Aims. A substantial fraction of patients undergoing knee arthroplasty (KA) or hip arthroplasty (HA) do not achieve an improvement as high as the minimal clinically important difference (MCID), i.e. do not achieve a meaningful improvement. Using three patient-reported outcome measures (PROMs), our aim was: 1) to assess machine learning (ML), the simple pre-surgery PROM score, and logistic-regression (LR)-derived performance in their prediction of whether patients undergoing HA or KA achieve an improvement as high or higher than a calculated MCID; and 2) to test whether ML is able to outperform LR or pre-surgery PROM scores in predictive performance. Methods. MCIDs were derived using the change difference method in a sample of 1,843 HA and 1,546 KA patients. An artificial neural network, a gradient boosting machine, least absolute shrinkage and selection operator (LASSO) regression, ridge regression, elastic net, random forest, LR, and pre-surgery PROM scores were applied to predict MCID for the following PROMs: EuroQol five-dimension, five-level questionnaire (EQ-5D-5L), EQ visual analogue scale (EQ-VAS), Hip disability and Osteoarthritis Outcome Score-Physical Function Short-form (HOOS-PS), and Knee injury and Osteoarthritis Outcome Score-Physical Function Short-form (KOOS-PS). Results. Predictive performance of the best models per outcome ranged from 0.71 for HOOS-PS to 0.84 for EQ-VAS (HA sample). ML statistically significantly outperformed LR and pre-surgery PROM scores in two out of six cases. Conclusion. MCIDs can be predicted with reasonable performance. ML was able to outperform traditional methods, although only in a minority of cases. Cite this article: Bone Joint Res 2023;12(9):512–521

The pathogenesis of scoliosis progression remains poorly understood. Seventy-two subject data sets, consisting of four successive values of Cobb-angle and lateral deviations at apices for six and twelve-months intervals in the coronal plane, were used to train and test an artificial neural network (ANN) to predict spinal deformity progression. The accuracies of the trained ANN (3-4-1) for training and testing data were within 3.64° (±2.58°) and 4.40° (±1.86°) of Cobb angles, and within 3.59 (±3.96) mm and 3.98 (±3.41) mm of lateral deviations, respectively. The adapted technique for predicting the scoliosis deformity progression has promising clinical applications. Scoliosis is a common and poorly understood three-dimensional spinal deformity. The study purpose is to predict scoliosis progression at six and twelve months intervals in the future using successive spinal indices with an artificial neural network (ANN). The adapted ANN technique enables earlier detection of scoliosis progression with high accuracy. Improved prediction of scoliosis progression will impact bracing or surgical treatment decisions, and may decrease hazardous X-ray exposure. Seventy-two data sets from adolescent idiopathic scoliosis subjects recruited at the Alberta Children’s Hospital were used in this study. Data sets composed of four successive values of Cobb angles and lateral deviations at apices for six and twelvemonth intervals (coronal plane) were extracted to train and test a specific ANN for predicting scoliosis progression. Progression patterns in Cobb angles (n = 10) and lateral deviations (n = 8) were successfully identified. The accuracies of the trained ANN (3-4-1) with the training and testing data sets were 3.64° (±2.58°) and 4.40° (±1.86°) of Cobb angles, 3.59 (±3.96) mm and 3.98 (±3.41) mm of lateral deviations, respectively. These results are in close agreement with those using cubic spline extrapolation techniques (3.49° ± 1.85° and 3.31 ± 4.22 mm) and adaptive neuro-fuzzy inference system (3.92° ±3.53° and 3.37 ±3.95 mm) for the same testing data. ANN can be a promising technique for prediction of scoliosis progression with substantial improvements in accuracy over current techniques, leading to potentially important implications for scoliosis monitoring and treatment decisions. Funding: AHFMR, CIHR, Fraternal Order of Eagles, NSERC, GEOIDE

