Convergence analysis and validation of a discrete element model of the human lumbar spine

Degenerative diseases of the spine can lead to or hasten the onset of additional spinal problems that significantly reduce human mobility. The spine consists of vertebral bodies and intervertebral discs. The most degraded are intervertebral discs. The vertebral body consists of a shell (cortical bone tissue) and an internal content (cancellous bone tissue). The intervertebral disc is a complex structural element of the spine, consisting of the nucleus pulposus, annulus fibrosus, and cartilaginous plates. To develop numerical models for the vertebral body and intervertebral disc, first, it is necessary to verify and validate the models for the constituent elements of the lumbar spine. This paper, for the first time, presents discrete elements-based numerical models for the constituent parts of the lumbar spine, and their verification and validation. The models are validated using uniaxial compression experiments available in the literature. The model predictions are in good qualitative and quantitative agreement with the data of those experiments. The loading rate sensitivity analysis revealed that fluid-saturated porous materials are highly sensitive to loading rate: a 1000-fold increase in rate leads to the increase in effective stiffness of 130 % for the intervertebral disc, and a 250-fold increase in rate leads to the increase in effective stiffness of 50 % for the vertebral body. The developed model components can be used to create an L4-L5 segment model, which, in the future, will allow investigating the mechanical behavior of the spine under different types of loading.

Image processing lumbar spine to highlight the location of the herniated disc

Low back pain is a common disease. A common cause of this problem is a herniated disc in the lumbar spine. Lumbar disc herniation represents the displacement of the disc (annular fibrosis or medullary nuclei). While most cases, the pain will disappear in a few days to a few weeks; however, it can last for three months or more. Detection and diagnosis are the two most important tasks in a computer-aided diagnostic system. In this article, we use images taken from the results of the MRI imaging of the patient. Through the use of image inversion to highlight the position of degenerative discs. This result wishes to provide a simple and inexpensive diagnostic image processing method to help doctors quickly determine the degree of disc herniation, the status of lumbar discs, they can give the appropriate treatment to the patient.

Assessing spinal movement during four extrication methods: a biomechanical study using healthy volunteers

Background Motor vehicle collisions are a common cause of death and serious injury. Many casualties will remain in their vehicle following a collision. Trapped patients have more injuries and are more likely to die than their untrapped counterparts. Current extrication methods are time consuming and have a focus on movement minimisation and mitigation. The optimal extrication strategy and the effect this extrication method has on spinal movement is unknown. The aim of this study was to evaluate the movement at the cervical and lumbar spine for four commonly utilised extrication techniques. Methods Biomechanical data was collected using inertial Measurement Units on 6 healthy volunteers. The extrication types examined were: roof removal, b-post rip, rapid removal and self-extrication. Measurements were recorded at the cervical and lumbar spine, and in the anteroposterior (AP) and lateral (LAT) planes. Total movement (travel), maximal movement, mean, standard deviation and confidence intervals are reported for each extrication type. Results Data from a total of 230 extrications were collected for analysis. The smallest maximal and total movement (travel) were seen when the volunteer self-extricated (AP max = 2.6 mm, travel 4.9 mm). The largest maximal movement and travel were seen in rapid extrication extricated (AP max = 6.21 mm, travel 20.51 mm). The differences between self-extrication and all other methods were significant (p < 0.001), small non-significant differences existed between roof removal, b-post rip and rapid removal. Self-extrication was significantly quicker than the other extrication methods (mean 6.4 s). Conclusions In healthy volunteers, self-extrication is associated with the smallest spinal movement and the fastest time to complete extrication. Rapid, B-post rip and roof off extrication types are all associated with similar movements and time to extrication in prepared vehicles.

Sedentary lifestyle and body composition in type 2 diabetes

Background Body composition alterations may participate in the pathophysiological processes of type 2 diabetes (T2D). A sedentary lifestyle may be responsible for alterations of body composition and adverse consequences, but on which body composition of patients with T2D and to what extent the sedentary lifestyle has an effect have been poorly investigated. Methods We recruited 402 patients with T2D for this cross-sectional study. All patients received questionnaires to evaluate sedentary time and were further divided into three subgroups: low sedentary time (LST, < 4 h, n = 109), middle sedentary time (MST, 4–8 h, n = 129) and high sedentary time (HST, > 8 h, n = 164). Each patient underwent a dual energy X-ray absorptiometry (DXA) scan to detect body composition, which included body fat percentage (B-FAT), trunk fat percentage (T-FAT), appendicular skeletal muscle index (ASMI), lumbar spine bone mineral density (BMD) (LS-BMD), femoral neck BMD (FN-BMD), hip BMD (H-BMD) and total BMD (T-BMD). Other relevant clinical data were also collected. Results With increasing sedentary time (from the LST to HST group), B-FAT and T-FAT were notably increased, while ASMI, LS-BMD, FN-BMD, H-BMD and T-BMD were decreased (p for trend < 0.01). After adjustment for other relevant clinical factors and with the LST group as the reference, the adjusted mean changes [B (95% CI)] in B-FAT, T-FAT, ASMI, LS-BMD, FN-BMD, H-BMD and T-BMD in the HST group were 2.011(1.014 to 3.008)%, 1.951(0.705 to 3.197)%, − 0.377(− 0.531 to − 0.223) kg/m2, − 0.083(− 0.124 to − 0.042) g/cm2, − 0.051(− 0.079 to − 0.024) g/cm2, − 0.059(− 0.087 to − 0.031) g/cm2 and − 0.060(− 0.088 to − 0.033) g/cm2, p < 0.01, respectively. Conclusions A sedentary lifestyle may independently account for increases in trunk and body fat percentage and decreases in appendicular skeletal muscle mass and BMD of the lumbar spine, femoral neck, hip and total body in patients with T2D.

