The purpose of this study was to develop a convolutional neural network (CNN) for fracture detection, classification, and identification of greater tuberosity displacement ≥ 1 cm, neck-shaft angle (NSA) ≤ 100°, shaft translation, and articular fracture involvement, on plain radiographs. The CNN was trained and tested on radiographs sourced from 11 hospitals in Australia and externally validated on radiographs from the Netherlands. Each radiograph was paired with corresponding CT scans to serve as the reference standard based on dual independent evaluation by trained researchers and attending orthopaedic surgeons. Presence of a fracture, classification (non- to minimally displaced; two-part, multipart, and glenohumeral dislocation), and four characteristics were determined on 2D and 3D CT scans and subsequently allocated to each series of radiographs. Fracture characteristics included greater tuberosity displacement ≥ 1 cm, NSA ≤ 100°, shaft translation (0% to < 75%, 75% to 95%, > 95%), and the extent of articular involvement (0% to < 15%, 15% to 35%, or > 35%).Aims
Methods
The prevalence of osteoarthritis (OA) associated with instability of the shoulder ranges between 4% and 60%. Articular cartilage is, however, routinely assessed in these patients using radiographs or scans (2D or 3D), with little opportunity to record early signs of cartilage damage. The aim of this study was to assess the prevalence and localization of chondral lesions and synovial damage in patients undergoing arthroscopic surgery for instablility of the shoulder, in order to classify them and to identify risk factors for the development of glenohumeral OA. A total of 140 shoulders in 140 patients with a mean age of 28.5 years (15 to 55), who underwent arthroscopic treatment for recurrent glenohumeral instability, were included. The prevalence and distribution of chondral lesions and synovial damage were analyzed and graded into stages according to the division of the humeral head and glenoid into quadrants. The following factors that might affect the prevalence and severity of chondral damage were recorded: sex, dominance, age, age at the time of the first dislocation, number of dislocations, time between the first dislocation and surgery, preoperative sporting activity, Beighton score, type of instability, and joint laxity.Aims
Methods
Cemented hemiarthroplasty is an effective form of treatment for most patients with an intracapsular fracture of the hip. However, it remains unclear whether there are subgroups of patients who may benefit from the alternative operation of a modern uncemented hemiarthroplasty – the aim of this study was to investigate this issue. Knowledge about the heterogeneity of treatment effects is important for surgeons in order to target operations towards specific subgroups who would benefit the most. We used causal forest analysis to compare subgroup- and individual-level treatment effects between cemented and modern uncemented hemiarthroplasty in patients aged > 60 years with an intracapsular fracture of the hip, using data from the World Hip Trauma Evaluation 5 (WHiTE 5) multicentre randomized clinical trial. EuroQol five-dimension index scores were used to measure health-related quality of life at one, four, and 12 months postoperatively.Aims
Methods
To examine whether natural language processing (NLP) using a clinically based large language model (LLM) could be used to predict patient selection for total hip or total knee arthroplasty (THA/TKA) from routinely available free-text radiology reports. Data pre-processing and analyses were conducted according to the Artificial intelligence to Revolutionize the patient Care pathway in Hip and knEe aRthroplastY (ARCHERY) project protocol. This included use of de-identified Scottish regional clinical data of patients referred for consideration of THA/TKA, held in a secure data environment designed for artificial intelligence (AI) inference. Only preoperative radiology reports were included. NLP algorithms were based on the freely available GatorTron model, a LLM trained on over 82 billion words of de-identified clinical text. Two inference tasks were performed: assessment after model-fine tuning (50 Epochs and three cycles of k-fold cross validation), and external validation.Aims
Methods
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:
We aim to explore the potential technologies for monitoring and assessment of patients undergoing arthroplasty by examining selected literature focusing on the technology currently available and reflecting on possible future development and application. The reviewed literature indicates a large variety of different hardware and software, widely available and used in a limited manner, to assess patients’ performance. There are extensive opportunities to enhance and integrate the systems which are already in existence to develop patient-specific pathways for rehabilitation. Cite this article:
The aim of this study was to estimate the 90-day periprosthetic joint infection (PJI) rates following total knee arthroplasty (TKA) and total hip arthroplasty (THA) for osteoarthritis (OA). This was a data linkage study using the New South Wales (NSW) Admitted Patient Data Collection (APDC) and the Australian Orthopaedic Association National Joint Replacement Registry (AOANJRR), which collect data from all public and private hospitals in NSW, Australia. Patients who underwent a TKA or THA for OA between 1 January 2002 and 31 December 2017 were included. The main outcome measures were 90-day incidence rates of hospital readmission for: revision arthroplasty for PJI as recorded in the AOANJRR; conservative definition of PJI, defined by T84.5, the PJI diagnosis code in the APDC; and extended definition of PJI, defined by the presence of either T84.5, or combinations of diagnosis and procedure code groups derived from recursive binary partitioning in the APDC.Aims