Introduction. Although total knee arthroplasty (TKA) is generally considered successful, 16–30% of patients are dissatisfied. There are multiple reasons for this, but some of the most frequent reasons for revision are instability and joint stiffness. A possible explanation for this is that the implant alignment is not optimized to ensure joint stability in the individual patient. In this work, we used an artificial neural network (ANN) to learn the relation between a given standard cruciate-retaining (CR) implant position and model-predicted post-operative knee kinematics. The final aim was to find a patient-specific implant alignment that will result in the estimated post-operative knee kinematics closest to the native knee. Methods. We developed subject-specific musculoskeletal models (MSM) based on magnetic resonance images (MRI) of four ex vivo left legs. The MSM allowed for the estimation of secondary knee kinematics (e.g. varus-valgus rotation) as a function of contact, ligament, and muscle forces in a native and post-TKA knee. We then used this model to train an ANN with 1800 simulations of knee flexion with random implant position variations in the ±3 mm and ±3° range from mechanical alignment. The trained ANN was used to find the implant alignment that resulted in the smallest mean-square-error (MSE) between native and post-TKA tibiofemoral kinematics, which we term the dynamic alignment. Results. Dynamic alignment average MSE kinematic differences to the native knees were 1.47 mm (± 0.89 mm) for translations and 2.89° (± 2.83°) for rotations. The implant variations required were in the range of ±3 mm and ±3° from the starting mechanical alignment. Discussion. In this study we showed that the developed tool has the potential to find an implant position that will restore native tibiofemoral kinematics in TKA. The proposed method might also be used with other alignment strategies, such as to optimize implant position towards native ligament strains. If native knee kinematics are restored, a more normal gait pattern can be achieved, which might result in improved patient satisfaction. The small changes required to achieve the dynamic alignment do not represent large modifications that might compromise implant survivorship. Conclusion. Patient-specific implant position predicted with MSM and ANN can restore native knee function in a post-TKA knee with a standard CR implant

Pedicle screw fixation is a technically demanding procedure with potential difficulties and reoperation rates are currently on the order of 11%. The most common intraoperative practice for position assessment of pedicle screws is biplanar fluoroscopic imaging that is limited to two- dimensions and is associated to low accuracies. We have previously introduced a full-dimensional position assessment framework based on registering intraoperative X-rays to preoperative volumetric images with sufficient accuracies. However, the framework requires a semi-manual process of pedicle screw segmentation and the intraoperative X-rays have to be taken from defined positions in space in order to avoid pedicle screws' head occlusion. This motivated us to develop advancements to the system to achieve higher levels of automation in the hope of higher clinical feasibility. In this study, we developed an automatic segmentation and X-ray adequacy assessment protocol. An artificial neural network was trained on a dataset that included a number of digitally reconstructed radiographs representing pedicle screw projections from different points of view. This model was able to segment the projection of any pedicle screw given an X-ray as its input with accuracy of 93% of the pixels. Once the pedicle screw was segmented, a number of descriptive geometric features were extracted from the isolated blob. These segmented images were manually labels as ‘adequate’ or ‘not adequate’ depending on the visibility of the screw axis. The extracted features along with their corresponding labels were used to train a decision tree model that could classify each X-ray based on its adequacy with accuracies on the order of 95%. In conclusion, we presented here a robust, fast and automated pedicle screw segmentation process, combined with an accurate and automatic algorithm for classifying views of pedicle screws as adequate or not. These tools represent a useful step towards full automation of our pedicle screw positioning assessment system

Introduction. Short-segment posterior instrumentation for spine fractures is threatened by unacceptable failure rates. Two important design objectives of pedicle screws, bending and pullout strength, may conflict with each other. Hypothesis. Multiobjective optimization study with artificial neural network (ANN) algorithm and genetic algorithm (GA). Materials & Methods. Three-dimensional finite element (FE) methods were applied to investigate the optimal designs of pedicle screws with an outer diameter of 7 mm using a multiobjective approach for these two objectives. Based on the FE results on an L25 orthogonal array, two objective functions were developed by an ANN algorithm. Then, the trade-off solutions known as Pareto optima were explored by a GA. The optimal design was validated by mechanical tests. Results. The knee solutions of the Pareto fronts had simultaneous high bending and pullout strength ranging from 92 to 94 percent of their maxima. The corresponding range of the design parameters was 3.8 to 4.06 mm for inner diameter and 3.21 to 3.3 mm for pitch; 0 mm for beginning position of conical angle, 0.4 mm for proximal root radius, 5 degrees for proximal half angle, and 0.1 mm for thread width. The optimal design was well validated by mechanical tests, comparing with commercially available pedicle screws. Discussion & Conclusions. The optimal design of pedicle screws obtained could achieve an ideal with high mechanical performance in both bending and pullout tests

