article","created":{"date-parts":[[2021,4,2]],"date-time":"2021-04-02T04:15:45Z","timestamp":1617336945000},"page":"988-996.e1","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":20,"title":["Computational methods to automate the initial interpretation of lower extremity arterial Doppler and duplex carotid ultrasound studies"],"prefix":"10.1016","volume":"74","author":[{"given":"Xiao","family":"Luo","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lena","family":"Ara","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Haoran","family":"Ding","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"David","family":"Rollins","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raghu","family":"Motaganahalli","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Alan P.","family":"Sawchuk","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.jvs.2021.02.050_bib1","doi-asserted-by":"crossref","first-page":"664","DOI":"10.1109\/TBME.2015.2468589","article-title":"Real-time patient-specific ECG classification by 1-D convolutional neural networks","volume":"63","author":"Kiranyaz","year":"2016","journal-title":"IEEE Trans Biomed Eng"},{"key":"10.1016\/j.jvs.2021.02.050_bib2","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1038\/s41591-018-0268-3","article-title":"Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network [published correction in Nat Med 2019;25:530","volume":"25","author":"Hannun","year":"2019","journal-title":"Nat Med"},{"key":"10.1016\/j.jvs.2021.02.050_bib3","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1097\/00001721-200011010-00005","article-title":"Plasma fibrinogen, haemostatic factors and prediction of peripheral arterial disease in the Edinburgh Artery Study","volume":"11","author":"Smith","year":"2000","journal-title":"Blood Coagul Fibrinolysis"},{"key":"10.1016\/j.jvs.2021.02.050_bib4","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1186\/s13047-019-0341-2","article-title":"Lower limb vascular assessment techniques of podiatrists in the United Kingdom: a national survey","volume":"12","author":"Tehan","year":"2019","journal-title":"J\u00a0Foot Ankle Res"},{"key":"10.1016\/j.jvs.2021.02.050_bib5","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1161\/CIRCOUTCOMES.113.000855","article-title":"Institutional differences in carotid artery duplex diagnostic criteria result in significant variability in classification of carotid artery stenoses and likely lead to disparities in care","volume":"7","author":"Arous","year":"2014","journal-title":"Circ Cardiovasc Qual Outcomes"},{"key":"10.1016\/j.jvs.2021.02.050_bib6","series-title":"Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstration, Baltimore, Maryland, June 2014","first-page":"55","article-title":"The Stanford CoreNPL natural language processing toolkit","author":"Manning","year":"2014"},{"key":"10.1016\/j.jvs.2021.02.050_bib7","series-title":"2016 Fourth International Conference on 3D Vision (3DV), Stanford, CA","first-page":"565","article-title":"V-net: fully convolutional neural networks for volumetric medical image segmentation","author":"Milletari","year":"2016"},{"key":"10.1016\/j.jvs.2021.02.050_bib8","series-title":"Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV","first-page":"2825","article-title":"CNN-RNN: Unified framework for multi-label image classification","author":"Wang","year":"2016"},{"key":"10.1016\/j.jvs.2021.02.050_bib9","doi-asserted-by":"crossref","first-page":"431","DOI":"10.1148\/radiol.2321030791","article-title":"Quantification of internal carotid artery stenosis with duplex US: comparative analysis of different flow velocity criteria","volume":"232","author":"Sabeti","year":"2004","journal-title":"Radiology"},{"key":"10.1016\/j.jvs.2021.02.050_bib10","doi-asserted-by":"crossref","first-page":"916","DOI":"10.1161\/STROKEAHA.111.636084","article-title":"Grading carotid stenosis using ultrasonic methods","volume":"43","author":"Von Reutern","year":"2012","journal-title":"Stroke"},{"key":"10.1016\/j.jvs.2021.02.050_bib11","doi-asserted-by":"crossref","first-page":"500","DOI":"10.1038\/s41568-018-0016-5","article-title":"Artificial intelligence in radiology","volume":"18","author":"Hosny","year":"2018","journal-title":"Nat Rev Cancer"},{"key":"10.1016\/j.jvs.2021.02.050_bib12","doi-asserted-by":"crossref","first-page":"540","DOI":"10.1055\/s-0038-1673383","article-title":"Artificial intelligence in radiology: current technology and future directions","volume":"22","author":"Syed","year":"2018","journal-title":"Semin Musculoskelet Radiol"},{"key":"10.1016\/j.jvs.2021.02.050_bib13","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.clinimag.2017.11.007","article-title":"Artificial intelligence and deep learning\u2014radiology's next frontier?","volume":"49","author":"Mayo","year":"2018","journal-title":"Clin Imaging"},{"key":"10.1016\/j.jvs.2021.02.050_bib14","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.ins.2017.04.012","article-title":"Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network","volume":"405","author":"Acharya","year":"2017","journal-title":"Inform Sci"},{"key":"10.1016\/j.jvs.2021.02.050_bib15","doi-asserted-by":"crossref","first-page":"536","DOI":"10.1001\/jama.295.5.536","article-title":"Does the clinical examination predict lower extremity peripheral arterial disease?","volume":"295","author":"Khan","year":"2006","journal-title":"JAMA"},{"key":"10.1016\/j.jvs.2021.02.050_bib16","doi-asserted-by":"crossref","first-page":"851","DOI":"10.2147\/TCRM.S107635","article-title":"The