{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,25]],"date-time":"2026-09-25T05:13:03Z","timestamp":1790313183579,"version":"4.1.0"},"reference-count":38,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T00:00:00Z","timestamp":1759276800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2025,6,1]],"date-time":"2025-06-01T00:00:00Z","timestamp":1748736000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100010665","name":"H2020 Marie Sk\u0142odowska-Curie Actions","doi-asserted-by":"publisher","award":["EP\/Z002206\/1"],"award-info":[{"award-number":["EP\/Z002206\/1"]}],"id":[{"id":"10.13039\/100010665","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014013","name":"UK Research and Innovation","doi-asserted-by":"publisher","award":["MR\/U506710\/1"],"award-info":[{"award-number":["MR\/U506710\/1"]}],"id":[{"id":"10.13039\/100014013","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100014013","name":"UK Research and Innovation","doi-asserted-by":"publisher","award":["MR\/V023799\/1"],"award-info":[{"award-number":["MR\/V023799\/1"]}],"id":[{"id":"10.13039\/100014013","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100017325","name":"Engineering Research Centers","doi-asserted-by":"publisher","award":["101005122"],"award-info":[{"award-number":["101005122"]}],"id":[{"id":"10.13039\/100017325","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003009","name":"Science and Technology Development Fund","doi-asserted-by":"publisher","award":["AZ2023-ZH-03"],"award-info":[{"award-number":["AZ2023-ZH-03"]}],"id":[{"id":"10.13039\/501100003009","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100010661","name":"Horizon 2020 Framework Programme","doi-asserted-by":"publisher","award":["952172"],"award-info":[{"award-number":["952172"]}],"id":[{"id":"10.13039\/100010661","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100013342","name":"NIHR Imperial Biomedical Research Centre","doi-asserted-by":"publisher","award":["RDA01"],"award-info":[{"award-number":["RDA01"]}],"id":[{"id":"10.13039\/501100013342","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82202139"],"award-info":[{"award-number":["82202139"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["82071880"],"award-info":[{"award-number":["82071880"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100007065","name":"Nvidia","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100007065","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000265","name":"Medical Research Council","doi-asserted-by":"publisher","award":["MC\/PC\/21013"],"award-info":[{"award-number":["MC\/PC\/21013"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000265","name":"Medical Research Council","doi-asserted-by":"publisher","award":["IEC\\\\NSFC\\\\211235"],"award-info":[{"award-number":["IEC\\\\NSFC\\\\211235"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100028897","name":"Wellcome Leap","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100028897","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100008349","name":"Boehringer Ingelheim","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100008349","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["clinicalkey.com","clinicalkey.com.au","clinicalkey.es","clinicalkey.fr","clinicalkey.jp","internationaljournalofcardiology.com","elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Cardiology"],"published-print":{"date-parts":[[2025,10]]},"DOI":"10.1016\/j.ijcard.2025.133457","type":"journal-article","created":{"date-parts":[[2025,5,30]],"date-time":"2025-05-30T20:42:13Z","timestamp":1748637733000},"page":"133457","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":4,"special_numbering":"C","title":["Machine learning\u2013based hemodynamics quantitative assessment of pulmonary circulation using computed tomographic pulmonary angiography"],"prefix":"10.1016","volume":"437","author":[{"given":"Hongyan","family":"Xie","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiayi","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guang","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yunshan","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jialin","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lei","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhonghua","family":"Sun","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhaoying","family":"Wen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Senchun","family":"Chai","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dongting","family":"Liu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.ijcard.2025.133457_bb0005","doi-asserted-by":"crossref","first-page":"2361","DOI":"10.1056\/NEJMra2000348","article-title":"Pulmonary arterial hypertension","volume":"385","author":"Hassoun","year":"2021","journal-title":"N. Engl. J. Med."