Introduction: Coordination is a strategy chosen by the central nervous system to control the movements and maintain stability during gait. Coordinated multi-joint movements require a complex interaction between nervous outputs, biomechanical constraints, and pro-prioception. Quantitatively understanding and modeling gait coordination still remain a challenge. Surgeons lack a way to model and appreciate the coordination of patients before and after surgery of the lower limbs. Patients alter their gait patterns and their kinematic synergies when they walk faster or slower than normal speed to maintain their stability and minimize the energy cost of locomotion. The goal of this study was to provide a dynamical system approach to quantitatively describe human gait coordination and apply it to patients before and after total knee arthroplasty. Methods: A new method of quantitative analysis of interjoint coordination during gait was designed, providing a general model to capture the whole dynamics and showing the kinematic synergies at various walking speeds. The proposed model imposed a relationship among lower limb joint angles (hips and knees) to parameterize the dynamics of locomotion of each individual. An integration of different analysis tools such as Harmonic analysis, Principal Component Analysis, and Artificial Neural Network helped overcome high-dimensionality, temporal dependence, and non-linear relationships of the gait patterns. Ten patients were studied using an ambulatory gait device (Physilog®). Each participant was asked to perform two walking trials of 30m long at 3 different speeds and to complete an EQ-5D questionnaire, a WOMAC and Knee Society Score. Lower limbs rotations were measured by four miniature angular rate sensors mounted respectively, on each shank and thigh. The outcomes of the eight patients undergoing total knee arthroplasty, recorded pre-operatively and post-operatively at 6 weeks, 3 months, 6 months and 1 year were compared to 2 age-matched healthy subjects. Results: The new method provided coordination scores at various walking speeds, ranged between 0 and 10. It determined the overall coordination of the lower limbs as well as the contribution of each joint to the total coordination. The difference between the pre-operative and post-operative coordination values were correlated with the improvements of the subjective outcome scores. Although the study group was small, the results showed a new way to objectively quantify gait coordination of patients undergoing total knee arthroplasty, using only portable body-fixed sensors. Conclusion: A new method for objective gait coordination analysis has been developed with very encouraging results regarding the objective outcome of lower limb surgery

