Adolescent Idiopathic Scoliosis (AIS) is a 3D spinal deformity characterized by curvature and rotation of the spine. Markerless surface topography (ST) analysis has been proposed for diagnosing and monitoring AIS to reduce the X-ray radiation exposure to patients. This method captures scans of the cosmetic deformity of the torso using visible, radiation-free light. The asymmetry analysis of the torso, represented as a deviation contour map with deviation patches outlining the areas of cosmetic asymmetries, has previously been shown to predict the severity and progression of the condition in comparison with radiographs, by using classification trees. While the classification results were promising, it was reported that some mild curves were erroneously diagnosed. Furthermore, this approach is highly sensitive to threshold values selected in the decision trees. Therefore, this study aims to define a custom Neighbourhood Classifier algorithm for AIS classification to improve the accuracy, sensitivity, and specificity of predicting curve severity and curve progression in AIS. Curve severity was predicted with 80% accuracy (sensitivity = 81%; specificity = 79%) for thoracic-thoracolumbar curves and 72% (sensitivity = 93%; specificity = 53%) for lumbar curves. This represents an improvement over the previous method with curve severity accuracies of 77% and 63% for thoracic-thoracolumbar and lumbar curves, respectively. Additionally, curve progression was predicted with 93% accuracy (sensitivity = 83%; specificity = 95%) representing a substantial improvement over the previous method with an accuracy of 59%. The current method has shown the potential to further reduce radiation exposure for AIS patients by avoiding X-rays for mild and non-progressive curves identified using ST analysis.

Adolescent idiopathic scoliosis (AIS) is a three-dimensional lateral and torsional deformity of the spine, affecting up to 5% of the population. Traditional scoliosis screening methods exhibit limited accuracy, leading to unnecessary referrals and exposure to ionizing radiation from x-ray examinations. The 3D markerless surface topography (ST) technique quantifies trunk asymmetry and can be a potential scoliosis screening tool. However, differences in trunk asymmetry between individuals with scoliosis and those with a typically developing spine have yet to be thoroughly studied. Using the ST method, this study aims to distinguish adolescents with AIS from those with typically a developing spine. Participants aged 10 to 18 years, comprising of 285 individuals with confirmed AIS and 273 with typically developing spines, were included in the study (total scans including follow-ups: 693 for the AIS group and 298 for the control group). The positive for AIS group was identified through radiographic exams, specifically with curves ranging from 10° to 45°, while the negative (control) group qualified if their scoliometer test measured less than 7° and they had no known scoliosis diagnosis. The dataset comprised of surface torso scans captured either using stationary Minolta cameras or with the Structure sensor. ST analysis involved the reflection of the 3D geometry of the torso, aligning it with the original torso by minimizing the distance between corresponding points. Deviations between the original and reflected torso over the back surface and torso surface depth were mapped onto 102 × 102 grids. A convolutional neural network (CNN) was developed using deviations and depth (distance between the back surface and frontal plane) maps as inputs to classify the torso surface of typically developing adolescents and those with AIS. 10-fold cross-validation was applied during model development. 20% of the data was used as a holdout for final testing. Classification results of the proposed model were compared to the ground truth. The average training and validation accuracy across the ten folds was 100% and 94%, respectively. The classifications from the testing sets using the best performing model from the 10-fold cross-validation obtained accuracy, sensitivity, and specificity of 95%, 97%, and 90%, respectively. The positive likelihood ratio (PLR) of the testing set was 9.7. Likewise, a negative likelihood ratio (NLR) of 0.032 was also attained. The model sensitivity for detecting curves with Cobb greater than 25° was 99%. The sensitivity for detecting mild cases (Cobb < 25°) was 96%. The proposed CNN predictive model to detect AIS using ST showed excellent classification results. Markerless surface topography can serve as a dependable and non-invasive method for screening AIS.

Strain-based design (SBD) plays an important role in pipeline design and assessment of pipelines subjected to geo-hazards. Under such hazards, a pipe can be subjected to substantial plastic strains, leading to tensile failure at locations of girth weld flaws. For SBD, the finite element method (FEM) can be a reliable tool to calculate the tensile strain capacity (TSC) for better design in pipelines. This study aims to investigate the ductile fracture properties for specific vintage pipeline steel (API 5L grade of X52) using the extended finite element method (XFEM). Eight full-scale tests were simulated using the commercial finite element analysis software ABAQUS Version 6.17. Maximum principal strain is used to assess the damage initiation using the cohesive zone model (CZM) when the crack evolution is evaluated by fracture energy release. A proper set of damage parameters for the X52 materials was calibrated based on the ability of the model to reproduce the experimental results. These experimental results included the tensile strain, applied load, endplate rotation, and crack mouth opening displacement (CMOD). This study describes a methodology for validation of the XFEM and the proper damage parameters required to model crack initiation and propagation in X52 grades of pipeline.

Due to the complexity of articular interconnections and tenuous blood supply to the talus, talus fractures are often associated with complications (e.g., avascular necrosis). Currently, surgically fusing the talus to adjacent bones is widely used as treatment to talus fractures, but this procedure can greatly reduce mobility in the ankle and hindfoot. Alternatively, customized talus implants have shown an overall satisfactory patient feedback but with the limitation of high expenses and time-consuming manufacturing process. In order to circumvent these disadvantages, universal talus implants have been proposed as a potential solution. In our study, we aimed to develop a methodology using Statistical Shape Model (SSM) to simulate the talus, and then evaluate the feasibility of the model to obtain the mean shape needed for universal implant design. In order to achieve this, we registered 98 tali (41 females and 57 males) and used the registered dataset to train our SSM. We used the mean shape derived from the SSM as the basis for our talus implant template, and compared our template with that of previous works. We found that our SSM mean shape talus implant was geometrically similar to implants from other works, which used a different method for the mean shape. This suggests the feasibility of SSM as a method of finding mean shape information for the development of universal implants. A second aim of our study was to investigate if one scalable talus implant can accommodate all patients. In our study, we focused on addressing this from a geometric perspective as there are multiple factors impacting this (e.g., articular surface contact characteristics, implant material properties). Our initial findings are that the first two principal components should be afforded consideration for the geometrical accuracy of talus implant design. Additional factors would need to be further evaluated for their role in informing universal talus implant design.