{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,19]],"date-time":"2025-12-19T22:05:37Z","timestamp":1766181937343,"version":"3.40.3"},"publisher-location":"Cham","reference-count":33,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783031167874"},{"type":"electronic","value":"9783031167881"}],"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\/978-3-031-16788-1_32","type":"book-chapter","created":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T20:35:56Z","timestamp":1663878956000},"page":"529-542","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Interpretable Prediction of\u00a0Pulmonary Hypertension in\u00a0Newborns Using Echocardiograms"],"prefix":"10.1007","author":[{"given":"Hanna","family":"Ragnarsdottir","sequence":"first","affiliation":[]},{"given":"Laura","family":"Manduchi","sequence":"additional","affiliation":[]},{"given":"Holger","family":"Michel","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8562-5680","authenticated-orcid":false,"given":"Fabian","family":"Laumer","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9230-6266","authenticated-orcid":false,"given":"Sven","family":"Wellmann","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9889-6348","authenticated-orcid":false,"given":"Ece","family":"Ozkan","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6004-7770","authenticated-orcid":false,"given":"Julia E.","family":"Vogt","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,9,20]]},"reference":[{"key":"32_CR1","unstructured":"Adebayo, J., Gilmer, J., Muelly, M., Goodfellow, I., Hardt, M., Kim, B.: Sanity checks for saliency maps. In: Proceedings of the 32nd International Conference on Neural Information Processing Systems, NIPS 2018, pp. 9525\u20139536. Curran Associates Inc., Red Hook (2018)"},{"issue":"1","key":"32_CR2","doi-asserted-by":"publisher","first-page":"113","DOI":"10.1161\/CIRCULATIONAHA.111.026591","volume":"125","author":"RJ Barst","year":"2012","unstructured":"Barst, R.J., McGoon, M.D., Elliott, C.G., Foreman, A.J., Miller, D.P., Ivy, D.D.: Survival in childhood pulmonary arterial hypertension. Circulation 125(1), 113\u2013122 (2012)","journal-title":"Circulation"},{"issue":"2","key":"32_CR3","doi-asserted-by":"publisher","first-page":"95","DOI":"10.1038\/s42256-019-0019-2","volume":"1","author":"GA Bello","year":"2019","unstructured":"Bello, G.A., et al.: Deep-learning cardiac motion analysis for human survival prediction. Nat. Mach. Intell. 1(2), 95\u2013104 (2019)","journal-title":"Nat. Mach. Intell."},{"issue":"S1","key":"32_CR4","doi-asserted-by":"publisher","first-page":"68","DOI":"10.1038\/s41390-018-0082-0","volume":"84","author":"WP de Boode","year":"2018","unstructured":"de Boode, W.P., et al.: Application of neonatologist performed echocardiography in the assessment and management of persistent pulmonary hypertension of the newborn. Pediatric Res. 84(S1), 68\u201377 (2018)","journal-title":"Pediatric Res."},{"key":"32_CR5","doi-asserted-by":"crossref","unstructured":"Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset, pp. 4724\u20134733 (2017)","DOI":"10.1109\/CVPR.2017.502"},{"issue":"134","key":"32_CR6","doi-asserted-by":"publisher","first-page":"488","DOI":"10.1183\/09059180.00007214","volume":"23","author":"P Corris","year":"2014","unstructured":"Corris, P., Degano, B.: Severe pulmonary arterial hypertension: treatment options and the bridge to transplantation. Eur. Resp. Rev. 23(134), 488\u2013497 (2014)","journal-title":"Eur. Resp. Rev."},{"issue":"1","key":"32_CR7","doi-asserted-by":"publisher","first-page":"19","DOI":"10.1038\/s41372-021-01009-6","volume":"42","author":"S Dasgupta","year":"2021","unstructured":"Dasgupta, S., Richardson, J.C., Aly, A.M., Jain, S.K.: Role of functional echocardiographic parameters in the diagnosis of bronchopulmonary dysplasia-associated pulmonary hypertension. J. Perinatol. 42(1), 19\u201330 (2021)","journal-title":"J. Perinatol."