{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T20:43:23Z","timestamp":1793133803956,"version":"4.5.0"},"reference-count":15,"publisher":"Springer Science and Business Media LLC","license":[{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T00:00:00Z","timestamp":1782777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["25K15930"],"award-info":[{"award-number":["25K15930"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100004721","name":"The University of Tokyo","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100004721","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["J Digit Imaging. Inform. med."],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>The purpose of this study is to develop and validate a deep learning model for automatic identification of acquisition sequences (including T1-weighted sequences with various dynamic contrast-enhanced phases and other auxiliary sequences) in gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)\u2013enhanced liver MRI, enabling automated examination-level data curation. This retrospective study included internal Gd-EOB-DTPA\u2013enhanced liver MRI examinations acquired at our institution between June 2018 and May 2020, with independent external test datasets from three additional institutions. Each examination comprised multiple dynamic contrast-enhanced phases and auxiliary sequences, resulting in 13 predefined label categories. A deep learning pipeline was constructed using a convolutional neural network (ConvNeXt) for feature extraction, followed by sequential models (gated recurrent unit [GRU]-based or transformer-based) to model temporal relationships across series. Models were trained using series-level 3D image volumes without reliance on textual Digital Imaging and Communications in Medicine metadata. Model selection was performed on a validation set. Performance was evaluated on internal and external test sets using examination-level accuracy, complete-correct rate, and category-level accuracy. Sequential modeling substantially improved performance compared with a convolution-only baseline. The ConvNeXt\u2009+\u2009GRU model achieved the highest examination-level accuracy and was selected for final evaluation. Dynamic contrast-enhanced phases were identified with high accuracy across datasets. Reduced performance on the external test set was mainly observed for auxiliary sequences with high inter-institutional variability, particularly in T2-weighted imaging. The proposed framework enables accurate automatic identification of dynamic phases and auxiliary sequences in Gd-EOB-DTPA\u2013enhanced liver MRI, supporting robust examination-level data organization in multicenter studies.<\/jats:p>","DOI":"10.1007\/s10278-026-02080-1","type":"journal-article","created":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T23:42:08Z","timestamp":1782862928000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Automatic Phase and Sequence Identification in Gd-EOB-DTPA\u2013Enhanced Liver MRI Using Deep Convolutional and Sequential Learning"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7198-1588","authenticated-orcid":false,"given":"Tomomi","family":"Takenaga","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7496-1651","authenticated-orcid":false,"given":"Shouhei","family":"Hanaoka","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6471-9936","authenticated-orcid":false,"given":"Yukihiro","family":"Nomura","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4222-4569","authenticated-orcid":false,"given":"Tomohiro","family":"Kikuchi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1924-5468","authenticated-orcid":false,"given":"Takeharu","family":"Yoshikawa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1180-2629","authenticated-orcid":false,"given":"Osamu","family":"Abe","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,30]]},"reference":[{"key":"2080_CR1","doi-asserted-by":"crossref","unstructured":"Kudo M, Kawamura Y, Hasegawa K, Tateishi R, Kariyama K, Shiina S, Toyoda H, Imai Y, Hiraoka A, Ikeda M, Izumi N, Moriguchi M, Ogasawara S, Minami Y, Ueshima K, Murakami T, Miyayama S, Nakashima O, Yano H, Sakamoto M, Hatano E, Shimada M, Kokudo N, Mochida S, Takehara T: Management of hepatocellular carcinoma in Japan: JSH consensus statements and recommendations 2021 update. 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