{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T15:04:01Z","timestamp":1775919841622,"version":"3.50.1"},"reference-count":42,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,2,26]],"date-time":"2026-02-26T00:00:00Z","timestamp":1772064000000},"content-version":"vor","delay-in-days":56,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100007229","name":"Bijzonder Onderzoeksfonds UGent","doi-asserted-by":"publisher","award":["#038"],"award-info":[{"award-number":["#038"]}],"id":[{"id":"10.13039\/501100007229","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["esskajournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["J. exp. orthop."],"published-print":{"date-parts":[[2026,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Accurate anatomical landmarking is crucial for assessing lower limb alignment, diagnosing deformities, planning surgeries and monitoring treatment outcomes. Traditional methods rely on manual measurements from 2D standing radiographs, which fail to capture 3D bone morphology and are influenced by patient positioning. Weight\u2010bearing computed tomography (WBCT) enables 3D evaluations under physiological loading conditions, but manual landmark identification on WBCT is time\u2010consuming and subject to observer variability. This study aims to leverage deep learning (DL) and statistical shape modelling (SSM) for automated assessment of lower limb alignment and morphology.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A hybrid DL\u2010SSM model automatically calculated 28 lower limb alignment and morphology measurements using 30 full\u2010leg WBCT scans. The DL model was trained in a five\u2010fold cross\u2010validation setting. It automatically segmented the femur, patella, tibia, talus, calcaneus and second metatarsal. A cascaded SSM\u2010fitting methodology automatically identified the necessary 3D landmarks to derive the 28 measurements. The automated measurements were statistically compared to manual measurements performed by three experienced raters on both WBCT scans and 3D bone models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The DL segmentation model achieved high accuracy, with a mean dice similarity coefficient exceeding 0.96. The proposed method corresponded well to manual assessments, with the magnitude of detected differences generally matching the interobserver reliability of the manual method. The mean absolute error for the angular measurements ranged from 0.35\u00b0\u2009\u00b1\u20090.39\u00b0 to 5.53\u00b0\u2009\u00b1\u20094.68\u00b0.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The hybrid DL\u2010SSM methodology for automated assessment of lower limb alignment demonstrated comparable reliability to manual methods. This method provides an observer\u2010independent method for 3D lower limb alignment and morphology assessment under weight\u2010bearing conditions.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Level of Evidence<\/jats:title>\n                    <jats:p>Level III.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/jeo2.70669","type":"journal-article","created":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T09:04:05Z","timestamp":1772960645000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Combining deep learning with statistical shape modelling enables automated lower limb measurements with observer\u2010level reliability using weight\u2010bearing computed tomography"],"prefix":"10.1002","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8810-0644","authenticated-orcid":false,"given":"Ide","family":"Van den Borre","sequence":"first","affiliation":[{"name":"Department of Telecommunications and Information Processing Ghent University Ghent Belgium"},{"name":"Department of Human Structure and Repair Ghent University Ghent Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5508-6743","authenticated-orcid":false,"given":"Emmanuel","family":"Audenaert","sequence":"additional","affiliation":[{"name":"Department of Human Structure and Repair Ghent University Ghent Belgium"},{"name":"Department of Orthopaedic Surgery and Traumatology Ghent University Hospital Ghent Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hannes","family":"Vermue","sequence":"additional","affiliation":[{"name":"Department of Orthopaedic Surgery and Traumatology Ghent University Hospital Ghent Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Roel","family":"Huysentruyt","sequence":"additional","affiliation":[{"name":"Department of Telecommunications and Information Processing Ghent University Ghent Belgium"},{"name":"Department of Human Structure and Repair Ghent University Ghent Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leonie","family":"Van Vynckt","sequence":"additional","affiliation":[{"name":"Department of Human Structure and Repair Ghent University Ghent Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Robin","family":"Vanhauwe","sequence":"additional","affiliation":[{"name":"Department of Human Structure and Repair Ghent University Ghent Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Victor","family":"Pas","sequence":"additional","affiliation":[{"name":"Department of Human Structure and Repair Ghent University Ghent Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aleksandra","family":"Pizurica","sequence":"additional","affiliation":[{"name":"Department of Telecommunications and Information Processing Ghent University Ghent Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aline","family":"Van Oevelen","sequence":"additional","affiliation":[{"name":"Department of Human Structure and Repair Ghent University Ghent Belgium"},{"name":"Department of Orthopaedic Surgery and Traumatology Ghent University Hospital Ghent Belgium"},{"name":"Department of Electromechanics University of Antwerp Antwerp Belgium"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,2,26]]},"reference":[{"key":"e_1_2_11_2_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00402-021-04163-w"},{"key":"e_1_2_11_3_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.medengphy.2016.09.019"},{"key":"e_1_2_11_4_1","doi-asserted-by":"publisher","DOI":"10.1080\/10255842.2019.1577828"},{"key":"e_1_2_11_5_1","doi-asserted-by":"publisher","DOI":"10.3389\/fbioe.2019.00302"},{"key":"e_1_2_11_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11999-011-1936-5"},{"key":"e_1_2_11_7_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.gaitpost.2024.05.029"},{"key":"e_1_2_11_8_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00167-022-07302-x"},{"key":"e_1_2_11_9_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-022-18436-w"},{"key":"e_1_2_11_10_1","doi-asserted-by":"crossref","unstructured":"DanckaersF HuysmansT LackoD LeddaA VerwulgentS Van DongenS et al. 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