},{"issue":"2","key":"32_CR8","doi-asserted-by":"publisher","first-page":"381","DOI":"10.1148\/radiol.2016161315","volume":"283","author":"TJW Dawes","year":"2017","unstructured":"Dawes, T.J.W., et al.: Machine learning of three-dimensional right ventricular motion enables outcome prediction in pulmonary hypertension: a cardiac MR imaging study. Radiology 283(2), 381\u2013390 (2017)","journal-title":"Radiology"},{"key":"32_CR9","unstructured":"EL-Khuffash, A.: Neonatal echocardiography teaching manual (2014)"},{"key":"32_CR10","doi-asserted-by":"crossref","unstructured":"Feichtenhofer, C., Fan, H., Malik, J., He, K.: SlowFast networks for video recognition. In: 2019 IEEE\/CVF International Conference on Computer Vision (ICCV) (2019)","DOI":"10.1109\/ICCV.2019.00630"},{"issue":"7","key":"32_CR11","doi-asserted-by":"publisher","first-page":"615","DOI":"10.1164\/rccm.200811-1691OC","volume":"179","author":"MR Fisher","year":"2009","unstructured":"Fisher, M.R., et al.: Accuracy of doppler echocardiography in the hemodynamic assessment of pulmonary hypertension. Am. J. Resp. Crit. Care Med. 179(7), 615\u2013621 (2009)","journal-title":"Am. J. Resp. Crit. Care Med."},{"issue":"4","key":"32_CR12","doi-asserted-by":"publisher","first-page":"903","DOI":"10.1183\/13993003.01032-2015","volume":"46","author":"N Gali\u00e8","year":"2015","unstructured":"Gali\u00e8, N., et al.: 2015 ESC\/ERS guidelines for the diagnosis and treatment of pulmonary hypertension. Eur. Resp. J. 46(4), 903\u2013975 (2015)","journal-title":"Eur. Resp. J."},{"issue":"20","key":"32_CR13","doi-asserted-by":"publisher","first-page":"2551","DOI":"10.1016\/j.jacc.2017.03.575","volume":"69","author":"G Hansmann","year":"2017","unstructured":"Hansmann, G.: Pulmonary hypertension in infants, children, and young adults. J. Am. Coll. Cardiol. 69(20), 2551\u20132569 (2017)","journal-title":"J. Am. Coll. Cardiol."},{"key":"32_CR14","doi-asserted-by":"crossref","unstructured":"Hara, K., Kataoka, H., Satoh, Y.: Learning spatio-temporal features with 3D residual networks for action recognition, pp. 3154\u20133160 (2017)","DOI":"10.1109\/ICCVW.2017.373"},{"key":"32_CR15","doi-asserted-by":"crossref","unstructured":"Kaddoura, T., et al.: Acoustic diagnosis of pulmonary hypertension: automated speech- recognition-inspired classification algorithm outperforms physicians. Sci. Rep. 6(1) (2016)","DOI":"10.1038\/srep33182"},{"key":"32_CR16","doi-asserted-by":"crossref","unstructured":"Kindermans, P.-J., et al.: The (Un)reliability of saliency methods, pp. 267\u2013280. Springer International Publishing, Cham (2019)","DOI":"10.1007\/978-3-030-28954-6_14"},{"key":"32_CR17","unstructured":"Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)"},{"key":"32_CR18","doi-asserted-by":"crossref","unstructured":"Kusunose, K., Hirata, Y., Tsuji, T., Kotoku, J., Sata, M.: Deep learning to predict elevated pulmonary artery pressure in patients with suspected pulmonary hypertension using standard chest X-ray. Sci. Rep. 10(1) (2020)","DOI":"10.1038\/s41598-020-76359-w"},{"issue":"8","key":"32_CR19","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1016\/j.healun.2020.04.009","volume":"39","author":"JM Kwon","year":"2020","unstructured":"Kwon, J.M., et al.: Artificial intelligence for early prediction of pulmonary hypertension using electrocardiography. J. Heart Lung Transplant. 39(8), 805\u2013814 (2020)","journal-title":"J. Heart Lung Transplant."},{"issue":"3","key":"32_CR20","doi-asserted-by":"publisher","first-page":"233","DOI":"10.1093\/ehjci\/jev014","volume":"16","author":"R Lang","year":"2015","unstructured":"Lang, R., et al.: Recommendations for cardiac chamber quantification by echocardiography in adults: an update from the American Society of echocardiography and the European Association of cardiovascular imaging. Eur. Heart J. Cardiovasc. Imaging 16(3), 233\u201370 (2015)","journal-title":"Eur. Heart J. Cardiovasc. Imaging"},{"issue":"10","key":"32_CR21","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0224453","volume":"14","author":"A Leha","year":"2019","unstructured":"Leha, A., et al.: A machine learning approach for the prediction of pulmonary hypertension. PLOS ONE 14(10), e0224453 (2019)","journal-title":"PLOS ONE"},{"issue":"6","key":"32_CR22","doi-asserted-by":"publisher","first-page":"1379","DOI":"10.1007\/s00246-021-02622-0","volume":"42","author":"H Mori","year":"2021","unstructured":"Mori, H., Inai, K., Sugiyama, H., Muragaki, Y.: Diagnosing atrial septal defect from electrocardiogram with deep learning. Pediatric Cardiol. 42(6), 1379\u20131387 (2021)","journal-title":"Pediatric Cardiol."},{"issue":"12","key":"32_CR23","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2019-033084","volume":"9","author":"JR Ni","year":"2019","unstructured":"Ni, J.R., et al.: Diagnostic accuracy of transthoracic echocardiography for pulmonary hypertension: a systematic review and meta-analysis. BMJ Open 9(12), e033084 (2019)","journal-title":"BMJ Open"},{"issue":"138","key":"32_CR24","doi-asserted-by":"publisher","first-page":"642","DOI":"10.1183\/16000617.0062-2015","volume":"24","author":"S Rosenkranz","year":"2015","unstructured":"Rosenkranz, S., Preston, I.R.: Right heart catheterisation: best practice and pitfalls in pulmonary hypertension. Eur. Resp. Rev. 24(138), 642\u2013652 (2015)","journal-title":"Eur. Resp. Rev."