{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,2]],"date-time":"2026-09-02T16:56:01Z","timestamp":1788368161672,"version":"build-2803163510"},"reference-count":39,"publisher":"SAGE Publications","issue":"9","license":[{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2025,9,1]],"date-time":"2025-09-01T00:00:00Z","timestamp":1756684800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100001711","name":"Schweizerischer Nationalfonds zur F\u00c3\u00b6rderung der Wissenschaftlichen Forschung","doi-asserted-by":"publisher","award":["P2BEP3_195241"],"award-info":[{"award-number":["P2BEP3_195241"]}],"id":[{"id":"10.13039\/501100001711","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Orthopaedic Journal of Sports Medicine"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:sec>\n                    <jats:title>Background:<\/jats:title>\n                    <jats:p>Femoroacetabular impingement (FAI) can cause hip pain and osteoarthritis in patients. Importantly, 3-dimensional (3D) bone models in dynamic hip impingement simulations can enable a patient-specific diagnosis of FAI. The manual segmentation of bone models is time-consuming; therefore, automatic segmentation should be investigated.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Purpose:<\/jats:title>\n                    <jats:p>To investigate the difference between manual and automatic segmentation of magnetic resonance imaging (MRI)\u2013based 3D bone models of the hip, (2) to correlate impingement-free hip range of motion, and (3) to determine external validation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Study Design:<\/jats:title>\n                    <jats:p>Cohort study (Diagnosis); Level of evidence, 3.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>An institutional review board\u2013approved retrospective study involving a total of 98 hips was performed. Of these, 30 patients with symptomatic FAI (60 hips; mean age, 27 \u00b1 9 years) and 19 asymptomatic participants (38 hips) underwent 3-T MRI of the hip including a rapid 3D T1-weighted VIBE Dixon sequence of the pelvis (192 images acquired in 32 seconds). The automatic segmentation of MRI-based 3D bone models was performed using machine learning (convolutional neural network). The Dice similarity coefficient (DSC) was calculated for 98 hips to assess the overlap and difference in MRI-based 3D bone models with 5-fold cross-validation. Automatic segmentation was assessed with 16 patients (32 hips) from another institution for external validation. Impingement-free range of motion was compared between the manual and automatic segmentation of MRI-based 3D bone models (30 patients with FAI).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>\n                      The difference between the manual and automatic segmentation of MRI-based 3D bone models was &lt;1 mm (&lt;0.6 mm for pelvic models and &lt;0.5 mm for femoral models), and the DSC of the 30 patients with FAI was 94.1% (pelvis) and 97.0% (proximal femur); for the 19 asymptomatic participants, the difference was &lt;0.5 mm, and the DSC was 95.5% and 97.5%, respectively. The correlation for impingement-free flexion (\n                      <jats:italic>r<\/jats:italic>\n                      = 0.93;\n                      <jats:italic>P<\/jats:italic>\n                      &lt; .001) was excellent (30 patients with FAI). The mean difference in flexion and internal rotation at 90\u00b0 of flexion was 2.9\u00b0\u00b1 4.0\u00b0 and 3.0\u00b0\u00b1 4.0\u00b0, respectively. The DSC of the 16 patients from another institution was 92.2% (pelvis) and 94.9% (proximal femur) for external validation.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion:<\/jats:title>\n                    <jats:p>The automatic segmentation of MRI-based 3D bone models was as accurate as the manual segmentation of MRI-based 3D bone models for patients with FAI. It allows radiation-free and patient-specific preoperative surgical planning of hip preservation surgery and hip arthroscopic surgery for patients with FAI of a childbearing age. The automatic segmentation of MRI-based 3D bone models using deep learning was feasible with routine MRI (3D T1-weighted VIBE Dixon sequence) with a short image acquisition time (&lt;1 minute).<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/23259671251334138","type":"journal-article","created":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T10:18:53Z","timestamp":1758277133000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":3,"title":["Magnetic Resonance Imaging\u2013Based 3-Dimensional Models of the Pelvis and Hip Using Machine Learning for Automatic Bone Segmentation in a Dynamic Hip Impingement Simulation"],"prefix":"10.1177","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0475-0269","authenticated-orcid":false,"given":"Till D.","family":"Lerch","sequence":"first","affiliation":[{"name":"Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, University of Bern, Bern, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Guodong","family":"Zeng","sequence":"additional","affiliation":[{"name":"Department of Radiology, District Hospital St. Johann in Tirol, Austria"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Adam","family":"Boschung","sequence":"additional","affiliation":[{"name":"Department of Orthopedic Surgery and Traumatology, Inselspital, University of Bern, Bern, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Malin K.","family":"Meier","sequence":"additional","affiliation":[{"name":"Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, University of Bern, Bern, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Simon D.","family":"Steppacher","sequence":"additional","affiliation":[{"name":"Department of Orthopedic Surgery and Traumatology, Inselspital, University of Bern, Bern, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Moritz","family":"Tannast","sequence":"additional","affiliation":[{"name":"Department of Orthopedic Surgery and Traumatology, Inselspital, University of Bern, Bern, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Johannes","family":"Heverhagen","sequence":"additional","affiliation":[{"name":"Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, University of Bern, Bern, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Florian","family":"Schmaranzer","sequence":"additional","affiliation":[{"name":"Department of Diagnostic, Interventional and Pediatric Radiology, Inselspital, University of Bern, Bern, Switzerland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2025,9,19]]},"reference":[{"key":"e_1_3_3_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.arthro.2011.11.005"},{"key":"e_1_3_3_3_2","doi-asserted-by":"publisher","DOI":"10.1177\/23259671221148502"},{"key":"e_1_3_3_4_2","doi-asserted-by":"publisher","DOI":"10.1177\/03635465231153624"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1177\/0363546519878170"},{"key":"e_1_3_3_6_2","doi-asserted-by":"publisher","DOI":"10.1097\/CORR.0000000000001778"},{"key":"e_1_3_3_7_2","doi-asserted-by":"publisher","DOI":"10.1148\/ryai.2020190116"},{"key":"e_1_3_3_8_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.media.2014.02.002"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-019-06167-y"},{"key":"e_1_3_3_10_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11548-018-1899-z"},{"key":"e_1_3_3_11_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-018-34817-6"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11999-008-0617-5"},{"key":"e_1_3_3_13_2","doi-asserted-by":"publisher","DOI":"10.1002\/jmri.28067"},{"key":"e_1_3_3_14_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00330-019-06136-5"},{"key":"e_1_3_3_15_2","doi-asserted-by":"publisher","DOI":"10.1097\/01.blo.0000096804.78689.c2"},{"key":"e_1_3_3_16_2","first-page":"1","article-title":"Automatic segmentation of the paediatric femoral head","volume":"2023","author":"Gartland C","year":"2023","unstructured":"Gartland C, Curran E, Healy J, et al. 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