{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T14:55:40Z","timestamp":1777042540350,"version":"3.51.4"},"reference-count":46,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,4,23]],"date-time":"2026-04-23T00:00:00Z","timestamp":1776902400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Biomedical Signal Processing and Control"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.bspc.2026.110363","type":"journal-article","created":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T09:57:41Z","timestamp":1777024661000},"page":"110363","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["CardioSmartAssist: A customisable AI framework for echocardiography-based cardiac assessment"],"prefix":"10.1016","volume":"122","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-1519-5258","authenticated-orcid":false,"given":"Francesca Giada","family":"Antonaci","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0002-1860-8543","authenticated-orcid":false,"given":"Piera","family":"Ciaramella","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2359-4409","authenticated-orcid":false,"given":"Giorgia","family":"Marullo","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8407-0660","authenticated-orcid":false,"given":"Luca","family":"Ulrich","sequence":"additional","affiliation":[]},{"given":"Vincenza","family":"Papa","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4554-1110","authenticated-orcid":false,"given":"Walter Grosso","family":"Marra","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0672-6278","authenticated-orcid":false,"given":"Alessandro","family":"Depaoli","sequence":"additional","affiliation":[]},{"given":"Riccardo","family":"Miraglia","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5097-7344","authenticated-orcid":false,"given":"Sandro","family":"Moos","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8910-7020","authenticated-orcid":false,"given":"Enrico","family":"Vezzetti","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.bspc.2026.110363_b1","article-title":"What is the effectiveness of systematic population-level screening programmes for reducing the burden of cardiovascular diseases?","author":"Eriksen","year":"2021"},{"issue":"7802","key":"10.1016\/j.bspc.2026.110363_b2","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1038\/s41586-020-2145-8","article-title":"Video-based AI for beat-to-beat assessment of cardiac function","volume":"580","author":"Ouyang","year":"2020","journal-title":"Nature"},{"key":"10.1016\/j.bspc.2026.110363_b3","series-title":"2024 IEEE\/CVF Conference on Computer Vision and Pattern Recognition","first-page":"9622","article-title":"MemSAM: Taming segment anything model for echocardiography video segmentation","author":"Deng","year":"2024"},{"issue":"1","key":"10.1016\/j.bspc.2026.110363_b4","doi-asserted-by":"crossref","first-page":"59","DOI":"10.1038\/s41746-018-0065-x","article-title":"Deep echocardiography: data-efficient supervised and semi-supervised deep learning towards automated diagnosis of cardiac disease","volume":"1","author":"Madani","year":"2018","journal-title":"Npj Digit. Med."},{"issue":"1","key":"10.1016\/j.bspc.2026.110363_b5","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1016\/j.jacc.2012.09.035","article-title":"Reproducibility of echocardiographic techniques for sequential assessment of left ventricular ejection fraction and volumes","volume":"61","author":"Thavendiranathan","year":"2013","journal-title":"J. Am. Coll. Cardiol."},{"issue":"7","key":"10.1016\/j.bspc.2026.110363_b6","doi-asserted-by":"crossref","first-page":"767","DOI":"10.1001\/jamacardio.2017.1413","article-title":"Association of implantable cardioverter defibrillators with survival in patients with and without improved ejection fraction: Secondary analysis of the sudden cardiac death in heart failure trial","volume":"2","author":"Adabag","year":"2017","journal-title":"JAMA Cardiol."