{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T05:55:09Z","timestamp":1791006909130,"version":"4.1.0"},"reference-count":34,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T00:00:00Z","timestamp":1790899200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,2]],"date-time":"2026-10-02T00:00:00Z","timestamp":1790899200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["R01EB031886"],"award-info":[{"award-number":["R01EB031886"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2137424"],"award-info":[{"award-number":["2137424"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Manual prescription of liver MRI volumes introduces inter\u2010operator variability and prolongs exam duration. Artificial intelligence (AI)\u2010based prescription has been demonstrated, but existing work has been limited to single\u2010vendor settings.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To develop and validate an AI model for automated liver MRI prescription using a large, consecutive, multicenter dataset encompassing multiple vendors, field strengths, localizer sequences, and liver pathologies.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Study Type<\/jats:title>\n                    <jats:p>Retrospective.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Population<\/jats:title>\n                    <jats:p>A total of 11,012 patients (5928 female) who underwent liver MRI exams across three vendors (GE HealthCare, Philips Healthcare, Siemens Healthineers) at three institutions, split into training\/validation\/test sets (70\/10\/20%). Liver disorders included cirrhosis, ascites, portal hypertension, and iron overload.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Field Strength\/Sequence<\/jats:title>\n                    <jats:p>Three\u2010plane localizers acquired using single\u2010shot fast spin echo (68.3%) and gradient echo (31.7%) at 1.5\u2009T (52.8%) and 3\u2009T (47.2%).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Assessment<\/jats:title>\n                    <jats:p>\n                      A YOLOv8 object detection model was trained to detect the liver, torso, and arms on localizer images. Automated prescription accuracy was assessed using three\u2010dimensional intersection\u2010over\u2010union (IoU\n                      <jats:sub>3D<\/jats:sub>\n                      ) for axial, coronal, and sagittal prescriptions. Subgroup analyses evaluated performance across technical and patient\u2010related factors.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Statistical Tests<\/jats:title>\n                    <jats:p>\n                      Mann\u2013Whitney\n                      <jats:italic>U<\/jats:italic>\n                      tests with Bonferroni correction, Kruskal\u2010Wallis tests, Wilcoxon signed\u2010rank tests, Spearman correlation. Effect sizes were calculated using rank\u2010biserial correlation (\n                      <jats:italic>r<\/jats:italic>\n                      <jats:sub>rb<\/jats:sub>\n                      ) and epsilon\u2010squared (\n                      <jats:italic>\u03b5<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      ).\n                      <jats:italic>p<\/jats:italic>\n                      \u2009&lt;\u20090.05 was considered statistically significant.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      Median axial prescription IoU\n                      <jats:sub>3D<\/jats:sub>\n                      was 0.963 (interquartile range: 0.946\u20130.976), with median IoU\n                      <jats:sub>3D<\/jats:sub>\n                      above 0.940 for coronal and sagittal prescriptions. For axial prescriptions, margins less than 4\u2009mm in each direction were required to achieve complete coverage of manual prescriptions in 90% of cases in the held\u2010out test set. Effects were negligible to small across vendors, field strengths, localizer sequences, and patient factors (\n                      <jats:italic>r<\/jats:italic>\n                      <jats:sub>rb<\/jats:sub>\n                      \u2264\u20090.16,\n                      <jats:italic>\u03b5<\/jats:italic>\n                      <jats:sup>2<\/jats:sup>\n                      \u2009\u2264\u20090.01).\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Data Conclusion<\/jats:title>\n                    <jats:p>A single multivendor model achieved accurate automated liver MRI prescription without clinically meaningful performance differences across vendors, field strengths, localizer sequences, or patient factors.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Evidence Level<\/jats:title>\n                    <jats:p>2.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Technical Efficacy<\/jats:title>\n                    <jats:p>Stage 1.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jmri.70575","type":"journal-article","created":{"date-parts":[[2026,10,3]],"date-time":"2026-10-03T05:36:33Z","timestamp":1791005793000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multicenter, Multivendor Development and Validation of Automated Liver Prescription"],"prefix":"10.1002","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3420-9956","authenticated-orcid":false,"given":"Garrett C.","family":"Fullerton","sequence":"first","affiliation":[{"name":"Department of Radiology University of Wisconsin\u2013Madison  Madison Wisconsin USA"},{"name":"Department of Medical Physics University of Wisconsin\u2013Madison  Madison Wisconsin 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