Comorbidities and Health-Related Quality of Life in Subjects with Spine Osteoarthritis at 50 Years of Age or Older: Data from the Korea National Health and Nutrition Examination Survey

Background and Objective: This study assessed comorbidities and health-related quality of life (HRQOL) in subjects with lumbar spine osteoarthritis (OA) in the Korean population. Materials and Methods: We analyzed 3256 subjects who were 50 years or older and underwent plain radiography of the lumbar spine as part of the Korea National Health and Nutrition Examination Survey (KNHANES) 2012. Radiographic assessment was based on Kellgren–Lawrence (K-L) grade ranging from 0 to 2, with K-L grade 2 defined as lumbar spine OA. HRQOL was assessed by EuroQol-5 dimensions (EQ-5D), which include the EQ-5D index and visual analogue scale (EQ-VAS) measurements. Results: Comorbidities such as hypertension, myocardial infarction, angina, cerebral infarction, and diabetes mellitus were more frequent in spine OA than in controls, while dyslipidemia was less common. Subjects with spine OA had higher mean number of comorbid conditions than controls (1.40 (SE 0.05) vs. 1.20 (SE 0.03), p = 0.001). Subjects with spine OA had much lower EQ-5D index than controls (p < 0.001) but not lower EQ-VAS score. Multivariate binary logistic analysis showed that hypertension and colon cancer were associated with spine OA compared to controls (OR 1.219, 95% CI 1.020–1.456, p = 0.030 and OR 0.200, 95% CI 0.079–0.505, p = 0.001, respectively) after adjustment for confounding factors. Lower EQ-5D index was related to spine OA (95% CI 0.256, 95% CI 0.110–0.595, p = 0.002) but not EQ-VAS score. Conclusion: In this study, we found that comorbidities such as hypertension and colon cancer as well as lower HRQOL were associated with spine OA.

Automated selection of mid-height intervertebral disc slice in traverse lumbar spine MRI using a combination of deep learning feature and machine learning classifier

Abnormalities and defects that can cause lumbar spinal stenosis often occur in the Intervertebral Disc (IVD) of the patient’s lumbar spine. Their automatic detection and classification require an application of an image analysis algorithm on suitable images, such as mid-sagittal images or traverse mid-height intervertebral disc slices, as inputs. Hence the process of selecting and separating these images from other medical images in the patient’s set of scans is necessary. However, the technological progress in making this process automated is still lagging behind other areas in medical image classification research. In this paper, we report the result of our investigation on the suitability and performance of different approaches of machine learning to automatically select the best traverse plane that cuts closest to the half-height of an IVD from a database of lumbar spine MRI images. This study considers images features extracted using eleven different pre-trained Deep Convolution Neural Network (DCNN) models. We investigate the effectiveness of three dimensionality-reduction techniques and three feature-selection techniques on the classification performance. We also investigate the performance of five different Machine Learning (ML) algorithms and three Fully Connected (FC) neural network learning optimizers which are used to train an image classifier with hyperparameter optimization using a wide range of hyperparameter options and values. The different combinations of methods are tested on a publicly available lumbar spine MRI dataset consisting of MRI studies of 515 patients with symptomatic back pain. Our experiment shows that applying the Support Vector Machine algorithm with a short Gaussian kernel on full-length image features extracted using a pre-trained DenseNet201 model is the best approach to use. This approach gives the minimum per-class classification performance of around 0.88 when measured using the precision and recall metrics. The median performance measured using the precision metric ranges from 0.95 to 0.99 whereas that using the recall metric ranges from 0.93 to 1.0. When only considering the L3/L4, L4/L5, and L5/S1 classes, the minimum F1-Scores range between 0.93 to 0.95, whereas the median F1-Scores range between 0.97 to 0.99.