Objective This study evaluated lumbar spine radiographs using the Choi and Patgaonkar classifications to verify parameters potentially influencing the L5-S1 transforaminal approach. Materials and Methods We studied 167 lumbosacral spine radiographs from patients over 18 years old with no history of surgeries, tumors, fractures, or scoliosis to measure the iliac crest height and rim angle. We categorized the cases per pelvic morphology, mega-apophysis presence, and Choi and Patgaonkar classifications. Results Seventy-five cases had an android pelvis and 92 had a gynecoid pelvis. The mean iliac height was 25.9 ± 7.5 cm, and the rim angle was 23.4 ± 7.5 degrees. The gynecoid pelvis showed a lower iliac height. According to Patgaonkar, 63 cases indicated a suprailiac approach, and per the Choi classification, 37 were suitable for a suprailiac approach and 106 for a suprailiac approach with foraminoplasty. Conclusion Gynecoid pelvises had a lower iliac height. Furthermore, 37.7% of the cases were suitable for a suprailiac approach per the Patgaonkar classification. The Choi classification indicated a suprailiac approach for 22.1% of the cases and a suprailiac approach with foraminoplasty for 63.4% of the subjects.

This study aimed to evaluate the effect of pelvis type in percutaneous acetabular column fixation. What is the effect of pelvis type in percutaneous acetabular colon fixation? The available pelvic computed tomography (CT) scans which were obtained in the diagnostic imaging center with a 1 mm slice width were evaluated. The pelvic type was classified with the help of MPR (Multiplanar Reformat) and 3D (Three Dimensional) imaging modes. All evaluated bony pelvic structures were anatomically intact. 40 types of android, gynecoid, anthropoid, and platypelloid pelvis were determined. CT sections were created in MPR imaging mode. Anterior obturator oblique (AOO) and inlet images were created for anterior column evaluation, while iliac oblique (IO) and outlet images were created for posterior column evaluation. The possibility of obtaining a linear corridor for acetabular columns was investigated by measuring corridor width and lengthon images of pelvic CTs. A linear corridor could not be obtained between the pubic tubercle and the supraacetabular region of 12 (30%) CTs in the anterior column of gynecoid pelvis group. The diameter of the anterior column corridor was below 5.5 mm in 10 (25%) of Gynecoid pelvis group, 5 (12.5%) of Anthropoid pelvis group, and 10 of Platypelloid pelvis group, , and all those scans belonged to the female gender. There was a statistically significant difference between pelvis types in terms of anterior and posterior column diameters (p <0.001). While the android pelvis type had the highest diameter and corridor length in both anterior column and posterior column measurements, the gynecoid pelvic type had the lowest diameter and corridor length. In the evaluations made according to gender, both anterior and posterior column diameters were larger and longer in males than in females (p <0.001). Pelvis type is an important factor which can affect anterior and posterior column diameter and length of acetabulum. Pelvic typing before acetabular surgery can help the surgeon determining the most appropriate patient position, surgical approach, and implant selection. Level of Evidence: Level 2.

Bipedalism is a unique function in humans. Various investigations in bipedal walking have assessed the kinetic chain from the pelvis to the lower limbs. However, few studies have investigated the functions of the upper body including the psoas major muscles. In the present study, a bipedal-walking human full-body skeletal model, "the bipedal android model", was generated by attaching air cylinder devices to simulate the contraction and relaxation of various muscles required for bipedal walking, including the psoas major muscles. The bipedal-walking principle was discussed using the model.

Foot strike detection is important when evaluating a person's gait characteristics. Accelerometer and gyroscope signals from smartphones have been used to train artificial intelligence (AI) models for automated foot strike detection in able-bodied and elderly populations. However, there is limited research on foot strike detection in lower limb amputees, who have a more variable and asymmetric gait. A novel method for automated foot strike detection in lower limb amputees was developed using raw accelerometer and gyroscope signals collected from a smartphone positioned at the posterior pelvis. Raw signals were used to train a decision tree model and long short-term memory (LSTM) model for automated foot strike detection. These models were developed using retrospective data (n = 72) collected with the TOHRC Walk Test app during a 6-min walk test (6MWT). An Android smartphone was placed on a posterior belt for each participant during the 6MWT to collect accelerometer and gyroscope signals at 50 Hz. The best model for foot strike identification was the LSTM with 100 hidden nodes in the LSTM layer, 50 hidden nodes in the dense layer, and a batch size of 64 (99.0% accuracy, 86.4% sensitivity, 99.4% specificity, and 83.7% precision). This research created a novel method for automated foot strike identification in lower extremity amputee populations that is equivalent to manual labelling and accessible for clinical use. Automated foot strike detection is required for stride analysis and to enable other AI applications, such as fall detection.

Fall-risk classification is a challenging but necessary task to enable the recommendation of preventative programs for individuals identified at risk for falling. Existing research has primarily focused on older adults, with no predictive fall-risk models for lower limb amputees, despite their greater likelihood of fall-risk than older adults. In this study, 89 amputees with varying degrees of lower limb amputation were asked if they had fallen in the past 6 months. Those who reported at least one fall were considered a fall risk. Each participant performed a 6 minute walk test (6MWT) with an Android smartphone placed in a holder located on the back of the pelvis. A fall-risk classification method was developed using data from sensors within the smartphone. The Ottawa Hospital Rehabilitation Center Walk Test app captured accelerometer and gyroscope data during the 6MWT. From this data, foot strikes were identified, and 248 features were extracted from the collection of steps. Steps were segmented into turn and straight walking, and four different data sets were created: turn steps, straightaway steps, straightaway and turn steps, and all steps. From these, three feature selection techniques (correlation-based feature selection, relief F, and extra trees classifier ensemble) were used to eliminate redundant or ineffective features. Each feature subset was tested with a random forest classifier and optimized for the best number of trees. The best model used turn data, with three features selected by Correlation-based feature selection (CFS), and used 500 trees in a random forest classifier. The resulting metrics were 81.3% accuracy, 57.2% sensitivity, 94.9% specificity, a Matthews correlation coefficient of 0.587, and an F1 score of 0.83. Since the outcomes are comparable to metrics achieved by existing clinical tests, the classifier may be viable for use in clinical practice.

The pelvis functions to transmit upper body loads to the lower limbs and is critical in human locomotion. Semi-automated, finite element (FE) morphing techniques eliminate the need for segmentation and have shown to accelerate the generation of multiple specimen-specific pelvic FE models to enable the study of pelvic mechanical behaviour. The purpose of this research was to produce simulated human pelvic FE models representing android, gynecoid, anthropoid and platypelloid morphologies and to isolate differences in strain patterns due to anatomic shape under physiologic loading. Using five initially generated specimen-specific FE models, each specimen-specific FE model was reconfigured into three different morphologies using FE mesh morphing techniques. Significantly different strains were found comparing the gynecoid (classical female pelvis') to the android ('true male pelvis') models (p = 0.040), with strains twice as high in the superior pubic rami. No significant differences were seen in comparing overall strains between the other pelvic shapes (p = 0.61-0.126). The highest strain regions in all models were found in the supra-acetabular regions, with high strains also found in the regions of the superior pubic rami, the greater sciatic notch and sacral regions about the L5 vertebrae. Quantifying the contributions of shape to strain in the pelvis may increase the understanding of sex and patient-specific differences in fracture risk and motivate the consideration of treatment strategies that account for anatomic pelvic differences.