Methods
Artificial intelligence (AI) is, in essence, the concept of ‘computer thinking’, encompassing methods that train computers to perform and learn from executing certain tasks, called machine learning, and methods to build intricate computer models that both learn and adapt, called complex neural networks. Computer vision is a function of AI by which machine learning and complex neural networks can be applied to enable computers to capture, analyze, and interpret information from clinical images and visual inputs. This annotation summarizes key considerations and future perspectives concerning computer vision, questioning the need for this technology (the ‘why’), the current applications (the ‘what’), and the approach to unlocking its full potential (the ‘how’). Cite this article:
Total hip arthroplasty (THA) and total knee arthroplasty (TKA) are common orthopaedic procedures requiring postoperative radiographs to confirm implant positioning and identify complications. Artificial intelligence (AI)-based image analysis has the potential to automate this postoperative surveillance. The aim of this study was to prepare a scoping review to investigate how AI is being used in the analysis of radiographs following THA and TKA, and how accurate these tools are. The Embase, MEDLINE, and PubMed libraries were systematically searched to identify relevant articles. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for scoping reviews and Arksey and O’Malley framework were followed. Study quality was assessed using a modified Methodological Index for Non-Randomized Studies tool. AI performance was reported using either the area under the curve (AUC) or accuracy.Aims
Methods
There is increasing popularity in the use of artificial intelligence and
The aim of this study was to investigate whether wear and backside deformation of polyethylene (PE) tibial inserts may influence the cement cover of tibial trays of explanted total knee arthroplasties (TKAs). At our retrieval centre, we measured changes in the wear and deformation of PE inserts using coordinate measuring machines and light microscopy. The amount of cement cover on the backside of tibial trays was quantified as a percentage of the total surface. The study involved data from the explanted fixed-bearing components of four widely used contemporary designs of TKA (Attune, NexGen, Press Fit Condylar (PFC), and Triathlon), revised for any indication, and we compared them with components that used previous generations of PE. Regression modelling was used to identify variables related to the amount of cement cover on the retrieved trays.Aims
Methods
Deep surgical site infection (SSI) is common after lower limb fracture. We compared the diagnosis of deep SSI using alternative methods of data collection and examined the agreement of clinical photography and in-person clinical assessment by the Centers for Disease Control and Prevention (CDC) criteria after lower limb fracture surgery. Data from two large, UK-based multicentre randomized controlled major trauma trials investigating SSI and wound healing after surgical repair of open lower limb fractures that could not be primarily closed (UK WOLLF), and surgical incisions for fractures that were primarily closed (UK WHiST), were examined. Trial interventions were standard wound care management and negative pressure wound therapy after initial surgical debridement. Wound outcomes were collected from 30 days to six weeks. We compared the level of agreement between wound photography and clinical assessment of CDC-defined SSI. We are also assessed the level of agreement between blinded independent assessors of the photographs.Aims
Methods
Aims. This study used an artificial neural network (ANN) model to determine the most important pre- and perioperative variables to predict same-day discharge in patients undergoing total knee arthroplasty (TKA). Methods. Data for this study were collected from the National Surgery Quality Improvement Program (NSQIP) database from the year 2018. Patients who received a primary, elective, unilateral TKA with a diagnosis of primary osteoarthritis were included. Demographic, preoperative, and intraoperative variables were analyzed. The ANN model was compared to a logistic regression model, which is a conventional
The purpose of this study was to develop a personalized outcome prediction tool, to be used with knee arthroplasty patients, that predicts outcomes (lengths of stay (LOS), 90 day readmission, and one-year patient-reported outcome measures (PROMs) on an individual basis and allows for dynamic modifiable risk factors. Data were prospectively collected on all patients who underwent total or unicompartmental knee arthroplasty at a between July 2015 and June 2018. Cohort 1 (n = 5,958) was utilized to develop models for LOS and 90 day readmission. Cohort 2 (n = 2,391, surgery date 2015 to 2017) was utilized to develop models for one-year improvements in Knee Injury and Osteoarthritis Outcome Score (KOOS) pain score, KOOS function score, and KOOS quality of life (QOL) score. Model accuracies within the imputed data set were assessed through cross-validation with root mean square errors (RMSEs) and mean absolute errors (MAEs) for the LOS and PROMs models, and the index of prediction accuracy (IPA), and area under the curve (AUC) for the readmission models. Model accuracies in new patient data sets were assessed with AUC.Aims
Methods
Aims. The aim of this study was to evaluate the ability of a
Aims. To compare the gait of unicompartmental knee arthroplasty (UKA)
and total knee arthroplasty (TKA) patients with healthy controls,
using a