},{"issue":"1","key":"10.1016\/j.bspc.2026.110363_b7","doi-asserted-by":"crossref","first-page":"35","DOI":"10.1007\/s10396-021-01116-z","article-title":"How to standardize the measurement of left ventricular ejection fraction","volume":"49","author":"Kusunose","year":"2022","journal-title":"J. Med. Ultrason."},{"key":"10.1016\/j.bspc.2026.110363_b8","article-title":"Deep learning supported echocardiogram analysis: A comprehensive review","volume":"151","author":"Sanjeevi","year":"2024","journal-title":"Artif. Intell. Med."},{"issue":"4","key":"10.1016\/j.bspc.2026.110363_b9","doi-asserted-by":"crossref","first-page":"R115","DOI":"10.1530\/ERP-18-0056","article-title":"Artificial intelligence and echocardiography","volume":"5","author":"Alsharqi","year":"2018","journal-title":"Echo Res. Pr."},{"issue":"16","key":"10.1016\/j.bspc.2026.110363_b10","doi-asserted-by":"crossref","first-page":"1623","DOI":"10.1161\/CIRCULATIONAHA.118.034338","article-title":"Fully automated echocardiogram interpretation in clinical practice: Feasibility and diagnostic accuracy","volume":"138","author":"Zhang","year":"2018","journal-title":"Circulation"},{"issue":"9","key":"10.1016\/j.bspc.2026.110363_b11","doi-asserted-by":"crossref","first-page":"2198","DOI":"10.1109\/TMI.2019.2900516","article-title":"Deep learning for segmentation using an open large-scale dataset in 2D echocardiography","volume":"38","author":"Leclerc","year":"2019","journal-title":"IEEE Trans. Med. Imaging"},{"issue":"1","key":"10.1016\/j.bspc.2026.110363_b12","doi-asserted-by":"crossref","first-page":"e46","DOI":"10.1016\/S2589-7500(21)00235-1","article-title":"Automated interpretation of systolic and diastolic function on the echocardiogram: a multicohort study","volume":"4","author":"Tromp","year":"2022","journal-title":"Lancet Digit. Health"},{"key":"10.1016\/j.bspc.2026.110363_b13","series-title":"Ultrasound video transformers for cardiac ejection fraction estimation","author":"Reynaud","year":"2021"},{"issue":"9","key":"10.1016\/j.bspc.2026.110363_b14","article-title":"Automated echocardiographic quantification of left ventricular ejection fraction without volume measurements using a machine learning algorithm mimicking a human expert","volume":"12","author":"Asch","year":"2019","journal-title":"Circ.: Cardiovasc. Imaging"},{"key":"10.1016\/j.bspc.2026.110363_b15","article-title":"Automated identification of human gastrointestinal tract abnormalities based on deep convolutional neural network with endoscopic images","volume":"16","author":"Iqbal","year":"2022","journal-title":"Intell. Syst. Appl."},{"key":"10.1016\/j.bspc.2026.110363_b16","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2024.109573","article-title":"An end-to-end deep convolutional neural network-based data-driven fusion framework for identification of human induced pluripotent stem cell-derived endothelial cells in photomicrographs","volume":"139","author":"Iqbal","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"6","key":"10.1016\/j.bspc.2026.110363_b17","doi-asserted-by":"crossref","first-page":"624","DOI":"10.1001\/jamacardio.2021.0185","article-title":"Utility of a deep-learning algorithm to guide novices to acquire echocardiograms for limited diagnostic use","volume":"6","author":"Narang","year":"2021","journal-title":"JAMA Cardiol."},{"issue":"2","key":"10.1016\/j.bspc.2026.110363_b18","doi-asserted-by":"crossref","first-page":"625","DOI":"10.3390\/jcm14020625","article-title":"Revolutionizing cardiology: The role of artificial intelligence in echocardiography","volume":"14","author":"Maturi","year":"2025","journal-title":"J. Clin. Med."},{"issue":"1","key":"10.1016\/j.bspc.2026.110363_b19","doi-asserted-by":"crossref","DOI":"10.1002\/mp.70280","article-title":"Prototype bank-driven test-time adaptation for medical ultrasound image segmentation","volume":"53","author":"Wang","year":"2026","journal-title":"Med. Phys."