},{"key":"10.1016\/j.ijcard.2025.133457_bb0010","doi-asserted-by":"crossref","first-page":"306","DOI":"10.1016\/S2213-2600(15)00543-3","article-title":"A global view of pulmonary hypertension","volume":"4","author":"Hoeper","year":"2016","journal-title":"Lancet Respir. Med."},{"key":"10.1016\/j.ijcard.2025.133457_bb0015","doi-asserted-by":"crossref","first-page":"1023","DOI":"10.1164\/rccm.200510-1668OC","article-title":"Pulmonary arterial hypertension in France: results from a national registry","volume":"173","author":"Humbert","year":"2006","journal-title":"Am. J. Respir. Crit. Care Med."},{"issue":"2025","key":"10.1016\/j.ijcard.2025.133457_bb0020","first-page":"69","article-title":"Pulmonary arterial hypertension collaborators. Global, regional, and national burden of pulmonary arterial hypertension, 1990-2021: a systematic analysis for the global burden of disease study 2021","volume":"13","author":"GBD","year":"2021","journal-title":"Lancet Respir. Med."},{"key":"10.1016\/j.ijcard.2025.133457_bb0025","doi-asserted-by":"crossref","DOI":"10.1161\/JAHA.121.025143","article-title":"Clinical characteristics of patients undergoing right heart catheterizations in community hospitals","volume":"11","author":"Jansen","year":"2022","journal-title":"J. Am. Heart Assoc."},{"issue":"2022","key":"10.1016\/j.ijcard.2025.133457_bb0030","first-page":"3618","article-title":"ESC\/ERS guidelines for the diagnosis and treatment of pulmonary hypertension","volume":"43","author":"Humbert","year":"2022","journal-title":"Eur. Respir. J."},{"key":"10.1016\/j.ijcard.2025.133457_bb0035","doi-asserted-by":"crossref","first-page":"2546","DOI":"10.1016\/j.jacc.2006.07.061","article-title":"Complications of right heart catheterization procedures in patients with pulmonary hypertension in experienced centers","volume":"48","author":"Hoeper","year":"2006","journal-title":"J. Am. Coll. Cardiol."},{"key":"10.1016\/j.ijcard.2025.133457_bb0040","doi-asserted-by":"crossref","first-page":"615","DOI":"10.1164\/rccm.200811-1691OC","article-title":"Accuracy of doppler echocardiography in the hemodynamic assessment of pulmonary hypertension","volume":"179","author":"Fisher","year":"2009","journal-title":"Am. J. Respir. Crit. Care Med."},{"key":"10.1016\/j.ijcard.2025.133457_bb0045","doi-asserted-by":"crossref","first-page":"G11","DOI":"10.1530\/ERP-17-0071","article-title":"Echocardiographic assessment of pulmonary hypertension: a guideline protocol from the British Society of Echocardiography","volume":"5","author":"Augustine","year":"2018","journal-title":"Echo Res. Pract."},{"key":"10.1016\/j.ijcard.2025.133457_bb0050","doi-asserted-by":"crossref","first-page":"421","DOI":"10.3389\/fped.2019.00421","article-title":"Comparison of pulmonary artery pressure measurement with doppler echocardiography or with right heart catheterization in patients with congenital heart disease","volume":"7","author":"Yin","year":"2019","journal-title":"Front. Pediatr."},{"key":"10.1016\/j.ijcard.2025.133457_bb0055","doi-asserted-by":"crossref","DOI":"10.1097\/MD.0000000000000256","article-title":"CT-base pulmonary artery measurement in the detection of pulmonary hypertension: a meta-analysis and systematic review","volume":"93","author":"Shen","year":"2014","journal-title":"Medicine (Baltimore)"},{"key":"10.1016\/j.ijcard.2025.133457_bb0060","article-title":"Efficacy of computed tomography in diagnosing pulmonary hypertension: A systematic review and meta-analysis","volume":"9","author":"Chen","year":"2022","journal-title":"Front. Cardiovasc. Med."},{"key":"10.1016\/j.ijcard.2025.133457_bb0065","doi-asserted-by":"crossref","first-page":"4918","DOI":"10.1007\/s00330-020-06846-1","article-title":"Diagnostic accuracy of CT pulmonary angiography in suspected pulmonary hypertension","volume":"30","author":"Swift","year":"2020","journal-title":"Eur. Radiol."},{"key":"10.1016\/j.ijcard.2025.133457_bb0070","doi-asserted-by":"crossref","DOI":"10.1016\/j.ejro.2021.100384","article-title":"Noninvasive prediction of pulmonary hemodynamics in chronic thromboembolic pulmonary hypertension by electrocardiogram-gated computed tomography","volume":"8","author":"Roller","year":"2021","journal-title":"Eur. J. Radiol. Open"},{"key":"10.1016\/j.ijcard.2025.133457_bb0075","doi-asserted-by":"crossref","first-page":"2401128","DOI":"10.1183\/13993003.01128-2024","article-title":"Emerging multimodality imaging techniques for the pulmonary circulation","volume":"64","author":"Rajagopal","year":"2024","journal-title":"Eur. Respir. J."