role of inflammation in cardiovascular diseases: the predictive value of neutrophil-lymphocyte ratio as a marker in peripheral arterial disease","volume":"12","author":"Paquissi","year":"2016","journal-title":"Ther Clin Risk Manag"},{"key":"10.1016\/j.jvs.2021.02.050_bib17","doi-asserted-by":"crossref","first-page":"88","DOI":"10.1016\/j.jelectrocard.2018.11.013","article-title":"A\u00a0deep neural network learning algorithm outperforms a conventional algorithm for emergency department electrocardiogram interpretation","volume":"52","author":"Smith","year":"2019","journal-title":"J\u00a0Electrocardiol"},{"key":"10.1016\/j.jvs.2021.02.050_bib18","doi-asserted-by":"crossref","first-page":"829","DOI":"10.1007\/s10016-005-7685-8","article-title":"Prediction of the exact degree of internal carotid artery stenosis using an artificial neural network based on duplex velocity measurements","volume":"19","author":"Mofidi","year":"2005","journal-title":"Ann Vasc Surg"},{"key":"10.1016\/j.jvs.2021.02.050_bib19","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1016\/j.compbiomed.2004.05.004","article-title":"Adaptive neuro-fuzzy inference systems for analysis of internal carotid arterial Doppler signals","volume":"35","author":"Derya Ubeyli","year":"2005","journal-title":"Comput Biol Med"},{"key":"10.1016\/j.jvs.2021.02.050_bib20","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1016\/j.artmed.2008.05.003","article-title":"Application of complex discrete wavelet transform in classification of Doppler signals using complex-valued artificial neural network","volume":"44","author":"Ceylan","year":"2008","journal-title":"Artif Intell Med"},{"key":"10.1016\/j.jvs.2021.02.050_bib21","first-page":"109","article-title":"Comparison of three arterial pulse waveform classification techniques","volume":"20","author":"Allen","year":"1996","journal-title":"J\u00a0Med Eng Technol"},{"key":"10.1016\/j.jvs.2021.02.050_bib22","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1088\/0967-3334\/16\/1\/003","article-title":"Prospective assessment of an artificial neural network for the detection of peripheral vascular disease from lower limb pulse waveforms","volume":"16","author":"Allen","year":"1995","journal-title":"Physiol Meas"},{"key":"10.1016\/j.jvs.2021.02.050_bib23","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1088\/0967-3334\/14\/1\/003","article-title":"Development of a neural network screening aid for diagnosing lower limb peripheral vascular disease from photoelectric plethysmography pulse waveforms","volume":"14","author":"Allen","year":"1993","journal-title":"Physiol Meas"},{"key":"10.1016\/j.jvs.2021.02.050_bib24","doi-asserted-by":"crossref","first-page":"1650","DOI":"10.1016\/j.rser.2015.07.068","article-title":"A\u00a0comprehensive overview on signal processing and artificial intelligence techniques applications in classification of power quality disturbances","volume":"51","author":"Khokhar","year":"2015","journal-title":"Renew Sustain Energy Rev"},{"key":"10.1016\/j.jvs.2021.02.050_bib25","author":"Sadler"},{"key":"10.1016\/j.jvs.2021.02.050_bib26","series-title":"Atrial Fibrillation: Basic Research and Clinical Applications","first-page":"147","article-title":"Digital signal processing and artificial intelligence methods for intracardial signal complexity evaluation","author":"Kremen","year":"2011"},{"key":"10.1016\/j.jvs.2021.02.050_bib27","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1016\/j.trsl.2017.10.010","article-title":"Digital image analysis in breast pathology\u2014from image processing techniques to artificial intelligence","volume":"194","author":"Robertson","year":"2018","journal-title":"Transl Res"},{"key":"10.1016\/j.jvs.2021.02.050_bib28","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1016\/j.jvcir.2019.03.004","article-title":"Applications of artificial intelligence algorithms in image processing","volume":"61","author":"Zhang","year":"2019","journal-title":"J\u00a0Vis Commun Image Rep"},{"key":"10.1016\/j.jvs.2021.02.050_bib29","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1109\/86.736154","article-title":"EMG pattern recognition based on artificial intelligence techniques","volume":"6","author":"Park","year":"1998","journal-title":"IEEE Trans Rehab Eng"}],"container-title":["Journal of Vascular Surgery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0741521421004730?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0741521421004730?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T20:33:40Z","timestamp":1760042020000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0741521421004730"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,9]]},"references-count":29,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2021,9]]}},"alternative-id":["S0741521421004730"],"URL":"https:\/\/doi.org\/10.1016\/j.jvs.2021.02.050","relation":{},"ISSN":["0741-5214"],"issn-type":[{"value":"0741-5214","type":"print"}],"subject":[],"published":{"date-parts":[[2021,9]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Computational methods to automate the initial interpretation of lower extremity arterial Doppler and duplex carotid ultrasound studies","name":"articletitle","label":"Article Title"},{"value":"Journal of Vascular Surgery","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.jvs.2021.02.050","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2021 Published by Elsevier Inc. on behalf of the Society for Vascular Surgery.","name":"copyright","label":"Copyright"}]}}