{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T22:57:37Z","timestamp":1780959457471,"version":"3.54.1"},"reference-count":22,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T00:00:00Z","timestamp":1780876800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T00:00:00Z","timestamp":1780876800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100004913","name":"Universit\u00e0 degli Studi di Palermo","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100004913","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Eur J Orthop Surg Traumatol"],"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Purpose<\/jats:title>\n                    <jats:p>Robotic-assisted total knee arthroplasty (RA-TKA) is increasingly used to improve implant positioning, soft-tissue balance, and procedural reproducibility. Yet, little is known about how different components of the operation independently contribute to the overall learning curve. This study aimed to characterize the learning curve of MAKO-assisted TKA by separately evaluating these components and assessing their differential impact on operative workflow.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A retrospective observational study included 92 consecutive patients who underwent image-based RA-TKA (MAKO, Stryker) for primary knee osteoarthritis. All procedures were performed by a single experienced arthroplasty surgeon with no prior robotic or computer-assisted surgery background. Cumulative sum (CUSUM) analysis with piecewise linear regression was applied to total surgical time, pin placement time, and composite robotic workflow time to identify inflection points and define learning curve phases.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Piecewise regression of the CUSUM plot for total surgical time revealed two breakpoints at cases 11 and 51, defining three phases of the learning curve: (1) initial learning (cases 1\u201311), (2) competence (cases 12\u201351), and (3) optimized performance (cases 52\u201390). Mean surgical time was 68.9\u2009\u00b1\u200920.1\u00a0min, stabilizing around 65\u00a0min after 50 cases. Along with total surgical time, the initial learning phase ended around cases 10\u201311 for both robotic workflow and pin placement. However, subsequent performance patterns differed: pin placement reached optimized performance by case 52 (mean 8.4\u2009\u00b1\u20094.3\u00a0min), whereas robotic workflow time improved more gradually, without clear stabilization until the end of the series (mean 37.8\u2009\u00b1\u200910.8\u00a0min).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>RA-TKA with the MAKO system follows a structured learning curve with early achievement of proficiency after 11 cases. Total surgical time and pin placement reached optimized performance by mid-series, whereas robotic workflow tasks required a longer consolidation period, likely influenced by patient-specific anatomical variability. These findings support RA-TKA as a safe and effective tool, offering rapid surgeon adaptation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Level of evidence<\/jats:title>\n                    <jats:p>IV.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1007\/s00590-026-04803-0","type":"journal-article","created":{"date-parts":[[2026,6,8]],"date-time":"2026-06-08T06:31:56Z","timestamp":1780900316000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Learning curve in image-based robotic assisted total knee arthroplasty: a MAKO-robot experience"],"prefix":"10.1007","volume":"36","author":[{"given":"Ferdinando","family":"Granata","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Francesco","family":"Bosco","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Claudio Domenico","family":"Cobisi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Federico","family":"Nasca","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Riccardo","family":"Giai Via","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mariazzurra","family":"Carlino","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jonathan M.","family":"Vigdorchik","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Carmelo","family":"Burgio","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,6,8]]},"reference":[{"issue":"3","key":"4803_CR1","doi-asserted-by":"publisher","first-page":"594","DOI":"10.1016\/j.arth.2022.10.011","volume":"38","author":"MJ DeFrance","year":"2023","unstructured":"DeFrance MJ, Scuderi GR (2023) Are 20% of patients actually dissatisfied following total knee arthroplasty? 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