},{"key":"10.1016\/j.ijcard.2025.133457_bb0080","doi-asserted-by":"crossref","first-page":"85","DOI":"10.3390\/medicina61010085","article-title":"A comprehensive review of artificial intelligence (AI) applications in pulmonary hypertension (PH)","volume":"vol. 61","author":"Attaripour Esfahani","year":"2025","journal-title":"Medicina (Kaunas, Lithuania)"},{"key":"10.1016\/j.ijcard.2025.133457_bb0085","doi-asserted-by":"crossref","first-page":"00229","DOI":"10.1183\/23120541.00229-2023","article-title":"Machine learning to differentiate pulmonary hypertension due to left heart disease from pulmonary arterial hypertension","volume":"9","author":"Swinnen","year":"2023","journal-title":"ERJ Open Res."},{"key":"10.1016\/j.ijcard.2025.133457_bb0090","first-page":"569","article-title":"Machine learning assisted feature identification and prediction of hemodynamic endpoints using computed tomography in patients with CTEPH","volume":"40","author":"Gawlitza","year":"2024","journal-title":"Int. J. Card. Imaging"},{"key":"10.1016\/j.ijcard.2025.133457_bb0095","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0300716","article-title":"Development and web deployment of prediction model for pulmonary arterial pressure in chronic thromboembolic pulmonary hypertension using machine learning","volume":"19","author":"Matsunaga","year":"2024","journal-title":"PLoS One"},{"key":"10.1016\/j.ijcard.2025.133457_bb0100","doi-asserted-by":"crossref","first-page":"203","DOI":"10.1038\/s41592-020-01008-z","article-title":"nnU-net: a self-configuring method for deep learning-based biomedical image segmentation","volume":"18","author":"Isensee","year":"2021","journal-title":"Nat. Methods"},{"key":"10.1016\/j.ijcard.2025.133457_bb0105","doi-asserted-by":"crossref","first-page":"e104","DOI":"10.1158\/0008-5472.CAN-17-0339","article-title":"Computational radiomics system to decode the radiographic phenotype","volume":"77","author":"van Griethuysen","year":"2017","journal-title":"Cancer Res."},{"key":"10.1016\/j.ijcard.2025.133457_bb0110","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1097\/RLI.0000000000000518","article-title":"Impact of machinelearning with multiparametric magnetic resonance imaging of the breast for early prediction of response to neoadjuvant chemotherapy and survival outcomes in breast cancer patients","volume":"54","author":"Tahmassebi","year":"2019","journal-title":"Investig. Radiol."},{"key":"10.1016\/j.ijcard.2025.133457_bb0115","doi-asserted-by":"crossref","first-page":"286","DOI":"10.1097\/RLI.0000000000000441","article-title":"Treatment effect of balloon pulmonary angioplasty in chronic thromboembolic pulmonary hypertension quantified by automatic comparative imaging in computed tomography pulmonary angiography","volume":"53","author":"Zhai","year":"2018","journal-title":"Investig. Radiol."},{"key":"10.1016\/j.ijcard.2025.133457_bb0120","doi-asserted-by":"crossref","DOI":"10.3389\/fcvm.2022.835655","article-title":"Pulmonary blood volume measured by dual-energy computed tomography can help distinguish patients with pulmonary hypertension","volume":"9","author":"Rezaei-Kalantari","year":"2022","journal-title":"Front. Cardiovasc. Med."},{"key":"10.1016\/j.ijcard.2025.133457_bb0125","doi-asserted-by":"crossref","first-page":"295","DOI":"10.1016\/j.ijcard.2011.07.019","article-title":"Cardiovascular parameters of computed tomographic pulmonary angiography to assess pulmonary vascular resistance in patients with chronic thromboembolic pulmonary hypertension","volume":"164","author":"Liu","year":"2013","journal-title":"Int. J. Cardiol."},{"key":"10.1016\/j.ijcard.2025.133457_bb0130","doi-asserted-by":"crossref","first-page":"5222","DOI":"10.1007\/s00330-022-08663-0","article-title":"Diagnostic accuracy of automated 3D volumetry of cardiac chambers by CT pulmonary angiography for identification of pulmonary hypertension due to left heart disease","volume":"32","author":"Melzig","year":"2022","journal-title":"Eur. Radiol."},{"key":"10.1016\/j.ijcard.2025.133457_bb0135","doi-asserted-by":"crossref","first-page":"2220","DOI":"10.1016\/j.chest.2021.06.069","article-title":"Quantification of arterial andvenous morphologic markers in pulmonary arterial hypertension using CT imaging","volume":"160","author":"Rahaghi","year":"2021","journal-title":"Chest"},{"key":"10.1016\/j.ijcard.2025.133457_bb0140","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.3390\/jcm12041297","article-title":"Machine learning based on computed tomography pulmonary angiography in evaluating pulmonary artery pressure in patients with pulmonary hypertension","volume":"12","author":"Zhang","year":"2023","journal-title":"J. Clin. Med."