},{"issue":"5","key":"32_CR25","first-page":"695","volume":"34","author":"M Schneider","year":"2018","unstructured":"Schneider, M., et al.: Multi-view approach for the diagnosis of pulmonary hypertension using transthoracic echocardiography. Int. J. Cardiovasc. Imaging 34(5), 695\u2013700 (2018)","journal-title":"Int. J. Cardiovasc. Imaging"},{"key":"32_CR26","doi-asserted-by":"crossref","unstructured":"Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., Batra, D.: Grad-CAM: visual explanations from deep networks via gradient-based localization. In: 2017 IEEE International Conference on Computer Vision, pp. 618\u2013626 (2017)","DOI":"10.1109\/ICCV.2017.74"},{"key":"32_CR27","unstructured":"Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.A.: Striving for simplicity: the all convolutional net. CoRR abs\/1412.6806 (2015)"},{"key":"32_CR28","doi-asserted-by":"publisher","first-page":"S79","DOI":"10.1097\/PCC.0b013e3181c76cdc","volume":"11","author":"RH Steinhorn","year":"2010","unstructured":"Steinhorn, R.H.: Neonatal pulmonary hypertension. Pediatric Crit. Care Med. 11, S79\u2013S84 (2010)","journal-title":"Pediatric Crit. Care Med."},{"key":"32_CR29","doi-asserted-by":"crossref","unstructured":"Tran, D., Wang, H., Torresani, L., Ray, J., LeCun, Y., Paluri, M.: A closer look at spatiotemporal convolutions for action recognition. In: 2018 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (2018)","DOI":"10.1109\/CVPR.2018.00675"},{"key":"32_CR30","doi-asserted-by":"crossref","unstructured":"Vainio, T., M\u00e4kel\u00e4, T., Savolainen, S., Kangasniemi, M.: Performance of a 3D convolutional neural network in the detection of hypoperfusion at CT pulmonary angiography in patients with chronic pulmonary embolism: a feasibility study. Eur. Radiol. Expe. 5(1) (2021)","DOI":"10.1186\/s41747-021-00235-z"},{"issue":"16","key":"32_CR31","doi-asserted-by":"publisher","first-page":"1623","DOI":"10.1161\/CIRCULATIONAHA.118.034338","volume":"138","author":"J Zhang","year":"2018","unstructured":"Zhang, J., et al.: Fully automated echocardiogram interpretation in clinical practice. Circulation 138(16), 1623\u20131635 (2018)","journal-title":"Circulation"},{"key":"32_CR32","doi-asserted-by":"crossref","unstructured":"Zhou, B., Khosla, A., Lapedriza, \u00c0., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2921\u20132929 (2016)","DOI":"10.1109\/CVPR.2016.319"},{"issue":"7","key":"32_CR33","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0236378","volume":"15","author":"XL Zou","year":"2020","unstructured":"Zou, X.L., et al.: A promising approach for screening pulmonary hypertension based on frontal chest radiographs using deep learning: a retrospective study. PLOS ONE 15(7), e0236378 (2020)","journal-title":"PLOS ONE"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-16788-1_32","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,9,22]],"date-time":"2022-09-22T20:42:40Z","timestamp":1663879360000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-16788-1_32"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031167874","9783031167881"],"references-count":33,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-16788-1_32","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":"20 September 2022","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"DAGM GCPR","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"DAGM German Conference on Pattern Recognition","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Konstanz","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Germany","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":"27 September 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"30 September 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"44","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"dagm2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/gcpr-vmv-2022.uni-konstanz.de\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"78","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":"37","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":"47% - 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":"2.6","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)"}}]}}