To explore a novel machine learning model to evaluate the vertebral fracture risk using Decision Tree model and train the model by Bone Mineral Density (BMD) of different compartments of vertebral body. We collected a Computed Tomography image dataset, including 10 patients with osteoporotic fracture and 10 patients without osteoporotic fracture. 40 non-fracture Vertebral bodies from T11 to L5 were segmented from 10 patients with osteoporotic fracture in the CT database and 53 non-fracture Vertebral bodies from T11 to L5 were segmented from 10 patients without osteoporotic fracture in the CT database. Based on the biomechanical properties, 93 vertebral bodies were further segmented into 11 compartments: eight trabecular bone, cortical shell, top and bottom endplate. BMD of these 11 compartments was calculated based on the HU value in CT images. Decision tree model was used to build fracture prediction model, and Support Vector Machine was built as a compared model. All BMD data was shuffled to a random order. 70% of data was used as training data, and 30% left was used as test data. Then, training prediction accuracy and testing prediction accuracy were calculated separately in the two models. The training accuracy of Decision Tree model is 100% and testing accuracy is 92.14% after trained by BMD data of 11 compartments of the vertebral body. The type I error is 7.14% and type II error is 0%. The training accuracy of Support Vector Machine model is 100% and the testing accuracy is 78.57%. The type I error is 17.86% and type II error is 3.57%. The performance of vertebral body fracture prediction using Decision Tree is significantly higher than using Support Vector Machine. The Decision Tree model is a potential risk assessment method for clinical application. The pilot evidence showed that Decision Tree prediction model overcomes the overfitting drawback of Support Vector Machine Model. However, larger dataset and cohort study should be conducted for further evidence

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

Background. The management of the patella during primary total knee arthroplasty (TKA) is controversial. Despite the majority of patients reporting excellent outcomes following TKA, a common complaint is anterior knee pain. Resurfacing of the patella at the time of initial surgery has been proposed as a means of preventing anterior knee pain, however current evidence, including four recent meta-analyses, has failed to show clear superiority of patellar resurfacing. Therefore, the purpose of this study was to estimate the cost-effectiveness of patellar resurfacing compared to non-resurfacing in TKA. Methods. We conducted a cost-effectiveness analysis using a decision analytic model to represent a hypothetical patient cohort undergoing primary TKA. Each patient will receive a TKA either with the Patella Resurfaced or Not Resurfaced. Following surgery, patients can transition to one of three chronic health states: 1) Well Post-operative, 2) Patellofemoral Pain (PFP), or 3) Serious Adverse Event (AE), which we have defined as any event requiring Revision TKA, including: loosening/lysis, infection, instability, or fracture (Figure 1). We obtained revision rates following TKA for both resurfaced and unresurfaced cohorts using data from the 2014 Australian Registry. This data was chosen due to similarities between Australian and North American practice patterns and patient demographics, as well as the availability of longer term follow up data, up to 14 years postoperative. Our effectiveness outcome for the model was the quality-adjusted life year (QALY). We used utility scores obtained from the literature to calculate QALYs for each health state. Direct procedure costs were obtained from our institution's case costing department, and the billing fees for each procedure. We estimated cost-effectiveness from a Canadian publicly funded health care system perspective. All costs and quality of life outcomes were discounted at a rate of 5%. All costs are presented in 2015 Canadian dollars. Results. Our cost-effectiveness analysis suggests that TKA with patella resurfacing is a dominant procedure. Patients who receive primary TKA with non-resurfaced patella had higher associated costs over the first 14 years postoperative ($16,182 vs $15,720), and slightly lower quality of life (5.37 QALYs vs 6.01 QALYs). The revision rate for patellar resurfacing was 1.3%. If the rate of secondary resurfacing procedures is 0.5% or less, there is no difference in costs between the two procedures. Discussion. Our results suggest that, up to 14 years postoperative, resurfacing the patella in primary TKA is cost-effective compared to primary TKA without patellar resurfacing, due to the higher revision rate in this cohort of patients for secondary resurfacing. Our sensitivity analysis suggests that, among surgical practices that do not routinely perform secondary resurfacing procedures (estimated rate at our institution is 0.3%) there is no significant difference in costs. Although our results suggest that patella resurfacing results in higher quality of life, our model is limited by the availability and validity of utility outcome estimates reported in the literature for the long term follow up of patients following TKA with or without patella resurfacing and secondary resurfacing procedures

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.