{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,28]],"date-time":"2026-10-28T23:45:35Z","timestamp":1793231135187,"version":"4.5.0"},"reference-count":21,"publisher":"Wiley","license":[{"start":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T00:00:00Z","timestamp":1789776000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"},{"start":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T00:00:00Z","timestamp":1789776000000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["arthroscopyjournals.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Arthroscopy"],"abstract":"<jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>To develop an artificial intelligence\u2010based system capable of measuring preoperative alignment parameters, calculating correction angles, and visualizing outcomes for open wedge high tibial osteotomy (OWHTO) and to validate the accuracy and efficiency of the system in clinical cases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>Between January and December 2023, standing hip\u2010to\u2010ankle radiographs from patients with varus knee osteoarthritis were retrospectively collected as the development cohort. The OWHTO auxiliary planning system (OAPS) was developed using a UNet convolutional neural network to detect 28 anatomical landmarks. The system was validated against manual measurements by attending surgeons and further evaluated through an external validation cohort across 2 institutions to compare accuracy and efficiency among junior residents using manual, semiautomatic, and OAPS\u2010assisted methods.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>\n                      The study included 346 knees for development and an external validation cohort of 110 knees. On the test set, the OAPS achieved a mean radial error of 2.703 pixels and a successful detection rate at the 6\u2010pixel threshold of 0.951. For hip\u2010knee\u2010ankle and correction angles, the mean absolute errors were 0.47\u00b0 (95% CI: 0.39\u00b0\u20100.56\u00b0) and 0.43\u00b0 (95% CI: 0.36\u00b0\u20100.51\u00b0), respectively. No significant differences were found between OAPS and attending surgeons across all alignment parameters (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&gt;\u2009.05). OAPS processed each image in 9.27\u2009seconds, which was significantly faster than the 4.30\u2009minutes required for manual planning on the test set (\n                      <jats:italic>P<\/jats:italic>\n                      \u2009&lt;\u2009.001). External validation confirmed that OAPS\u2010assisted resident measurements matched attending\u2010level accuracy and were significantly faster than manual and semiautomatic methods.\n                    <\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The artificial intelligence\u2010based OAPS enables accurate, efficient preoperative planning for OWHTO, achieving measurement precision comparable to experienced surgeons while significantly reducing planning time.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Clinical Relevance<\/jats:title>\n                    <jats:p>By automating anatomical measurement and planning, the OAPS reduces interobserver variability and enhances preoperative plan efficiency within OWHTO workflows, streamlining the preparation phase for surgical teams.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/arj.70515","type":"journal-article","created":{"date-parts":[[2026,9,20]],"date-time":"2026-09-20T06:54:08Z","timestamp":1789887248000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["An Artificial Intelligence\u2010Based Preoperative Planning System for High Tibial Osteotomy Significantly Enhances Planning Speed With Comparable Accuracy to Surgeons"],"prefix":"10.1002","author":[{"given":"Songlin","family":"Li","sequence":"first","affiliation":[{"name":"Department of Orthopedics Qilu Hospital of Shandong University  Jinan Shandong China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhe","family":"Li","sequence":"additional","affiliation":[{"name":"Department of Orthopedics Qilu Hospital of Shandong University  Jinan Shandong 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