},{"key":"10.1016\/j.bspc.2026.110363_b20","series-title":"EchoNet-dynamic: A large new cardiac motion dataset","author":"Ouyang","year":"2020"},{"key":"10.1016\/j.bspc.2026.110363_b21","series-title":"Echonet-frames-masks-dataset","author":"Maheshwari","year":"2024"},{"key":"10.1016\/j.bspc.2026.110363_b22","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.neunet.2019.08.025","article-title":"MultiResUNet : Rethinking the U-net architecture for multimodal biomedical image segmentation","volume":"121","author":"Ibtehaz","year":"2020","journal-title":"Neural Netw."},{"issue":"8","key":"10.1016\/j.bspc.2026.110363_b23","doi-asserted-by":"crossref","first-page":"987","DOI":"10.1109\/TMI.2006.877092","article-title":"Ultrasound image segmentation: a survey","volume":"25","author":"Noble","year":"2006","journal-title":"IEEE Trans. Med. Imaging"},{"key":"10.1016\/j.bspc.2026.110363_b24","series-title":"U-net: Convolutional networks for biomedical image segmentation","author":"Ronneberger","year":"2015"},{"key":"10.1016\/j.bspc.2026.110363_b25","series-title":"Ultralytics YOLO","author":"Ultralytics","year":"2024"},{"key":"10.1016\/j.bspc.2026.110363_b26","series-title":"SAM 2: Segment anything in images and videos","author":"Ravi","year":"2024"},{"issue":"3","key":"10.1016\/j.bspc.2026.110363_b27","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1016\/S0735-1097(83)80200-9","article-title":"Left ventricular volumes determined by two-dimensional echocardiography in a normal adult population","volume":"1","author":"Wahr","year":"1983","journal-title":"J. Am. Coll. Cardiol."},{"key":"10.1016\/j.bspc.2026.110363_b28","doi-asserted-by":"crossref","DOI":"10.1016\/0002-8703(82)90651-2","article-title":"Two-dimensional echocardiugraphic measurement of left ventricular ejection fraction: Prospective analysis of what constitutes an adequate determination","author":"Stamm","year":"1982","journal-title":"Am. Heart J."},{"issue":"3","key":"10.1016\/j.bspc.2026.110363_b29","doi-asserted-by":"crossref","first-page":"517","DOI":"10.1088\/0031-9155\/51\/3\/004","article-title":"Left ventricular motion reconstruction with a prolate spheroidal B-spline model","volume":"51","author":"Li","year":"2006","journal-title":"Phys. Med. Biol."},{"issue":"3","key":"10.1016\/j.bspc.2026.110363_b30","doi-asserted-by":"crossref","first-page":"863","DOI":"10.1016\/S0735-1097(83)80200-9","article-title":"Left ventricular volumes determined by two-dimensional echocardiography in a normal adult population","volume":"1","author":"Wahr","year":"1983","journal-title":"J. Am. Coll. Cardiol."},{"key":"10.1016\/j.bspc.2026.110363_b31","series-title":"Meta\u2019s SAM 2: The AI that can segment anything, even video","author":"Under","year":"2024"},{"key":"10.1016\/j.bspc.2026.110363_b32","series-title":"YOLO11: Il futuro della visione AI | ultralytics","year":"2024"},{"key":"10.1016\/j.bspc.2026.110363_b33","article-title":"Classification of AO\/OTA 31A\/B femur fractures in X-ray images using YOLOv8 and advanced data augmentation techniques","volume":"22","author":"Marullo","year":"2024","journal-title":"Bone Rep."},{"key":"10.1016\/j.bspc.2026.110363_b34","article-title":"Transfusion: Understanding transfer learning for medical imaging","volume":"325","author":"Raghu","year":"2019","journal-title":"Adv. Neural Inf. Process. Syst."},{"issue":"5","key":"10.1016\/j.bspc.2026.110363_b35","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3400066","article-title":"A survey of unsupervised deep domain adaptation","volume":"11","author":"Wilson","year":"2020","journal-title":"ACM Trans. Intell. Syst. Technol."},{"issue":"1","key":"10.1016\/j.bspc.2026.110363_b36","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.echo.2014.10.003","article-title":"Recommendations for cardiac chamber quantification by echocardiography in adults: An update from the American society of echocardiography and the European association of cardiovascular imaging","volume":"28","author":"Lang","year":"2015","journal-title":"J. Am. Soc. Echocardiogr."