{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T22:37:00Z","timestamp":1779403020956,"version":"3.53.1"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686615","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T00:00:00Z","timestamp":1779321600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2026,5,21]]},"abstract":"<jats:p>In critically ill patients, left ventricular ejection fraction (LVEF) is a key prognostic indicator, yet its assessment via echocardiography or cardiac magnetic resonance (CMR) is frequently delayed or unavailable in the intensive care unit (ICU). This study proposes a novel multimodal approach that combines deep embeddings from a Vision Transformer (ViT) pretrained on CMR data with conventional quantitative ECG features to predict reduced LVEF (\u226440%) directly from 12-lead ICU electrocardiograms. Using a retrospective cohort of approximately 900 ICU admissions from a single tertiary centre, three classifiers\u2014Random Forest, XGBoost, and a shallow multilayer perceptron (MLP) trained with focal loss\u2014were compared under stratified 5-fold cross-validation. The MLP achieved the best performance (AUC = 0.87, recall = 0.68, F1 = 0.65), demonstrating that cross-modal ViT embeddings transfer meaningful cardiac-structural information to ECG-based prediction tasks. These results support the feasibility of using routine ECGs as an early screening tool for ventricular dysfunction in the ICU.<\/jats:p>","DOI":"10.3233\/shti260123","type":"book-chapter","created":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T21:55:59Z","timestamp":1779400559000},"source":"Crossref","is-referenced-by-count":0,"title":["Prediction of Left Ventricular Systolic Dysfunction from ICU Electrocardiograms Using Vision Transformer Embeddings and Multimodal Features"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-8366-1474","authenticated-orcid":false,"given":"Jacopo","family":"Lenkowicz","sequence":"first","affiliation":[{"name":"Fondazione Policlinico Universitario A. Gemelli IRCCS"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8033-5411","authenticated-orcid":false,"given":"Nicoletta","family":"di Giorgi","sequence":"additional","affiliation":[{"name":"Fondazione Policlinico Universitario A. Gemelli IRCCS"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Opening the Personal Gate between Technology and Health Care"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI260123","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T21:56:00Z","timestamp":1779400560000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI260123"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,5,21]]},"ISBN":["9781643686615"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti260123","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,5,21]]}}}