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. A total of 780 patients (39% male; median age 70.7 years (IQR 62.0 to 77.2)) underwent cemented TKA using the Attune Knee System) at five centres, and with the latest radiograph available for the evaluation of RLL at between six and 36 months from surgery. Univariate and multivariate logistic regression models were performed to assess associations between patient and implant-associated factors on the presence of tibial and femoral RLLs. Differences in revision risk depending on RLLs and tibial baseplate design were investigated with the log-rank test.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
The use of artificial intelligence (AI) is rapidly growing across many domains, of which the medical field is no exception. AI is an umbrella term defining the practical application of algorithms to generate useful output, without the need of human cognition. Owing to the expanding volume of patient information collected, known as ‘big data’, AI is showing promise as a useful tool in healthcare research and across all aspects of patient care pathways. Practical applications in orthopaedic surgery include: diagnostics, such as fracture recognition and tumour detection; predictive models of clinical and patient-reported outcome measures, such as calculating mortality rates and length of hospital stay; and real-time rehabilitation monitoring and surgical training. However, clinicians should remain cognizant of AI’s limitations, as the development of robust reporting and validation frameworks is of paramount importance to prevent avoidable errors and biases. The aim of this review article is to provide a comprehensive understanding of AI and its subfields, as well as to delineate its existing clinical applications in trauma and orthopaedic surgery. Furthermore, this narrative review expands upon the limitations of AI and future direction. Cite this article:
The OpenAI chatbot ChatGPT is an
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