Understanding spinopelvic mechanics is important for the success of total hip arthroplasty (THA). Despite significant advancements in appreciating spinopelvic balance, numerous challenges remain. It is crucial to recognize the individual variability and postoperative changes in spinopelvic parameters and their consequential impact on prosthetic component positioning to mitigate the risk of dislocation and enhance postoperative outcomes. This review describes the integration of advanced diagnostic approaches, enhanced technology, implant considerations, and surgical planning, all tailored to the unique anatomy and biomechanics of each patient. It underscores the importance of accurately predicting postoperative spinopelvic mechanics, selecting suitable imaging techniques, establishing a consistent nomenclature for spinopelvic stiffness, and considering implant-specific strategies. Furthermore, it highlights the potential of artificial intelligence to personalize care. Cite this article: Bone Joint J 2024;106-B(11):1206–1215

Aims

This multicentre retrospective observational study’s aims were to investigate whether there are differences in the occurrence of radiolucent lines (RLLs) following total knee arthroplasty (TKA) between the conventional Attune baseplate and its successor, the novel Attune S+, independent from other potentially influencing factors; and whether tibial baseplate design and presence of RLLs are associated with differing risk of revision.

Aims

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.

Aims. Machine learning (ML), a branch of artificial intelligence that uses algorithms to learn from data and make predictions, offers a pathway towards more personalized and tailored surgical treatments. This approach is particularly relevant to prevalent joint diseases such as osteoarthritis (OA). In contrast to end-stage disease, where joint arthroplasty provides excellent results, early stages of OA currently lack effective therapies to halt or reverse progression. Accurate prediction of OA progression is crucial if timely interventions are to be developed, to enhance patient care and optimize the design of clinical trials. Methods. A systematic review was conducted in accordance with PRISMA guidelines. We searched MEDLINE and Embase on 5 May 2024 for studies utilizing ML to predict OA progression. Titles and abstracts were independently screened, followed by full-text reviews for studies that met the eligibility criteria. Key information was extracted and synthesized for analysis, including types of data (such as clinical, radiological, or biochemical), definitions of OA progression, ML algorithms, validation methods, and outcome measures. Results. Out of 1,160 studies initially identified, 39 were included. Most studies (85%) were published between 2020 and 2024, with 82% using publicly available datasets, primarily the Osteoarthritis Initiative. ML methods were predominantly supervised, with significant variability in the definitions of OA progression: most studies focused on structural changes (59%), while fewer addressed pain progression or both. Deep learning was used in 44% of studies, while automated ML was used in 5%. There was a lack of standardization in evaluation metrics and limited external validation. Interpretability was explored in 54% of studies, primarily using SHapley Additive exPlanations. Conclusion. Our systematic review demonstrates the feasibility of ML models in predicting OA progression, but also uncovers critical limitations that currently restrict their clinical applicability. Future priorities should include diversifying data sources, standardizing outcome measures, enforcing rigorous validation, and integrating more sophisticated algorithms. This paradigm shift from predictive modelling to actionable clinical tools has the potential to transform patient care and disease management in orthopaedic practice. Cite this article: Bone Joint J 2024;106-B(11):1216–1222

Aims. 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. Methods. 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. Results. For THA, there were 5,558 patient radiology reports included, of which 4,137 were used for model training and testing, and 1,421 for external validation. Following training, model performance demonstrated average (mean across three folds) accuracy, F1 score, and area under the receiver operating curve (AUROC) values of 0.850 (95% confidence interval (CI) 0.833 to 0.867), 0.813 (95% CI 0.785 to 0.841), and 0.847 (95% CI 0.822 to 0.872), respectively. For TKA, 7,457 patient radiology reports were included, with 3,478 used for model training and testing, and 3,152 for external validation. Performance metrics included accuracy, F1 score, and AUROC values of 0.757 (95% CI 0.702 to 0.811), 0.543 (95% CI 0.479 to 0.607), and 0.717 (95% CI 0.657 to 0.778) respectively. There was a notable deterioration in performance on external validation in both cohorts. Conclusion. The use of routinely available preoperative radiology reports provides promising potential to help screen suitable candidates for THA, but not for TKA. The external validation results demonstrate the importance of further model testing and training when confronted with new clinical cohorts. Cite this article: Bone Joint J 2024;106-B(7):688–695

Aims

To identify unanswered questions about the prevention, diagnosis, treatment, and rehabilitation and delivery of care of first-time soft-tissue knee injuries (ligament injuries, patella dislocations, meniscal injuries, and articular cartilage) in children (aged 12 years and older) and adults.

The OpenAI chatbot ChatGPT is an artificial intelligence (AI) application that uses state-of-the-art language processing AI. It can perform a vast number of tasks, from writing poetry and explaining complex quantum mechanics, to translating language and writing research articles with a human-like understanding and legitimacy. Since its initial release to the public in November 2022, ChatGPT has garnered considerable attention due to its ability to mimic the patterns of human language, and it has attracted billion-dollar investments from Microsoft and PricewaterhouseCoopers. The scope of ChatGPT and other large language models appears infinite, but there are several important limitations. This editorial provides an introduction to the basic functionality of ChatGPT and other large language models, their current applications and limitations, and the associated implications for clinical practice and research. Cite this article: Bone Joint J 2023;105-B(6):587–589

Aims

The aim of this study was to identify factors associated with five-year cancer-related mortality in patients with limb and trunk soft-tissue sarcoma (STS) and develop and validate machine learning algorithms in order to predict five-year cancer-related mortality in these patients.

Benefits of early stabilization of femoral shaft fractures, in mitigation of pulmonary and other complications, have been recognized over the past decades. Investigation into the appropriate level of resuscitation, and other measures of readiness for definitive fixation, versus a damage control strategy have been ongoing. These principles are now being applied to fractures of the thoracolumbar spine, pelvis, and acetabulum. Systems of trauma care are evolving to encompass attention to expeditious and safe management of not only multiply injured patients with these major fractures, but also definitive care for hip and periprosthetic fractures, which pose a similar burden of patient recumbency until stabilized. Future directions regarding refinement of patient resuscitation, assessment, and treatment are anticipated, as is the potential for data sharing and registries in enhancing trauma system functionality.

Cite this article: Bone Joint J 2023;105-B(4):361–364.

Recent publications have drawn attention to the fact that some brands of joint replacement may contain variants which perform significantly worse (or better) than their ‘siblings’. As a result, the National Joint Registry has performed much more detailed analysis on the larger families of knee arthroplasties in order to identify exactly where these differences may be present and may hitherto have remained hidden. The analysis of the Nexgen knee arthroplasty brand identified that some posterior-stabilized combinations have particularly high revision rates for aseptic loosening of the tibia, and consequently a medical device recall has been issued for the Nexgen ‘option’ tibial component which was implicated. More elaborate signal detection is required in order to identify such variation in results in a routine fashion if patients are to be protected from such variation in outcomes between closely related implant types.

Cite this article: Bone Joint J 2023;105-B(3):221–226.

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