{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,26]],"date-time":"2026-05-26T11:04:47Z","timestamp":1779793487193,"version":"3.53.1"},"reference-count":21,"publisher":"SAGE Publications","issue":"3","license":[{"start":{"date-parts":[[2026,3,5]],"date-time":"2026-03-05T00:00:00Z","timestamp":1772668800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["HIP International"],"published-print":{"date-parts":[[2026,5]]},"abstract":"<jats:sec>\n                    <jats:title>Introduction:<\/jats:title>\n                    <jats:p>Manual measurement of leg length (LL) and offset can be tedious. This study developed an automated algorithm for measuring LL and offset from pre- and postoperative AP pelvis radiographs in a large cohort of THA patients.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods:<\/jats:title>\n                    <jats:p>Using a deep learning model trained on 1100 total AP pelvis radiographs, an algorithm was developed to calculate LL and offset. Algorithm measurements were compared with manual measurements by 4 raters on a sample of 100 pre- and postoperative image pairs. Inter- and intra-rater consistency was calculated using the intraclass correlation coefficient (ICC). The algorithm was applied to calculate the pre- and postoperative LL and offset discrepancies and the change in LL and offset bilaterally in a cohort of 15,951 image pairs.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results:<\/jats:title>\n                    <jats:p>ICC values between the algorithm and human raters ranged from 0.83 to 0.88 for offset measurements and 0.92 to 0.97 for LL measurements. Human raters demonstrated good-to-excellent inter-rater ICC and uniformly excellent intra-rater ICC. Entire database measurements demonstrated shorter LLs for arthritic joints versus the contralateral leg preoperatively and reduced LL discrepancy post-arthroplasty.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions:<\/jats:title>\n                    <jats:p>We present a deep learning algorithm for calculating LL and offset using AP pelvis radiographs. This tool can support population-level studies and may assist operative management.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1177\/11207000261421918","type":"journal-article","created":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T10:47:50Z","timestamp":1773226070000},"page":"388-397","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["An algorithm for automated femoral leg length and offset calculations on pelvis radiographs"],"prefix":"10.1177","volume":"36","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1019-5035","authenticated-orcid":false,"given":"Kellen L","family":"Mulford","sequence":"first","affiliation":[{"name":"Orthopedic Surgery Artificial Intelligence Laboratory, Department of Orthopaedic Surgery, Mayo Clinic, Rochester, MN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ryan D","family":"Roman","sequence":"additional","affiliation":[{"name":"Orthopedic Surgery Artificial Intelligence Laboratory, Department of Orthopaedic Surgery, Mayo Clinic, Rochester, MN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Joshua R","family":"Labott","sequence":"additional","affiliation":[{"name":"Mayo Clinic Department of Orthopaedic Surgery, Mayo Clinic, Rochester, MN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elizabeth S","family":"Kaji","sequence":"additional","affiliation":[{"name":"Orthopedic Surgery Artificial Intelligence Laboratory, Department of Orthopaedic Surgery, Mayo Clinic, Rochester, MN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Austin F","family":"Grove","sequence":"additional","affiliation":[{"name":"Orthopedic Surgery Artificial Intelligence Laboratory, Department of Orthopaedic Surgery, Mayo Clinic, Rochester, MN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael J","family":"Taunton","sequence":"additional","affiliation":[{"name":"Orthopedic Surgery Artificial Intelligence Laboratory, Department of Orthopaedic Surgery, Mayo Clinic, Rochester, MN, USA"},{"name":"Mayo Clinic Department of Orthopaedic Surgery, Mayo Clinic, Rochester, MN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8629-7567","authenticated-orcid":false,"given":"Cody C","family":"Wyles","sequence":"additional","affiliation":[{"name":"Orthopedic Surgery Artificial Intelligence Laboratory, Department of Orthopaedic Surgery, Mayo Clinic, Rochester, MN, USA"},{"name":"Mayo Clinic Department of Orthopaedic Surgery, Mayo Clinic, Rochester, MN, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2026,3,5]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.arth.2015.04.030"},{"key":"e_1_3_2_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.arth.2013.12.033"},{"key":"e_1_3_2_4_2","doi-asserted-by":"publisher","DOI":"10.3389\/fbioe.2021.645019"},{"key":"e_1_3_2_5_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.arth.2022.09.001"},{"key":"e_1_3_2_6_2","doi-asserted-by":"publisher","DOI":"10.2106\/00004623-200312000-00007"},{"key":"e_1_3_2_7_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.clinbiomech.2019.05.015"},{"key":"e_1_3_2_8_2","doi-asserted-by":"publisher","DOI":"10.3928\/0147-7447-20000901-20"},{"key":"e_1_3_2_9_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.otsr.2009.03.010"},{"key":"e_1_3_2_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.arth.2023.03.006"},{"key":"e_1_3_2_11_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11999-010-1588-x"},{"key":"e_1_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.arth.2022.05.029"},{"key":"e_1_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.2106\/JBJS.21.01229"},{"key":"e_1_3_2_14_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.arth.2023.05.036"},{"key":"e_1_3_2_15_2","unstructured":"Ronneberger O Fischer P Brox T. 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