},{"key":"10.1016\/j.bspc.2026.110363_b37","series-title":"Ecocardiografia clinica: Sesta edizione","author":"Otto","year":"2019"},{"key":"10.1016\/j.bspc.2026.110363_b38","first-page":"353","volume":"vol. Unico","author":"Lancellotti","year":"2018"},{"key":"10.1016\/j.bspc.2026.110363_b39","article-title":"Explainable human-in-the-loop healthcare image information quality assessment and selection","volume":"n\/a","author":"Li","year":"2023","journal-title":"CAAI Trans. Intell. Technol."},{"key":"10.1016\/j.bspc.2026.110363_b40","series-title":"Actor-critic instance segmentation","author":"Araslanov","year":"2019"},{"issue":"9","key":"10.1016\/j.bspc.2026.110363_b41","doi-asserted-by":"crossref","DOI":"10.1161\/CIRCIMAGING.119.009303","article-title":"Automated echocardiographic quantification of left ventricular ejection fraction without volume measurements using a machine learning algorithm mimicking a human expert","volume":"12","author":"Asch","year":"2019","journal-title":"Circ. Cardiovasc. Imaging"},{"issue":"7","key":"10.1016\/j.bspc.2026.110363_b42","doi-asserted-by":"crossref","first-page":"1298","DOI":"10.3390\/diagnostics13071298","article-title":"Single-site experience with an automated artificial intelligence application for left ventricular ejection fraction measurement in echocardiography","volume":"13","author":"Sveric","year":"2023","journal-title":"Diagnostics"},{"issue":"1","key":"10.1016\/j.bspc.2026.110363_b43","doi-asserted-by":"crossref","DOI":"10.1111\/cpf.12918","article-title":"Impact of experience on visual and Simpson\u2019s biplane echocardiographic assessment of left ventricular ejection fraction","volume":"45","author":"Akil","year":"2025","journal-title":"Clin. Physiol. Funct. Imaging"},{"key":"10.1016\/j.bspc.2026.110363_b44","doi-asserted-by":"crossref","first-page":"40","DOI":"10.1016\/j.amjcard.2020.10.012","article-title":"Cardiac imaging to assess left ventricular systolic function in atrial fibrillation","volume":"139","author":"Bunting","year":"2021","journal-title":"Am. J. Cardiol."},{"issue":"20","key":"10.1016\/j.bspc.2026.110363_b45","doi-asserted-by":"crossref","first-page":"13456","DOI":"10.3390\/ijerph192013456","article-title":"Using unified modeling language to analyze business processes in the delivery of child health services","volume":"19","author":"Pecoraro","year":"2022","journal-title":"Int. J. Environ. Res. Public Health"},{"issue":"11","key":"10.1016\/j.bspc.2026.110363_b46","doi-asserted-by":"crossref","DOI":"10.1371\/journal.pone.0013893","article-title":"Standardizing clinical trials workflow representation in UML for international site comparison","volume":"5","author":"Carvalho","year":"2010","journal-title":"PLoS ONE"}],"container-title":["Biomedical Signal Processing and Control"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426009171?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1746809426009171?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T13:06:46Z","timestamp":1777036006000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1746809426009171"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":46,"alternative-id":["S1746809426009171"],"URL":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110363","relation":{},"ISSN":["1746-8094"],"issn-type":[{"value":"1746-8094","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"CardioSmartAssist: A customisable AI framework for echocardiography-based cardiac assessment","name":"articletitle","label":"Article Title"},{"value":"Biomedical Signal Processing and Control","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.bspc.2026.110363","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Authors. Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"110363"}}