Aims. Short-stemmed femoral implants have been used for total hip arthroplasty (THA) in young and active patients to conserve bone, provide physiological loading, and reduce the incidence of thigh pain. Only short- to mid-term results have been presented and there have been concerns regarding component malalignment, incorrect sizing, and subsidence. This systematic review reports clinical and radiological outcomes, complications, revision rates, and implant survival in THA using short-stemmed femoral components. Materials and Methods. A literature review was performed using the EMBASE, Medline, and Cochrane databases. Strict inclusion and exclusion criteria were used to identify studies reporting clinical and radiological follow-up for short-stemmed hip arthroplasties. Results. A total of 28 studies were eligible for inclusion. This included 5322 hips in 4657 patients with a mean age of 59 years (13 to 94). The mean follow-up was 6.1 years (0.5 to 20). The mean Harris Hip Score improved from 46 (0 to 100) to 92 (39 to 100). The mean Oxford Hip Score improved from 25 (2 to 42.5) to 35 (12.4 to 48). The mean Western Ontario & McMaster Universities Osteoarthritis Index improved from 54 (2 to 95) to 22 (0 to 98). Components were aligned in a neutral coronal alignment in up to 90.9% of cases. A total of 15 studies reported component survivorship, which was 98.6% (92% to 100%) at a mean follow-up of 12.1 years. Conclusion. Short-stemmed femoral implants show similar improvement in clinical and radiological outcomes compared with conventional length implants. Only mid-term survivorship, however, is known. An abundance of short components have been developed and used commercially without staged clinical trials. Long-term survival is still unknown for many of these components. There remains tension between innovation and the moral duty to ensure that the introduction of new implants is controlled until safety and patient benefit are demonstrated. Implant innovation and subsequent use should be driven by proven clinical outcomes, rather than market and financial forces, and ethical practice must be ensured. Cite this article: Bone Joint J 2019;101-B:502–511

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: Bone Joint J 2022;104-B(12):1292–1303.

Aims

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).

Aims

The goal of the current systematic review was to assess the impact of implant placement accuracy on outcomes following total knee arthroplasty (TKA).

Aims

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).

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: Bone Joint J 2020;102-B(4):407–413.