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:
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
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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
The risk factors for recurrent instability (RI) following a primary traumatic anterior shoulder dislocation (PTASD) remain unclear. In this study, we aimed to determine the rate of RI in a large cohort of patients managed nonoperatively after PTASD and to develop a clinical prediction model. A total of 1,293 patients with PTASD managed nonoperatively were identified from a trauma database (mean age 23.3 years (15 to 35); 14.3% female). We assessed the prevalence of RI, and used multivariate regression modelling to evaluate which demographic- and injury-related factors were independently predictive for its occurrence.Aims
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Despite the vast quantities of published artificial intelligence (AI) algorithms that target trauma and orthopaedic applications, very few progress to inform clinical practice. One key reason for this is the lack of a clear pathway from development to deployment. In order to assist with this process, we have developed the Clinical Practice Integration of Artificial Intelligence (CPI-AI) framework – a five-stage approach to the clinical practice adoption of AI in the setting of trauma and orthopaedics, based on the IDEAL principles ( Cite this article:
Rotator cuff tear (RCT) is the leading cause of shoulder pain, primarily associated with age-related tendon degeneration. This study aimed to elucidate the potential differential gene expressions in tendons across different age groups, and to investigate their roles in tendon degeneration. Linear regression and differential expression (DE) analyses were performed on two transcriptome profiling datasets of torn supraspinatus tendons to identify age-related genes. Subsequent functional analyses were conducted on these candidate genes to explore their potential roles in tendon ageing. Additionally, a secondary DE analysis was performed on candidate genes by comparing their expressions between lesioned and normal tendons to explore their correlations with RCTs.Aims
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Aims. Precise implant positioning, tailored to individual spinopelvic biomechanics and phenotype, is paramount for stability in total hip arthroplasty (THA). Despite a few studies on instability prediction, there is a notable gap in research utilizing artificial intelligence (AI). The objective of our pilot study was to evaluate the feasibility of developing an AI algorithm tailored to individual spinopelvic mechanics and patient phenotype for predicting impingement. Methods. This international, multicentre prospective cohort study across two centres encompassed 157 adults undergoing primary robotic arm-assisted THA. Impingement during specific flexion and extension stances was identified using the virtual range of motion (ROM) tool of the robotic software. The primary AI model, the Light Gradient-Boosting Machine (LGBM), used tabular data to predict impingement presence, direction (flexion or extension), and type. A secondary model integrating tabular data with plain anteroposterior pelvis radiographs was evaluated to assess for any potential enhancement in prediction accuracy. Results. We identified nine predictors from an analysis of baseline spinopelvic characteristics and surgical planning parameters. Using fivefold cross-validation, the LGBM achieved 70.2% impingement prediction accuracy. With impingement data, the LGBM estimated direction with 85% accuracy, while the support vector machine (SVM) determined impingement type with 72.9% accuracy. After integrating imaging data with a multilayer perceptron (tabular) and a convolutional neural network (radiograph), the LGBM’s prediction was 68.1%. Both combined and LGBM-only had similar impingement direction prediction rates (around 84.5%). Conclusion. This study is a pioneering effort in leveraging AI for impingement prediction in THA, utilizing a comprehensive, real-world clinical dataset. Our machine-learning algorithm demonstrated promising accuracy in predicting impingement, its type, and direction. While the addition of imaging data to our deep-learning algorithm did not boost accuracy, the potential for refined annotations, such as landmark markings, offers avenues for future enhancement. Prior to clinical integration,
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
Obtaining solid implant fixation is crucial in revision total knee arthroplasty (rTKA) to avoid aseptic loosening, a major reason for re-revision. This study aims to validate a novel grading system that quantifies implant fixation across three anatomical zones (epiphysis, metaphysis, diaphysis). Based on pre-, intra-, and postoperative assessments, the novel grading system allocates a quantitative score (0, 0.5, or 1 point) for the quality of fixation achieved in each anatomical zone. The criteria used by the algorithm to assign the score include the bone quality, the size of the bone defect, and the type of fixation used. A consecutive cohort of 245 patients undergoing rTKA from 2012 to 2018 were evaluated using the current novel scoring system and followed prospectively. In addition, 100 first-time revision cases were assessed radiologically from the original cohort and graded by three observers to evaluate the intra- and inter-rater reliability of the novel radiological grading system.Aims
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Aims. This study aimed to explore the biological and clinical importance of dysregulated key genes in osteoarthritis (OA) patients at the cartilage level to find potential biomarkers and targets for diagnosing and treating OA. Methods. Six sets of gene expression profiles were obtained from the Gene Expression Omnibus database. Differential expression analysis, weighted gene coexpression network analysis (WGCNA), and multiple machine-learning algorithms were used to screen crucial genes in osteoarthritic cartilage, and genome enrichment and functional annotation analyses were used to decipher the related categories of gene function. Single-sample gene set enrichment analysis was performed to analyze immune cell infiltration. Correlation analysis was used to explore the relationship among the hub genes and immune cells, as well as markers related to articular cartilage degradation and bone mineralization. Results. A total of 46 genes were obtained from the intersection of significantly upregulated genes in osteoarthritic cartilage and the key module genes screened by WGCNA. Functional annotation analysis revealed that these genes were closely related to pathological responses associated with OA, such as inflammation and immunity. Four key dysregulated genes (cartilage acidic protein 1 (CRTAC1), iodothyronine deiodinase 2 (DIO2), angiopoietin-related protein 2 (ANGPTL2), and MAGE family member D1 (MAGED1)) were identified after using machine-learning algorithms. These genes had high diagnostic value in both the training cohort and
This study aimed to compare the performance of survival prediction models for bone metastases of the extremities (BM-E) with pathological fractures in an Asian cohort, and investigate patient characteristics associated with survival. This retrospective cohort study included 469 patients, who underwent surgery for BM-E between January 2009 and March 2022 at a tertiary hospital in South Korea. Postoperative survival was calculated using the PATHFx3.0, SPRING13, OPTIModel, SORG, and IOR models. Model performance was assessed with area under the curve (AUC), calibration curve, Brier score, and decision curve analysis. Cox regression analyses were performed to evaluate the factors contributing to survival.Aims
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Aims. Machine-learning (ML) prediction models in orthopaedic trauma hold great promise in assisting clinicians in various tasks, such as personalized risk stratification. However, an overview of current applications and critical appraisal to peer-reviewed guidelines is lacking. The objectives of this study are to 1) provide an overview of current ML prediction models in orthopaedic trauma; 2) evaluate the completeness of reporting following the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement; and 3) assess the risk of bias following the Prediction model Risk Of Bias Assessment Tool (PROBAST) tool. Methods. A systematic search screening 3,252 studies identified 45 ML-based prediction models in orthopaedic trauma up to January 2023. The TRIPOD statement assessed transparent reporting and the PROBAST tool the risk of bias. Results. A total of 40 studies reported on training and internal validation; four studies performed both development and
To map the Oxford Knee Score (OKS) and High Activity Arthroplasty Score (HAAS) items to a common scale, and to investigate the psychometric properties of this new scale for the measurement of knee health. Patient-reported outcome measure (PROM) data measuring knee health were obtained from the NHS PROMs dataset and Total or Partial Knee Arthroplasty Trial (TOPKAT). Assumptions for common scale modelling were tested. A graded response model (fitted to OKS item responses in the NHS PROMs dataset) was used as an anchor to calibrate paired HAAS items from the TOPKAT dataset. Information curves for the combined OKS-HAAS model were plotted. Bland-Altman analysis was used to compare common scale scores derived from OKS and HAAS items. A conversion table was developed to map between HAAS, OKS, and the common scale.Aims
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The October 2023 Hip & Pelvis Roundup360 looks at: Femoroacetabular impingement syndrome at ten years – how do athletes do?; Venous thromboembolism in patients following total joint replacement: are transfusions to blame?; What changes in pelvic sagittal tilt occur 20 years after total hip arthroplasty?; Can stratified care in hip arthroscopy predict successful and unsuccessful outcomes?; Hip replacement into your nineties; Can large language models help with follow-up?; The most taxing of revisions – proximal femoral replacement for periprosthetic joint infection – what’s the benefit of dual mobility?
The October 2023 Spine Roundup360 looks at: Cutting through surgical smoke: the science of cleaner air in spinal operations; Unlocking success: key factors in thoracic spine decompression and fusion for ossification of the posterior longitudinal ligament; Deep learning algorithm for identifying cervical cord compression due to degenerative canal stenosis on radiography; Surgeon experience influences robotics learning curve for minimally invasive lumbar fusion; Decision-making algorithm for the surgical treatment of degenerative lumbar spondylolisthesis of L4/L5; Response to preoperative steroid injections predicts surgical outcomes in patients undergoing fusion for isthmic spondylolisthesis.
This study aimed to develop and validate a fully automated system that quantifies proximal femoral bone mineral density (BMD) from CT images. The study analyzed 978 pairs of hip CT and dual-energy X-ray absorptiometry (DXA) measurements of the proximal femur (DXA-BMD) collected from three institutions. From the CT images, the femur and a calibration phantom were automatically segmented using previously trained deep-learning models. The Hounsfield units of each voxel were converted into density (mg/cm3). Then, a deep-learning model trained by manual landmark selection of 315 cases was developed to select the landmarks at the proximal femur to rotate the CT volume to the neutral position. Finally, the CT volume of the femur was projected onto the coronal plane, and the areal BMD of the proximal femur (CT-aBMD) was quantified. CT-aBMD correlated to DXA-BMD, and a receiver operating characteristic (ROC) analysis quantified the accuracy in diagnosing osteoporosis.Aims
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The August 2023 Hip & Pelvis Roundup360 looks at: Using machine learning to predict venous thromboembolism and major bleeding events following total joint arthroplasty; Antibiotic length in revision total hip arthroplasty; Preoperative colonization and worse outcomes; Short stem cemented total hip arthroplasty; What are the outcomes of one- versus two-stage revisions in the UK?; To cement or not to cement? The best approach in hemiarthroplasty; Similar re-revisions in cemented and cementless femoral revisions for periprosthetic femoral fractures in total hip arthroplasty; Are hip precautions still needed?
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:
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. Methods. Demographic, clinicopathological, and treatment variables of limb and trunk STS patients in the Surveillance, Epidemiology, and End Results Program (SEER) database from 2004 to 2017 were analyzed. Multivariable logistic regression was used to determine factors significantly associated with five-year cancer-related mortality. Various machine learning models were developed and compared using area under the curve (AUC), calibration, and decision curve analysis. The model that performed best on the SEER testing data was further assessed to determine the variables most important in its predictive capacity. This model was externally validated using our institutional dataset. Results. A total of 13,646 patients with STS from the SEER database were included, of whom 35.9% experienced five-year cancer-related mortality. The random forest model performed the best overall and identified tumour size as the most important variable when predicting mortality in patients with STS, followed by M stage, histological subtype, age, and surgical excision. Each variable was significant in logistic regression.
