{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T10:50:34Z","timestamp":1782989434207,"version":"3.54.5"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643686684","type":"electronic"}],"license":[{"start":{"date-parts":[[2026,6,29]],"date-time":"2026-06-29T00:00:00Z","timestamp":1782691200000},"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":[[2026,6,29]]},"abstract":"<jats:p>Deploying cardiac segmentation models as remote inference services creates a black-box setting in which robustness under out-of-distribution shift is critical. We evaluated two candidate configurations for biventricular segmentation of short-axis cine cardiac magnetic resonance images trained on the same public benchmark dataset: a nnU-Net ensemble and nnSAM. Both were assessed within the same deployment-oriented framework on two external cohorts, a multi-site, multi-vendor adult dataset and a single-site, multi-scanner pediatric congenital heart disease dataset. Performance was evaluated using geometric metrics and biomarker-based endpoints, including clinically relevant ejection fraction (EF) failure rates. While performance was similar in the adult cohort, the nnU-Net ensemble was more robust in the pediatric cohort, with better geometric preservation and lower EF failure rates. These findings indicate that deployment-oriented validation of remote cardiac segmentation services should assess biomarker reliability alongside geometric accuracy.<\/jats:p>","DOI":"10.3233\/shti260788","type":"book-chapter","created":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T09:59:05Z","timestamp":1782986345000},"source":"Crossref","is-referenced-by-count":0,"title":["Pre-Deployment Evaluation of a Remote Service for Short-Axis Cardiac MRI Segmentation"],"prefix":"10.3233","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-2237-1910","authenticated-orcid":false,"given":"Sadat Hasan","family":"Chowdhury","sequence":"first","affiliation":[{"name":"Peter L. Reichertz Institute for Medical Informatics, Hannover Medical School (MHH), Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hinrich","family":"Winther","sequence":"additional","affiliation":[{"name":"Institute of Diagnostic and Interventional Radiology, MHH, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Steffen","family":"Oeltze-Jafra","sequence":"additional","affiliation":[{"name":"Peter L. Reichertz Institute for Medical Informatics, Hannover Medical School (MHH), Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"7437","container-title":["Studies in Health Technology and Informatics","Health Sciences Informatics Leads and Empowers the Digital Health Transformation"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/SHTI260788","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T09:59:05Z","timestamp":1782986345000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/SHTI260788"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6,29]]},"ISBN":["9781643686684"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/shti260788","relation":{},"ISSN":["0926-9630","1879-8365"],"issn-type":[{"value":"0926-9630","type":"print"},{"value":"1879-8365","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,6,29]]}}}