},{"key":"10.1016\/j.ijcard.2025.133457_bb0145","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1136\/thoraxjnl-2014-206088","article-title":"CT features of pulmonary arterial hypertension and its major subtypes: a systematic CT evaluation of 292 patients from the ASPIRE registry","volume":"70","author":"Rajaram","year":"2015","journal-title":"Thorax"},{"key":"10.1016\/j.ijcard.2025.133457_bb0150","doi-asserted-by":"crossref","first-page":"323","DOI":"10.3390\/jimaging10120323","article-title":"Current role of CT pulmonary angiography in pulmonary embolism: A state-of-the-art review","volume":"10","author":"Diaz-Lorenzo","year":"2024","journal-title":"J. Imag."},{"key":"10.1016\/j.ijcard.2025.133457_bb0155","doi-asserted-by":"crossref","first-page":"5050","DOI":"10.1038\/s41598-025-85318-2","article-title":"Bioinformatics analysis of coronary microvascular dysfunction in rats based on single-cell RNA sequencing","volume":"15","author":"Li","year":"2025","journal-title":"Sci. Rep."},{"key":"10.1016\/j.ijcard.2025.133457_bb0160","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pcbi.1012231","article-title":"Image2Flow: A proof-of-concept hybrid image and graph convolutional neural network for rapid patient-specific pulmonary artery segmentation and CFD flow field calculation from 3D cardiac MRI data","volume":"20","author":"Yao","year":"2024","journal-title":"PLoS Comput. Biol."},{"key":"10.1016\/j.ijcard.2025.133457_bb0165","doi-asserted-by":"crossref","first-page":"942","DOI":"10.1093\/eurheartj\/ehv512","article-title":"Left ventricular heart failure and pulmonary hypertension","volume":"37","author":"Rosenkranz","year":"2016","journal-title":"Eur. Heart J."},{"key":"10.1016\/j.ijcard.2025.133457_bb0170","doi-asserted-by":"crossref","first-page":"e500","DOI":"10.1016\/j.crad.2022.03.009","article-title":"Diagnostic accuracy of an automated artificial intelligence derived right ventricular to left ventricular diameter ratio tool on CT pulmonary angiography to predict pulmonary hypertension at right heart catheterisation","volume":"77","author":"Charters","year":"2022","journal-title":"Clin. Radiol."},{"key":"10.1016\/j.ijcard.2025.133457_bb0175","doi-asserted-by":"crossref","first-page":"H156","DOI":"10.1152\/ajpheart.00485.2019","article-title":"Right ventricular function correlates of right atrial strain in pulmonary hypertension: a combined cardiac magnetic resonance and conductance catheter study","volume":"318","author":"Tello","year":"2020","journal-title":"Am. J. Physiol. Heart Circ. Physiol."},{"key":"10.1016\/j.ijcard.2025.133457_bb0180","article-title":"Assessment of right ventricular remodeling in chronic thromboembolic pulmonary hypertension by 2D-speckle tracking echocardiography: A comparison with cardiac magnetic resonance","volume":"9","author":"Wang","year":"2022","journal-title":"Front. Cardiovasc. Med."},{"key":"10.1016\/j.ijcard.2025.133457_bb0185","doi-asserted-by":"crossref","first-page":"961","DOI":"10.1007\/s11604-022-01272-3","article-title":"Prediction of pulmonary hypertension using central-to-peripheral pulmonary vascular volume ratio on three-dimensional cardiothoracic CT in patients with congenital heart disease","volume":"40","author":"Goo","year":"2022","journal-title":"Jpn. J. Radiol."},{"key":"10.1016\/j.ijcard.2025.133457_bb0190","doi-asserted-by":"crossref","DOI":"10.3389\/fcvm.2022.983859","article-title":"Fully automatic cardiac four chamber and great vessel segmentation on CT pulmonary angiography using deep learning","volume":"9","author":"Sharkey","year":"2022","journal-title":"Front. Cardiovasc. Med."}],"container-title":["International Journal of Cardiology"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167527325005005?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0167527325005005?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T10:56:46Z","timestamp":1762253806000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0167527325005005"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,10]]},"references-count":38,"alternative-id":["S0167527325005005"],"URL":"https:\/\/doi.org\/10.1016\/j.ijcard.2025.133457","relation":{},"ISSN":["0167-5273"],"issn-type":[{"value":"0167-5273","type":"print"}],"subject":[],"published":{"date-parts":[[2025,10]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Machine learning\u2013based hemodynamics quantitative assessment of pulmonary circulation using computed tomographic pulmonary angiography","name":"articletitle","label":"Article Title"},{"value":"International Journal of Cardiology","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.ijcard.2025.133457","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2025 The Authors. Published by Elsevier B.V.","name":"copyright","label":"Copyright"}],"article-number":"133457"}}