{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,20]],"date-time":"2026-10-20T15:30:31Z","timestamp":1792510231121,"version":"4.5.0"},"reference-count":40,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2024,7,21]],"date-time":"2024-07-21T00:00:00Z","timestamp":1721520000000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Knee surg. sports traumatol. arthrosc."],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:sec><jats:title>Purpose<\/jats:title><jats:p>The aim of this study was to conduct a systematic review and meta\u2010analysis on the reliability and applicability of artificial intelligence (AI)\u2010based analysis of leg axis parameters. We hypothesized that AI\u2010based leg axis measurements would be less time\u2010consuming and as accurate as those performed by human raters.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>The study protocol was registered with the International Prospective Register of Systematic Reviews (PROSPERO). PubMed, Epistemonikos, and Web of Science were searched up to 24 February\u00a02024, using a BOOLEAN search strategy. Titles and abstracts of identified records were screened through a stepwise process. Data extraction and quality assessment of the included papers were followed by a frequentist meta\u2010analysis employing a common effect\/random effects model with inverse variance and the Sidik\u2013Jonkman heterogeneity estimator.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>A total of 13 studies encompassing 3192 patients were included in this meta\u2010analysis. All studies compared AI\u2010based leg axis measurements on long\u2010leg radiographs (LLR) with those performed by human raters. The parameters hip knee ankle angle (HKA), mechanical lateral distal femoral angle (mLDFA), mechanical medial proximal tibial angle (mMPTA), and joint\u2010line convergence angle (JLCA) showed excellent agreement between AI and human raters. The AI system was approximately 3\u2009min faster in reading standing long\u2010leg anteroposterior radiographs (LLRs) compared with human raters.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>AI\u2010based assessment of leg axis parameters is an efficient, accurate, and time\u2010saving procedure. The quality of AI\u2010based assessment of the investigated parameters does not appear to be affected by the presence of implants or pathological conditions.<\/jats:p><\/jats:sec><jats:sec><jats:title>Level of Evidence<\/jats:title><jats:p>Level I.<\/jats:p><\/jats:sec>","DOI":"10.1002\/ksa.12362","type":"journal-article","created":{"date-parts":[[2024,7,21]],"date-time":"2024-07-21T03:19:41Z","timestamp":1721531981000},"page":"177-190","source":"Crossref","is-referenced-by-count":25,"title":["Artificial intelligence\u2010based assessment of leg axis parameters shows excellent agreement with human raters: A systematic review and meta\u2010analysis"],"prefix":"10.1002","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-0957-2285","authenticated-orcid":false,"given":"Mikhail","family":"Salzmann","sequence":"first","affiliation":[{"name":"Center of Orthopaedics and Traumatology, Brandenburg Medical School University Hospital Brandenburg an der Havel  Brandenburg an der Havel 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