Advanced 3D imaging and CT-based navigation have emerged as valuable tools to use in total knee arthroplasty (TKA), for both preoperative planning and the intraoperative execution of different philosophies of alignment. Preoperative planning using CT-based 3D imaging enables more accurate prediction of the size of components, enhancing surgical workflow and optimizing the precision of the positioning of components. Surgeons can assess alignment, osteophytes, and arthritic changes better. These scans provide improved insights into the patellofemoral joint and facilitate tibial sizing and the evaluation of implant-bone contact area in cementless TKA. Preoperative CT imaging is also required for the development of patient-specific instrumentation cutting guides, aiming to reduce intraoperative blood loss and improve the surgical technique in complex cases. Intraoperative CT-based navigation and haptic guidance facilitates precise execution of the preoperative plan, aiming for optimal positioning of the components and accurate alignment, as determined by the surgeon’s philosophy. It also helps reduce iatrogenic injury to the periarticular soft-tissue structures with subsequent reduction in the local and systemic inflammatory response, enhancing early outcomes. Despite the increased costs and radiation exposure associated with CT-based navigation, these many benefits have facilitated the adoption of imaged based robotic surgery into routine practice. Further research on ultra-low-dose CT scans and exploration of the possible translation of the use of 3D imaging into improved clinical outcomes are required to justify its broader implementation. Cite this article:
Robotic-assisted total knee arthroplasty (TKA) has proven higher accuracy, fewer alignment outliers, and improved short-term clinical outcomes when compared to conventional TKA. However, evidence of cost-effectiveness and individual superiority of one system over another is the subject of further research. Despite its growing adoption rate, published results are still limited and comparative studies are scarce. This review compares characteristics and performance of five currently available systems, focusing on the information and feedback each system provides to the surgeon, what the systems allow the surgeon to modify during the operation, and how each system then aids execution of the surgical plan. Cite this article: Abstract
Dislocation following total hip arthroplasty (THA) is a well-known and potentially devastating complication. Clinicians have used many strategies in attempts to prevent dislocation since the introduction of THA. While the importance of postoperative care cannot be ignored, particular emphasis has been placed on preoperative planning in the prevention of dislocation. The strategies have progressed from more traditional approaches, including modular implants, the size of the femoral head, and augmentation of the offset, to newer concepts, including patient-specific component positioning combined with
In recent years, machine learning (ML) and artificial neural networks (ANNs), a particular subset of ML, have been adopted by various areas of healthcare. A number of diagnostic and prognostic algorithms have been designed and implemented across a range of orthopaedic sub-specialties to date, with many positive results. However, the methodology of many of these studies is flawed, and few compare the use of ML with the current approach in clinical practice. Spinal surgery has advanced rapidly over the past three decades, particularly in the areas of implant technology, advanced surgical techniques, biologics, and enhanced recovery protocols. It is therefore regarded an innovative field. Inevitably, spinal surgeons will wish to incorporate ML into their practice should models prove effective in diagnostic or prognostic terms. The purpose of this article is to review published studies that describe the application of neural networks to spinal surgery and which actively compare ANN models to contemporary clinical standards allowing evaluation of their efficacy, accuracy, and relatability. It also explores some of the limitations of the technology, which act to constrain the widespread adoption of neural networks for diagnostic and prognostic use in spinal care. Finally, it describes the necessary considerations should institutions wish to incorporate ANNs into their practices. In doing so, the aim of this review is to provide a practical approach for spinal surgeons to understand the relevant aspects of neural networks. Cite this article:
Upper limb amputations, ranging from transhumeral to partial hand, can be devastating for patients, their families, and society. Modern paradigm shifts have focused on reconstructive options after upper extremity limb loss, rather than considering the amputation an ablative procedure. Surgical advancements such as targeted muscle reinnervation and regenerative peripheral nerve interface, in combination with technological development of modern prosthetics, have expanded options for patients after amputation. In the near future, advances such as osseointegration, implantable myoelectric sensors, and implantable nerve cuffs may become more widely used and may expand the options for prosthetic integration, myoelectric signal detection, and restoration of sensation. This review summarizes the current advancements in surgical techniques and prosthetics for upper limb amputees. Cite this article:
Previous standards for assessing the reliability
of a measurement tool have lacked consistency. We reviewed the most
current American Society for Testing and Materials and International
Organisation for Standardisation (ISO) recommendations, and propose
an algorithm for orthopaedic surgeons. When assessing a measurement
tool, conditions of the experimental set-up and clear formulae used
to compile the results should be strictly reported. According to
these recent guidelines, accuracy is a confusing word with an overly
broad meaning and should therefore be abandoned. Depending on the
experimental conditions, one should be referring to bias (when the study
protocol involves accepted reference values), and repeatability
(sr, r) or reproducibility (SR, R). In the absence of accepted reference
values, only repeatability (sr, r) or reproducibility (SR, R) should
be provided. Take home message: Assessing the reliability of a measurement
tool involves reporting bias, repeatability and/or reproducibility
depending on the defined conditions, instead of precision or accuracy. Cite this article:
