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      Fostering Excellence in Knee Arthroplasty: Developing Optimal Patient Care Pathways and Inspiring Knowledge Transfer of Advanced Surgical Techniques

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Osteoarthritis of the knee is common. Early sports trauma or cartilage defects are risk factors for osteoarthritis. If conservative treatment fails, partial or total joint replacement is often performed. A joint replacement aims to restore physiological biomechanics and the quality of life of affected patients. Total knee arthroplasty is one of the most performed surgeries in musculoskeletal medicine. Several developments have taken place over the last decades that have truly altered the way we look at knee arthroplasty today. Some of the fascinating aspects will be presented and discussed in the present narrative review.

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          Probabilistic machine learning and artificial intelligence.

          Zoubin Ghahramani (2015)
          How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.
          Article 0 comments Cited 229 times – based on 0 reviews     Review now
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            The Chitranjan Ranawat award: is neutral mechanical alignment normal for all patients? The concept of constitutional varus.

            Johan Bellemans, William Colyn, Hilde Vandenneucker … (2012)
            Most knee surgeons have believed during TKA neutral mechanical alignment should be restored. A number of patients may exist, however, for whom neutral mechanical alignment is abnormal. Patients with so-called "constitutional varus" knees have had varus alignment since they reached skeletal maturity. Restoring neutral alignment in these cases may in fact be abnormal and undesirable and would likely require some degree of medial soft tissue release to achieve neutral alignment.
            Article 0 comments Cited 217 times – based on 0 reviews     Review now
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              Radiation dose associated with common computed tomography examinations and the associated lifetime attributable risk of cancer.

              Ralph Marcus, Mahadevappa Mahesh, Amy Berrington de Gonzalez … (2009)
              Use of computed tomography (CT) for diagnostic evaluation has increased dramatically over the past 2 decades. Even though CT is associated with substantially higher radiation exposure than conventional radiography, typical doses are not known. We sought to estimate the radiation dose associated with common CT studies in clinical practice and quantify the potential cancer risk associated with these examinations. We conducted a retrospective cross-sectional study describing radiation dose associated with the 11 most common types of diagnostic CT studies performed on 1119 consecutive adult patients at 4 San Francisco Bay Area institutions in California between January 1 and May 30, 2008. We estimated lifetime attributable risks of cancer by study type from these measured doses. Radiation doses varied significantly between the different types of CT studies. The overall median effective doses ranged from 2 millisieverts (mSv) for a routine head CT scan to 31 mSv for a multiphase abdomen and pelvis CT scan. Within each type of CT study, effective dose varied significantly within and across institutions, with a mean 13-fold variation between the highest and lowest dose for each study type. The estimated number of CT scans that will lead to the development of a cancer varied widely depending on the specific type of CT examination and the patient's age and sex. An estimated 1 in 270 women who underwent CT coronary angiography at age 40 years will develop cancer from that CT scan (1 in 600 men), compared with an estimated 1 in 8100 women who had a routine head CT scan at the same age (1 in 11 080 men). For 20-year-old patients, the risks were approximately doubled, and for 60-year-old patients, they were approximately 50% lower. Radiation doses from commonly performed diagnostic CT examinations are higher and more variable than generally quoted, highlighting the need for greater standardization across institutions.
              Article 0 comments Cited 182 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): J Healthc Leadersh
                Journal ID (iso-abbrev): J Healthc Leadersh
                Journal ID (publisher-id): jhl
                Title: Journal of Healthcare Leadership
                Publisher: Dove
                ISSN (Electronic): 1179-3201
                Publication date (Electronic): 21 November 2023
                Publication date Collection: 2023
                Volume: 15
                Pages: 327-338
                Affiliations
                [1 ]Department of Orthopaedic, Trauma, and Reconstructive Surgery, RWTH University Medical Centre , Aachen, 52074, Germany
                [2 ]Department of Orthopedics and Trauma Surgery, Academic Hospital of Bolzano (SABES-ASDAA), Teaching Hospital of Paracelsus Medical University , 39100 Bolzano, Italy
                Author notes
                Correspondence: Filippo Migliorini, Department of Orthopaedic, Trauma, and Reconstructive Surgery, RWTH University Hospital , Pauwelsstraße 30, Aachen, 52074, Germany, Tel +49 0241 80-35529, Email migliorini.md@gmail.com
                Author information
                http://orcid.org/0000-0001-7220-1221
                http://orcid.org/0000-0002-2537-5832
                Article
                Publisher ID: 383916
                DOI: 10.2147/JHL.S383916
                PMC ID: 10676205
                SO-VID: bc91afd8-69f8-4ef2-9025-414a61565b22
                Copyright © © 2023 Migliorini et al.
                License:

                This work is published and licensed by Dove Medical Press Limited. The full terms of this license are available at https://www.dovepress.com/terms.php and incorporate the Creative Commons Attribution – Non Commercial (unported, v3.0) License ( http://creativecommons.org/licenses/by-nc/3.0/). By accessing the work you hereby accept the Terms. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. For permission for commercial use of this work, please see paragraphs 4.2 and 5 of our Terms ( https://www.dovepress.com/terms.php).

                History
                Date received : 12 September 2023
                Date accepted : 13 November 2023
                Page count
                Figures: 1, Tables: 1, References: 146, Pages: 12
                Funding
                Funded by: authors received no financial support for the research, authorship, and/or publication of this article;
                The authors received no financial support for the research, authorship, and/or publication of this article.
                Categories
                Subject: Commentary

                Keywords: knee,arthroplasty,replacement,implant,materials,robotic,artificial intelligence
                Data availability:
                Keywords: knee, arthroplasty, replacement, implant, materials, robotic, artificial intelligence

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