Introduction: The prediction of clinical and biological behavior of bone tumors plays an important role in medical tasks such as diagnosis and treatment planning. Different prognostic factors for bone tumors outcome appear to be significant predictors for making definitive diagnosis. It is well-know that different clinical, radiological and histological characteristics are included in diagnostic process. The most important task for pathologist is to determinate biological behavior. Errors in diagnosis lead to wrong therapy and treatment. It was reason to determinate scores for tumor diagnostics. Score is usually determinate using classic statistical methods such multivariate logistic regression (MVLR), but new computer tehniks, and models of artificial intelligence take a place in modern scoring systems. Recently, classifications tree analysis (CTA) and artificial neural network (ANN) models have become popular in decision-making and outcome prediction of clinical medicine, especially in oncology. This study compared the levels of accuracy of MVLR, CTA and ANN model for the prediction of bone tumor’s biological behavior. Material and method: Data from patient who had diagnosed bone tumors in Institute of pathology, School of Medicine in the period of 10 years (1995–2004) were used for analysis purposed in the study. In the analyzed date –base were 3689 biopsies with these criteria. About 24% (882 biopsies) were excluded because of missing data about radiological presentation. Consequently, data from 2807 biopsies were used for the analyses. Clinical, radiological, histological characteristics, summary 166 variables were analyzed and used to compare the levels of accuracy for the three methods of scoring. All data were inserting in Spider 2.0 enterprise date-base who assisted MSSQL server 2000. For MVLR and CTA we used SPSS 15.0 program with incorporate CTA. There are methods of multivariate analysis that allow for study of simultaneous influence of a series of independed variable on the one depended variable (biological behavior of bone tumors). The ANN model used in this study were feed-forward networks, witch were trained with a back propagation algorithm (NNSYSID-Neural Network Based System Identification Toolbox) situated in the Matlab area. We compared three models across theirs overall percentages. The best model was one with highest overall percentage. Results: From all analyzed cases 1590 (56, 6%) were males and 1217 (43, 4%) were females patients with Middle Ages 34, 1 (aged from 0–94 years). Malignant bone tumors (prime and metastatic lesions) were 1339 (47,7%) and benign 1468 (52,3%). From all (166) characteristics 11 were selected on the bases of a definitive analysis and included into scoring system. From clinical characteristics just age of patient and clinical diagnosis “cyst” were included. Next radiological presentations: Pure osteolysis, osteolysis with cortical destruction, osteolysis with soft tissue mass, mixed lytic and sclerotic lesion was statistically significant for scoring model. Histological presents of fibroblasts, giant cells with hamosiderin pigment in stromal cells and atypical stromal cells, and hondroid stromal production were important for classification. Localization in finger’s bone was included in definitive score too. Three performed scoring models showed wary high overall percentages in prediction biological behavior of bone tumors: MVLR 93, 77%, CTA 88, 2% and ANN 91, 5%. The most informative variable, rang 1 in both models of artificial intelligence was radiological criterion. For CTA it was radiological presents of lytic lesion with soft tissue mass and for ANN was combined lytic and sclerotic presentation. Conclusions: All three scoring models are very useful in prediction bone’s tumor behavior, most of them each ones had priority versus others. The most successive (overall percentage 93, 77%) was MVLR. ANN had high sensitivity (overall percentage 93, 77%) and gave ranges of variables included in score. CTA algorithm had the least overall percentage but it is very simple and figurative for interpretation

Aims

To identify variables independently associated with same-day discharge (SDD) of patients following revision total knee arthroplasty (rTKA) and to develop machine learning algorithms to predict suitable candidates for outpatient rTKA.

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.

Aims

This systematic review aims to identify 3D predictors derived from biplanar reconstruction, and to describe current methods for improving curve prediction in patients with mild adolescent idiopathic scoliosis.

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: Bone Joint Res 2023;12(7):447–454.

Aims

The surgical target for optimal implant positioning in robotic-assisted total knee arthroplasty remains the subject of ongoing discussion. One of the proposed targets is to recreate the knee’s functional behaviour as per its pre-diseased state. The aim of this study was to optimize implant positioning, starting from mechanical alignment (MA), toward restoring the pre-diseased status, including ligament strain and kinematic patterns, in a patient population.

Aims

The use of technology to assess balance and alignment during total knee surgery can provide an overload of numerical data to the surgeon. Meanwhile, this quantification holds the potential to clarify and guide the surgeon through the surgical decision process when selecting the appropriate bone recut or soft tissue adjustment when balancing a total knee. Therefore, this paper evaluates the potential of deploying supervised machine learning (ML) models to select a surgical correction based on patient-specific intra-operative assessments.

Artificial intelligence and machine-learning analytics have gained extensive popularity in recent years due to their clinically relevant applications. A wide range of proof-of-concept studies have demonstrated the ability of these analyses to personalize risk prediction, detect implant specifics from imaging, and monitor and assess patient movement and recovery. Though these applications are exciting and could potentially influence practice, it is imperative to understand when these analyses are indicated and where the data are derived from, prior to investing resources and confidence into the results and conclusions. In this article, we review the current benefits and potential limitations of machine-learning for the orthopaedic surgeon with a specific emphasis on data quality.