{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,4,26]],"date-time":"2025-04-26T04:48:55Z","timestamp":1745642935610,"version":"3.40.3"},"publisher-location":"Cham","reference-count":19,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031169014"},{"type":"electronic","value":"9783031169021"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-16902-1_8","type":"book-chapter","created":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T18:06:10Z","timestamp":1663351570000},"page":"75-85","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["AI-Enabled Assessment of\u00a0Cardiac Systolic and\u00a0Diastolic Function from\u00a0Echocardiography"],"prefix":"10.1007","author":[{"given":"Esther","family":"Puyol-Ant\u00f3n","sequence":"first","affiliation":[]},{"given":"Bram","family":"Ruijsink","sequence":"additional","affiliation":[]},{"given":"Baldeep S.","family":"Sidhu","sequence":"additional","affiliation":[]},{"given":"Justin","family":"Gould","sequence":"additional","affiliation":[]},{"given":"Bradley","family":"Porter","sequence":"additional","affiliation":[]},{"given":"Mark K.","family":"Elliott","sequence":"additional","affiliation":[]},{"given":"Vishal","family":"Mehta","sequence":"additional","affiliation":[]},{"given":"Haotian","family":"Gu","sequence":"additional","affiliation":[]},{"given":"Christopher A.","family":"Rinaldi","sequence":"additional","affiliation":[]},{"given":"Martin","family":"cowie","sequence":"additional","affiliation":[]},{"given":"Phil","family":"Chowienczyk","sequence":"additional","affiliation":[]},{"given":"Reza","family":"Razavi","sequence":"additional","affiliation":[]},{"given":"Andrew P.","family":"King","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,15]]},"reference":[{"issue":"9","key":"8_CR1","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCIMAGING.119.009303","volume":"12","author":"FM Asch","year":"2019","unstructured":"Asch, F.M., Poilvert, N., et al.: Automated echocardiographic quantification of left ventricular ejection fraction without volume measurements using a machine learning algorithm mimicking a human expert. Circ. Cardiovasc. Imaging 12(9), e009303 (2019)","journal-title":"Circ. Cardiovasc. Imaging"},{"issue":"3","key":"8_CR2","first-page":"189","volume":"20","author":"SL Bacharach","year":"1979","unstructured":"Bacharach, S.L., Green, M.V., Borer, J.S., et al.: Left-ventricular peak ejection rate, filling rate, and ejection fraction-frame rate requirements at rest and exercise: concise communication. J. Nucl. Med. Official Publ. Soc. Nucl. Med. 20(3), 189\u2013193 (1979)","journal-title":"J. Nucl. Med. Official Publ. Soc. Nucl. Med."},{"issue":"3","key":"8_CR3","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1145\/1531326.1531330","volume":"28","author":"C Barnes","year":"2009","unstructured":"Barnes, C., Shechtman, E., Finkelstein, A., Goldman, D.B.: PatchMatch: a randomized correspondence algorithm for structural image editing. ACM Trans. Graph. 28(3), 24 (2009)","journal-title":"ACM Trans. Graph."},{"issue":"8476","key":"8_CR4","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1016\/S0140-6736(86)90837-8","volume":"327","author":"JM Bland","year":"1986","unstructured":"Bland, J.M., Altman, D.: Statistical methods for assessing agreement between two methods of clinical measurement. lancet 327(8476), 307\u2013310 (1986)","journal-title":"lancet"},{"issue":"1","key":"8_CR5","doi-asserted-by":"publisher","DOI":"10.1161\/CIRCHEARTFAILURE.116.003529","volume":"10","author":"H Burnett","year":"2017","unstructured":"Burnett, H., Earley, A., Voors, A.A., et al.: Thirty years of evidence on the efficacy of drug treatments for chronic heart failure with reduced ejection fraction. Circ. Heart Fail. 10(1), e003529 (2017)","journal-title":"Circ. Heart Fail."},{"issue":"4","key":"8_CR6","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1016\/j.tjem.2018.09.001","volume":"18","author":"N\u00d6 Do\u011fan","year":"2018","unstructured":"Do\u011fan, N.\u00d6.: Bland-Altman analysis: a paradigm to understand correlation and agreement. Turk. J. Emerg. Med. 18(4), 139\u2013141 (2018)","journal-title":"Turk. J. Emerg. Med."},{"issue":"4","key":"8_CR7","doi-asserted-by":"publisher","first-page":"760","DOI":"10.1161\/01.CIR.60.4.760","volume":"60","author":"E Folland","year":"1979","unstructured":"Folland, E., Parisi, A., Moynihan, P., Jones, D.R., et al.: Assessment of left ventricular ejection fraction and volumes by real-time, two-dimensional echocardiography. a comparison of cineangiographic and radionuclide techniques. Circulation 60(4), 760\u2013766 (1979)","journal-title":"Circulation"},{"issue":"21","key":"8_CR8","doi-asserted-by":"publisher","first-page":"1897","DOI":"10.1016\/j.jacc.2012.01.046","volume":"59","author":"J Greupner","year":"2012","unstructured":"Greupner, J., Zimmermann, E., Grohmann, A., et al.: Head-to-head comparison of left ventricular function assessment with 64-row computed tomography, biplane left cineventriculography, and both 2-and 3-dimensional transthoracic echocardiography: comparison with magnetic resonance imaging as the reference standard. J. Am. Coll. Cardiol. 59(21), 1897\u20131907 (2012)","journal-title":"J. Am. Coll. Cardiol."