{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T04:04:32Z","timestamp":1773029072812,"version":"3.50.1"},"reference-count":32,"publisher":"Wiley","issue":"4","license":[{"start":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T00:00:00Z","timestamp":1766534400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T00:00:00Z","timestamp":1766534400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100002701","name":"Ministry of Education","doi-asserted-by":"publisher","award":["NRF\u20102021R1C1C1014413"],"award-info":[{"award-number":["NRF\u20102021R1C1C1014413"]}],"id":[{"id":"10.13039\/501100002701","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002701","name":"Ministry of Education","doi-asserted-by":"publisher","award":["NRF\u20102022R1I1A1A01072397"],"award-info":[{"award-number":["NRF\u20102022R1I1A1A01072397"]}],"id":[{"id":"10.13039\/501100002701","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Magnetic Resonance Imaging"],"published-print":{"date-parts":[[2026,4]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Brain segmentation using structural MRI is effective for identifying regional atrophy in Parkinsonian syndromes. However, clinical validation of the automated deep learning\u2010based brainstem segmentation model has been limited.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To develop and validate a two\u2010step deep learning algorithm for automatic segmentation of brainstem substructures and classifying Parkinsonian syndromes using derived volumetric measurements.<\/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>Subjects<\/jats:title>\n                    <jats:p>The internal dataset comprised 300 normal cognition (NC) subjects (171 females) for segmentation and 513 subjects (265 males) for classification (207 NC, 52 progressive supranuclear palsy [PSP], 65 multiple system atrophy\u2010cerebellar variant [MSA\u2010C], and 189 Parkinson's disease [PD]). The external dataset comprised 82 subjects (43 males; 24 PSP, 28 MSA\u2010C, and 30 PD).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Field Strength\/Sequence<\/jats:title>\n                    <jats:p>3D gradient\u2010echo T1\u2010weighted sequence at 3\u2009T.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Assessment<\/jats:title>\n                    <jats:p>Segmentation performance was evaluated with the Dice Similarity Coefficient (DSC) by comparing model outputs against manual labels. For classification, regional brain volumes from the segmentations were used as input features for multi\u2010class classification with support vector machine (SVM), random forest, and XGBoost models, evaluated by area under the receiver operating characteristic curve (AUROC). Five\u2010fold cross\u2010validation was used for internal validation and tested on an external dataset. Three radiologists analyzed an external dataset with and without the model, with a one\u2010month washout period between sessions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Statistical Tests<\/jats:title>\n                    <jats:p>\n                      For the segmentation volume, differences between groups were assessed using Student's\n                      <jats:italic>t<\/jats:italic>\n                      \u2010test or Mann\u2013Whitney\n                      <jats:italic>U<\/jats:italic>\n                      test. Classification performance was evaluated using a one\u2010vs\u2010rest approach with macro\u2010averaging across classes.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Brainstem segmentation DSC scores were 0.969 (internal) and 0.996 (external) compared to the ground\u2010truth masks. Using regional volumetrics, the SVM achieved the highest differentiation performance, with AUROCs of 0.937 (internal) and 0.914 (external). A radiology resident achieved improved performance with the model.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Data Conclusion<\/jats:title>\n                    <jats:p>Our proposed two\u2010step algorithm combining deep\u2010learning\u2010based brainstem segmentation and machine\u2010learning classification enables automated differentiation of Parkinsonian syndromes using 3D T1\u2010weighted brain MRI.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Evidence Level<\/jats:title>\n                    <jats:p>3.<\/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.70215","type":"journal-article","created":{"date-parts":[[2025,12,24]],"date-time":"2025-12-24T12:18:23Z","timestamp":1766578703000},"page":"1108-1121","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Deep Learning\u2010Based Brainstem Segmentation and Multi\u2010Class Classification for Parkinsonian Syndrome"],"prefix":"10.1002","volume":"63","author":[{"given":"Seongken","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Radiology and Research Institute of Radiology Asan Medical Center, University of Ulsan College of Medicine  Seoul Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Pae Sun","family":"Suh","sequence":"additional","affiliation":[{"name":"Department of Radiology, Research Institute of Radiological Science and Center for Clinical Imaging Data Science Yonsei University College of Medicine  Seoul Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Woo Hyun","family":"Shim","sequence":"additional","affiliation":[{"name":"Department of Radiology and Research Institute of Radiology Asan Medical Center, University of Ulsan College of Medicine  Seoul Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hwon","family":"Heo","sequence":"additional","affiliation":[{"name":"Department of Radiology and Research Institute of Radiology Asan Medical Center, University of Ulsan College of Medicine  Seoul Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Changhyun","family":"Park","sequence":"additional","affiliation":[{"name":"VUNO Inc.  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