Literature surrounding artificial intelligence (AI)-related applications for hip and knee arthroplasty has proliferated. However, meaningful advances that fundamentally transform the practice and delivery of joint arthroplasty are yet to be realized, despite the broad range of applications as we continue to search for meaningful and appropriate use of AI. AI literature in hip and knee arthroplasty between 2018 and 2021 regarding image-based analyses, value-based care, remote patient monitoring, and augmented reality was reviewed. Concerns surrounding meaningful use and appropriate methodological approaches of AI in joint arthroplasty research are summarized. Of the 233 AI-related orthopaedics articles published, 178 (76%) constituted original research, while the rest consisted of editorials or reviews. A total of 52% of original AI-related research concerns hip and knee arthroplasty (n = 92), and a narrative review is described. Three studies were externally validated. Pitfalls surrounding present-day research include conflating vernacular (“AI/machine learning”), repackaging limited registry data, prematurely releasing internally validated prediction models, appraising model architecture instead of inputted data, withholding code, and evaluating studies using antiquated regression-based guidelines. While AI has been applied to a variety of hip and knee arthroplasty applications with limited clinical impact, the future remains promising if the question is meaningful, the methodology is rigorous and transparent, the data are rich, and the model is externally validated. Simple checkpoints for meaningful AI adoption include ensuring applications focus on: administrative support over clinical evaluation and management; necessity of the advanced model; and the novelty of the question being answered. Cite this article:
This systematic review aims to compare the precision of component positioning, patient-reported outcome measures (PROMs), complications, survivorship, cost-effectiveness, and learning curves of MAKO robotic arm-assisted unicompartmental knee arthroplasty (RAUKA) with manual medial unicompartmental knee arthroplasty (mUKA). Searches of PubMed, MEDLINE, and Google Scholar were performed in November 2021 according to the Preferred Reporting Items for Systematic Review and Meta-Analysis statement. Search terms included “robotic”, “unicompartmental”, “knee”, and “arthroplasty”. Published clinical research articles reporting the learning curves and cost-effectiveness of MAKO RAUKA, and those comparing the component precision, functional outcomes, survivorship, or complications with mUKA, were included for analysis.Aims
Methods
The goal of the current systematic review was to assess the impact of implant placement accuracy on outcomes following total knee arthroplasty (TKA). A systematic review was performed in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines using the Ovid Medline, Embase, Cochrane Central, and Web of Science databases in order to assess the impact of the patient-reported outcomes measures (PROMs) and implant placement accuracy on outcomes following TKA. Studies assessing the impact of implant alignment, rotation, size, overhang, or condylar offset were included. Study quality was assessed, evidence was graded (one-star: no evidence, two-star: limited evidence, three-star: moderate evidence, four-star: strong evidence), and recommendations were made based on the available evidence.Aims
Methods
The aims of this systematic review were to assess the learning curve of semi-active robotic arm-assisted total hip arthroplasty (rTHA), and to compare the accuracy, patient-reported functional outcomes, complications, and survivorship between rTHA and manual total hip arthroplasty (mTHA). Searches of PubMed, Medline, and Google Scholar were performed in April 2020 in line with the Preferred Reporting Items for Systematic Review and Meta-Analysis statement. Search terms included “robotic”, “hip”, and “arthroplasty”. The criteria for inclusion were published clinical research articles reporting the learning curve for rTHA (robotic arm-assisted only) and those comparing the implantation accuracy, functional outcomes, survivorship, or complications with mTHA.Aims
Methods
Computer-based applications are increasingly being used by orthopaedic surgeons in their clinical practice. With the integration of technology in surgery, augmented reality (AR) may become an important tool for surgeons in the future. By superimposing a digital image on a user’s view of the physical world, this technology shows great promise in orthopaedics. The aim of this review is to investigate the current and potential uses of AR in orthopaedics. A systematic review of the PubMed, MEDLINE, and Embase databases up to January 2019 using the keywords ‘orthopaedic’ OR ‘orthopedic AND augmented reality’ was performed by two independent reviewers.Aims
Materials and Methods
The application of robotics in the operating theatre for knee arthroplasty remains controversial. As with all new technology, the introduction of new systems might be associated with a learning curve. However, guidelines on how to assess the introduction of robotics in the operating theatre are lacking. This systematic review aims to evaluate the current evidence on the learning curve of robot-assisted knee arthroplasty. An extensive literature search of PubMed, Medline, Embase, Web of Science, and Cochrane Library was conducted. Randomized controlled trials, comparative studies, and cohort studies were included. Outcomes assessed included: time required for surgery, stress levels of the surgical team, complications in regard to surgical experience level or time needed for surgery, size prediction of preoperative templating, and alignment according to the number of knee arthroplasties performed. A total of 11 studies met the inclusion criteria. Most were of medium to low quality. The operating time of robot-assisted total knee arthroplasty (TKA) and unicompartmental knee arthroplasty (UKA) is associated with a learning curve of between six to 20 cases and six to 36 cases respectively. Surgical team stress levels show a learning curve of seven cases in TKA and six cases for UKA. Experience with the robotic systems did not influence implant positioning, preoperative planning, and postoperative complications. Robot-assisted TKA and UKA is associated with a learning curve regarding operating time and surgical team stress levels. Future evaluation of robotics in the operating theatre should include detailed measurement of the various aspects of the total operating time, including total robotic time and time needed for preoperative planning. The prior experience of the surgical team should also be evaluated and reported. Cite this article:
