Aims

Machine learning (ML) holds significant promise in optimizing various aspects of total shoulder arthroplasty (TSA), potentially improving patient outcomes and enhancing surgical decision-making. The aim of this systematic review was to identify ML algorithms and evaluate their effectiveness, including those for predicting clinical outcomes and those used in image analysis.

The February 2025 Oncology Roundup. 360. looks at:The role of bone grafting versus bone cement in the treatment of giant cell tumour of bone: a systematic review and meta-analysis on the risk of recurrence in 1,454 patients; Tumour necrosis drives prognosis in osteosarcoma; Correlation between post-chemotherapy MRI and histopathology of malignant bone tumours treated with extra-articular resection; Real-world referral pattern of unplanned excision in patients with soft-tissue sarcoma; Assessment of artificial intelligence chatbot responses to common patient questions on bone sarcoma; Chondrosarcoma of the pelvis and limbs at ten years; Chest wall resection and reconstruction for primary chest wall sarcomas: analysis of survival, predictors of outcome, and long-term functional status; Ewing’s sarcoma in the paediatric population: predictors of survival within the USA; Pulmonary metastasectomy for sarcoma: insights from a referral centre cohort

Aims. The aim of this study was to develop and evaluate a deep learning-based model for classification of hip fractures to enhance diagnostic accuracy. Methods. A retrospective study used 5,168 hip anteroposterior radiographs, with 4,493 radiographs from two institutes (internal dataset) for training and 675 radiographs from another institute for validation. A convolutional neural network (CNN)-based classification model was trained on four types of hip fractures (Displaced, Valgus-impacted, Stable, and Unstable), using DAMO-YOLO for data processing and augmentation. The model’s accuracy, sensitivity, specificity, Intersection over Union (IoU), and Dice coefficient were evaluated. Orthopaedic surgeons’ diagnoses served as the reference standard, with comparisons made before and after artificial intelligence assistance. Results. The accuracy, sensitivity, specificity, IoU, and Dice coefficients of the model for the four fracture categories in the internal dataset were as follows: Displaced (1.0, 0.79, 1.0, 0.70, 0.82), Valgus-impacted (1.0, 0.80, 1.0, 0.70, 0.82), Stable (0.99, 0.95, 0.99, 0.83, 0.89), and Unstable (1.0, 0.98, 0.99, 0.86, 0.92), respectively. For the external validation dataset, the sensitivity and specificity were as follows: Displaced (0.83, 0.94), Valgus-impacted (0.89, 0.90), Stable (0.88, 0.95), and Unstable (0.85, 0.99), respectively. The overall means (Micro AVG and Macro AVG) for the external dataset were Micro AVG (0.83 (SD 0.05), 0.96 (SD 0.01)) and Macro AVG (0.69 (SD 0.02), 0.95 (SD 0.02)), respectively. Conclusion. Compared to human diagnosis alone, our study demonstrates that the developed model significantly improves the accuracy of detecting and classifying hip fractures. Our model has shown great potential in assisting clinicians with the accurate diagnosis and classification of hip fractures. Cite this article: Bone Joint J 2025;107-B(2):213–220

Cite this article: Bone Joint Res 2024;13(12):790–792.

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. The aim of this study was to create artificial intelligence (AI) software with the purpose of providing a second opinion to physicians to support distal radius fracture (DRF) detection, and to compare the accuracy of fracture detection of physicians with and without software support. Methods. The dataset consisted of 26,121 anonymized anterior-posterior (AP) and lateral standard view radiographs of the wrist, with and without DRF. The convolutional neural network (CNN) model was trained to detect the presence of a DRF by comparing the radiographs containing a fracture to the inconspicuous ones. A total of 11 physicians (six surgeons in training and five hand surgeons) assessed 200 pairs of randomly selected digital radiographs of the wrist (AP and lateral) for the presence of a DRF. The same images were first evaluated without, and then with, the support of the CNN model, and the diagnostic accuracy of the two methods was compared. Results. At the time of the study, the CNN model showed an area under the receiver operating curve of 0.97. AI assistance improved the physician’s sensitivity (correct fracture detection) from 80% to 87%, and the specificity (correct fracture exclusion) from 91% to 95%. The overall error rate (combined false positive and false negative) was reduced from 14% without AI to 9% with AI. Conclusion. The use of a CNN model as a second opinion can improve the diagnostic accuracy of DRF detection in the study setting. Cite this article: Bone Joint Res 2024;13(10):588–595

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 (. https://www.ideal-collaboration.net/. ). Adherence to the framework would provide a robust evidence-based mechanism for developing trust in AI applications, where the underlying algorithms are unlikely to be fully understood by clinical teams. Cite this article: Bone Joint Res 2024;13(9):507–512

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, external validation and larger-scale testing of this algorithm are essential. Cite this article: Bone Jt Open 2024;5(8):671–680

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

The June 2024 Research Roundup³⁶⁰ looks at: Do the associations of daily steps with mortality and incident cardiovascular disease differ by sedentary time levels?; Large-scale assessment of ChatGPT in benign and malignant bone tumours imaging report diagnosis and its potential for clinical applications; Long-term effects of diffuse idiopathic skeletal hyperostosis on physical function: a longitudinal analysis; Effect of intramuscular fat in the thigh muscles on muscle architecture and physical performance in the middle-aged females with knee osteoarthritis; Preoperative package of care for osteoarthritis an opportunity not to be missed?; Superiority of kinematic alignment over mechanical alignment in total knee arthroplasty during medium- to long-term follow-up: a meta-analysis and trial sequential analysis.

The June 2024 Wrist & Hand Roundup. 360. looks at: One-year outcomes of the anatomical front and back reconstruction for scapholunate dissociation; Limited intercarpal fusion versus proximal row carpectomy in the treatment of SLAC or SNAC wrist: results after 3.5 years; Prognostic factors for clinical outcomes after arthroscopic treatment of traumatic central tears of the triangular fibrocartilage complex; The rate of nonunion in the MRI-detected occult scaphoid fracture: a multicentre cohort study; Does correction of carpal malalignment influence the union rate of scaphoid nonunion surgery?; Provision of a home-based video-assisted therapy programme in thumb carpometacarpal arthroplasty; Is replantation associated with better hand function after traumatic hand amputation than after revision amputation?; Diagnostic performance of artificial intelligence for detection of scaphoid and distal radius fractures: a systematic review

The June 2024 Hip & Pelvis Roundup³⁶⁰ looks at: Machine learning did not outperform conventional competing risk modelling to predict revision arthroplasty; Unravelling the risks: incidence and reoperation rates for femoral fractures post-total hip arthroplasty; Spinal versus general anaesthesia for hip arthroscopy: a COVID-19 pandemic- and opioid epidemic-driven study; Development and validation of a deep-learning model to predict total hip arthroplasty on radiographs; Ambulatory centres lead in same-day hip and knee arthroplasty success; Exploring the impact of smokeless tobacco on total hip arthroplasty outcomes: a deeper dive into postoperative complications.