},{"issue":"1","key":"8_CR9","doi-asserted-by":"publisher","first-page":"52","DOI":"10.1016\/j.jcmg.2018.08.037","volume":"12","author":"H Gu","year":"2019","unstructured":"Gu, H., Saeed, S., Boguslavskyi, A., et al.: First-phase ejection fraction is a powerful predictor of adverse events in asymptomatic patients with aortic stenosis and preserved total ejection fraction. JACC Cardiovasc. Imaging 12(1), 52\u201363 (2019)","journal-title":"JACC Cardiovasc. Imaging"},{"issue":"12","key":"8_CR10","doi-asserted-by":"publisher","first-page":"2275","DOI":"10.1016\/j.jcmg.2021.05.007","volume":"14","author":"H Gu","year":"2021","unstructured":"Gu, H., Sidhu, B.S., Fang, L., et al.: First-phase ejection fraction predicts response to cardiac resynchronization therapy and adverse outcomes. JACC Cardiovasc. Imaging 14(12), 2275\u20132285 (2021)","journal-title":"JACC Cardiovasc. Imaging"},{"issue":"2","key":"8_CR11","doi-asserted-by":"publisher","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","volume":"18","author":"F Isensee","year":"2021","unstructured":"Isensee, F., Jaeger, P.F., Kohl, S.A., et al.: nnU-Net: a self-configuring method for deep learning-based biomedical image segmentation. Nat. Methods 18(2), 203\u2013211 (2021)","journal-title":"Nat. Methods"},{"issue":"9","key":"8_CR12","doi-asserted-by":"publisher","first-page":"2198","DOI":"10.1109\/TMI.2019.2900516","volume":"38","author":"S Leclerc","year":"2019","unstructured":"Leclerc, S., Smistad, E., Pedrosa, J., et al.: Deep learning for segmentation using an open large-scale dataset in 2D echocardiography. IEEE Trans. Med. Imaging 38(9), 2198\u20132210 (2019)","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"7802","key":"8_CR13","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1038\/s41586-020-2145-8","volume":"580","author":"D Ouyang","year":"2020","unstructured":"Ouyang, D., He, B., Ghorbani, A., et al.: Video-based AI for beat-to-beat assessment of cardiac function. Nature 580(7802), 252\u2013256 (2020)","journal-title":"Nature"},{"issue":"27","key":"8_CR14","doi-asserted-by":"publisher","first-page":"2129","DOI":"10.1093\/eurheartj\/ehw128","volume":"37","author":"P Ponikowski","year":"2016","unstructured":"Ponikowski, P., Voors, A.A., Anker, S.D., Bueno, H., et al.: 2016 ESC guidelines for the diagnosis and treatment of acute and chronic heart failure: the task force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (esc) developed with the special contribution of the Heart Failure Association (HFA) of the ESC. Eur. Heart J. 37(27), 2129\u20132200 (2016)","journal-title":"Eur. Heart J."},{"issue":"1","key":"8_CR15","doi-asserted-by":"publisher","DOI":"10.1136\/openhrt-2015-000388","volume":"3","author":"M Rigolli","year":"2016","unstructured":"Rigolli, M., Anandabaskaran, S., Christiansen, J.P., Whalley, G.A.: Bias associated with left ventricular quantification by multimodality imaging: a systematic review and meta-analysis. Open Heart 3(1), e000388 (2016)","journal-title":"Open Heart"},{"issue":"3","key":"8_CR16","doi-asserted-by":"publisher","first-page":"543","DOI":"10.1161\/01.CIR.71.3.543","volume":"71","author":"R Rokey","year":"1985","unstructured":"Rokey, R., Kuo, L., Zoghbi, W.A., Limacher, M., Qui\u00f1ones, M.A.: Determination of parameters of left ventricular diastolic filling with pulsed Doppler echocardiography: comparison with cineangiography. Circulation 71(3), 543\u2013550 (1985)","journal-title":"Circulation"},{"issue":"3","key":"8_CR17","first-page":"684","volume":"13","author":"B Ruijsink","year":"2020","unstructured":"Ruijsink, B., Puyol-Ant\u00f3n, E., Oksuz, I., et al.: Fully automated, quality-controlled cardiac analysis from CMR: validation and large-scale application to characterize cardiac function. Cardiovasc. Imaging 13(3), 684\u2013695 (2020)","journal-title":"Cardiovasc. Imaging"},{"issue":"1","key":"8_CR18","doi-asserted-by":"publisher","first-page":"e46","DOI":"10.1016\/S2589-7500(21)00235-1","volume":"4","author":"J Tromp","year":"2022","unstructured":"Tromp, J., Seekings, P.J., Hung, C.L., Iversen, M.B., et al.: Automated interpretation of systolic and diastolic function on the echocardiogram: a multicohort study. Lancet Digit Health 4(1), e46\u2013e54 (2022)","journal-title":"Lancet Digit Health"},{"issue":"16","key":"8_CR19","doi-asserted-by":"publisher","first-page":"1623","DOI":"10.1161\/CIRCULATIONAHA.118.034338","volume":"138","author":"J Zhang","year":"2018","unstructured":"Zhang, J., Gajjala, S., Agrawal, P., et al.: Fully automated echocardiogram interpretation in clinical practice: feasibility and diagnostic accuracy. Circulation 138(16), 1623\u20131635 (2018)","journal-title":"Circulation"}],"container-title":["Lecture Notes in Computer Science","Simplifying Medical Ultrasound"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16902-1_8","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,16]],"date-time":"2022-09-16T18:06:48Z","timestamp":1663351608000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16902-1_8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031169014","9783031169021"],"references-count":19,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16902-1_8","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"15 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ASMUS","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Workshop on Advances in Simplifying Medical Ultrasound","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Singapore","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"asmus2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/miccai-ultrasound.github.io\/#\/asmus22","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"EquinOCS","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"23","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"18","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"78% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}