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Advances in Artificial Intelligence and Robotics in Joint Arthroplasty

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With ever-increasing patient expectations of enduring function and quality-of-life preservation, there exists an ongoing pressure to seek newer and better ways to diagnose and manage individuals with disabling joint pathology. Internationally, most contemporary estimates predict a rapidly increasing demand for joint replacement surgery in the coming two decades — a need that will likely far exceed existing surgical capacity. Current means will need to evolve towards newer / novel approaches to meet the rising demand in a climate whereby cost and resource accountability (including sustainability) and consistent achievement of lasting, high functional, standards will be paramount.

In many realms, technology-assisted surgery has already held the potential to improve multiple points of the arthroplasty patient journey — from initial diagnosis through to medium-term (and possibly longer-term) postoperative functional outcomes. Both artificial intelligence (AI) applications and the use of intra-operative robotics are exciting areas of active development and application.

While they have generated much enthusiasm (and marketing), there is a need for scientists and clinicians alike to ensure that the evidence base that underpins the use of such technologies stays ahead of the enthusiastic hype. While cutting-edge work in surgical robotics aims to improve the precision and performance of operative plans (and hence patient outcomes and satisfaction) on an individual patient level, these complex and highly sophisticated machines bring their own unique set of challenges including training and learning curves, alterations to existing surgical techniques and workflows, and often the considerable associated expense with the purchase and ongoing maintenance.

Artificial intelligence has already been explored in a wide range of applications relevant to the care of arthroplasty patients from diagnosis and imaging interpretation to patient selection and educational metrics, to administrative and cost-funding considerations, to augmented operative planning, and the predictive utility for a number of medical (and non-medical) outcomes. Concerns have been raised previously about the wider generalisability of much of the published AI literature to date, and a lack of reproduction of key findings away from algorithm designer sites or highly specialized quaternary centers.


With guest editing from Prof. Yan Wang (Editor-in-Chief of Arthroplasty) and Dr. Quanbo Ji, the journal launched its first Special Issue “Artificial Intelligence in Joint Arthroplasty” in 2020 and operated successfully. Based on the great support from the authors, reviewers as well as all audiences, more than ten papers have been published in that Article Collection. Coming with the increasing attention of orthopaedic surgeons, we would like to keep up with this eye-catching theme with a new topic of “Advances in Artificial Intelligence and Robotics in Joint Arthroplasty” continuously.                 

This Special Issue of Arthroplasty aims to provide an opportunity for clinical researchers from across the globe to contribute to the advancement of knowledge in the area of contemporary arthroplasty, specifically relating to robotic and AI applications. We seek to bring together a number of high-quality works in these fields to serve as both an informative and educational platform, but also to strengthen the foundation of science that supports the use of these exciting new technologies. All works accepted for publication will undergo a rigorous peer review process.

Scope and specific themes

We encourage diversity of content from both basic science and clinical research spheres. Original clinical research, structured (systematic) reviews and proof-of-concept papers will be considered. Such works may include, but not be limited to:

  • Advancements in robotic technologies (including novel applications) and the clinical evidence that might support wider uptake.
  • Cost and outcome analyses.
  • Head-to-head comparisons between the performance and outcomes of arthroplasty surgery and either conventional approaches and/or computer-navigated methods.
  • Applications with demonstrated benefit to patient-reported outcome measures (i.e., PROMs).
  • Registry-level evidence of the performance and survivorship of robot-assisted arthroplasty procedures.
  • Robotic applications in complex primary and revision surgery.
  • The use of AI in optimized implant prediction/templating.
  • The role of AI in improving patient outcomes.
  • AI utility in optimized patient selection pathways.
  • Demonstration of whole episode-of-care cost savings through the establishment and implementation of AI technologies.
  • The clinical outcomes of the use of validated AI applications in non-specialist centers (i.e., reflecting more generalizable use).

This special issue was published in Arthroplasty.

  1. In total knee arthroplasty (TKA), achieving soft-tissue balance while retaining acceptable lower limb alignment is sometimes difficult and may lead to patient dissatisfaction. Theoretically, patient-specific i...

    Authors: Hanlong Zheng, Mingxue Chen, Dejin Yang, Hongyi Shao and Yixin Zhou
    Citation: Arthroplasty 2024 6:34
  2. In the present study, the surgeon aimed to align the stem at 5° to 25° in anteversion. The robotic technology was used to measure stem anteversion with respect to proximal femur anteversion at different levels...

    Authors: Andrea Marcovigi, Gianluca Grandi, Luca Bianchi, Francesco Zambianchi, Marco Pavesi and Fabio Catani
    Citation: Arthroplasty 2024 6:27
  3. The coronal plane alignment of the knee (CPAK) classification was first developed using long leg radiographs (LLR) and has since been reported using image-based and imageless robotic total knee arthroplasty (T...

    Authors: Adam I. Edelstein, Alexander D. Orsi, Christopher Plaskos, Simon Coffey and Linda I. Suleiman
    Citation: Arthroplasty 2024 6:14
  4. Range of motion (ROM) following total knee replacement (TKR) has been associated with patient satisfaction and knee function, and is also an early indicator of a successful procedure. Robotic-assisted TKR (raT...

    Authors: Camdon Fary, Jason Cholewa, Anna N. Ren, Scott Abshagen, Mike B. Anderson and Krishna Tripuraneni
    Citation: Arthroplasty 2023 5:62
  5. Pre-operative alignment is important for knee procedures including total knee arthroplasty (TKA), especially when considering alternative alignments. The arithmetic Hip Knee Angle (aHKA) is a measure of corona...

    Authors: Tom Jan Gieroba, Sofia Marasco, Sina Babazadeh, Claudia Di Bella and Dirk van Bavel
    Citation: Arthroplasty 2023 5:35
  6. Artificial intelligence (AI) has become involved in many aspects of everyday life, from voice-activated virtual assistants built into smartphones to global online search engines. Similarly, many areas of moder...

    Authors: Andrew P. Kurmis
    Citation: Arthroplasty 2023 5:40
  7. Machine learning is a promising and powerful technology with increasing use in orthopedics. Periprosthetic joint infection following total knee arthroplasty results in increased morbidity and mortality. This s...

    Authors: Yuk Yee Chong, Ping Keung Chan, Vincent Wai Kwan Chan, Amy Cheung, Michelle Hilda Luk, Man Hong Cheung, Henry Fu and Kwong Yuen Chiu
    Citation: Arthroplasty 2023 5:38