• Korean Journal of Applied Biomechanics
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• v.24 no.4
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• pp.375-383
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• 2014

The purpose of this study was to assess the inter-segmental trunk motion during which multi-segmental movements of the spinal column was designed to interpret the effect of segmentation on the total measured spine motion. Also it analyzed the relative motion at three types of the spine models in drop landing. A secondary goal was to determine the intrinsic algorithmic errors of spine motion and the usefulness of such an approach as a tool to assess spinal motions. College students in the soccer team were selected the ten males with no history of spine symptoms or injuries. Each subject was given a fifteen minute adaptation period of drop landing on the 30cm height box. Inter-segmental spine motion were collected Vicon Motion Capture System (250 Hz) and synchronized with GRF data (1000 Hz). The result shows that Model III has a more increased range of motion (ROM) than Model I and Model II. And the Lagrange energy has significant difference of at E3 and E4 (p<.05). This study can be concluded that there are differences in the three models of algorithm during the phase of load absorption. Especially, Model III shows proper spine motion for the inter-segmental joint motion with the interaction effects using the seven segments. Model III shows more proper observed values about dynamic equilibrium than Model I & Model II. The findings have shown that the dynamic stability strategy of Model III toward multi-directional spinal motion supports for better function of the inter-segmental motor-control than the Model I and Model II.

• Journal of the Korean Society for Precision Engineering
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• v.23 no.3 s.180
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• pp.187-194
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• 2006

Unexpected postoperative changes, such as growth in rib hump and shoulder unbalance, have been occasionally reported after corrective surgery for scoliosis. However there has been neither experimental data fer explanation of these changes, nor the suggestion of optimal correction method. Therefore, the numerical study was designed to investigate the post-operative changes of vertebral rotation and rib cage deformation after the corrective surgery of scoliosis. A mathematical finite element model of normal spine including rib cage, sternum, both clavicles, and pelvis was developed with anatomical details. In this study, we also developed a special program which could convert a normal spine model to a desired scoliotic spine model automatically. A personalized skeletal deformity of scoliosis model was reconstructed with X-ray images of a scoliosis patient from the normal spine structures and rib cage model. The geometric mapping was performed by translating and rotating the spinal column with an amount analyzed from the digitized 12 built-in coordinate axes in each vertebral image. By utilizing this program, problems generated in mapping procedure such as facet joint overlapping, vertebral body deformity could be automatically resolved.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.1154-1159
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• 2004

Unexpected postoperative changes, such as growth in rib hump, has been occasionally reported after corrective surgery for scoliosis. However there has been experimental data for explanation of these changes, nor the suggestion of optimal correction method. This numerical study was designed to investigate the main correlating elements in operative kinematics with post-operative changes of vertebral rotation and rib cage deformation in the corrective surgery of scoliosis. To develop a scoliotic spine model automatically, a special program for converting normal spine model to scoliotic spine model was developed. A mathematical finite element model of normal spine including rib cage, sternum, both clavicles, and pelvis was developed with anatomical details. The skeletal deformity of scoliosis was reconstructed, by mapping the X-ray images of a scoliosis into this three dimensional normal spine and rib cage model. The geometric mapping was performed by translating and rotating the spinal colume with the amount analyzed from the digitized 12 built-in coordinate axes in each vertebral image. By utilizing this program, problems generated in mapping procedure such as facet joint overlapping, vertebral body deformity could be automatically resolved.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2013.05a
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• pp.965-966
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• 2013

• Journal of Electrical Engineering and Technology
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• v.10 no.6
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• pp.2413-2419
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• 2015

Core spinal muscles are related to trunk stability and assume the main role of stabilizing the spine during daily activities; strengthening of core muscles around the spine can therefore reduce the chance of back pain. The objective of the study was to investigate the effect of core muscle strengthening in the spine during spine stabilization exercise using a whole body tilt device. To achieve this, a validated musculoskeletal (MS) model of the whole body was used to replicate the input motion from the whole body tilting exercise. An inverse dynamics analysis was executed to estimate spine loads and muscle forces depending on the tilting angles of the exercise device. The activation of long and superficial back muscles such as the erector spinae (iliocostalis and longissimus) were mainly affected by the forward direction (-40°) of the tilt, while the front muscles (psoas major, quadratus lumborum, and external and internal obliques) were mainly affected by the backward tilting direction (40°). Deep muscles such as the multifidi and short muscles were activated in most directions of the rotation and tilt. The backward directions of the tilt using this device could be carefully applied for the elderly and for rehabilitation patients who are expected to have less muscle strength. In this study, it was shown that the spine stabilization exercise device can provide considerable muscle exercise effect.

• The Journal of the Korea Contents Association
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• v.11 no.10
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• pp.488-493
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• 2011

The Korean spine geometry and property data for researchers were made by KISTI and Catholic Institute for Applied Anatomy. We took whole spine CT, X-Ray, BMD scan for making high resolution cross-sectional spine images using more 20 donated cadavers(60 - 80 years). Then we constructed 3-dimensional volume model using serial CT images by Mimics software. The major morphometric parameters of vertebrae were measured. Mechanical motion and property data were obtained by the same cadavers using the DEXA for BMD and the spine simulator. The Korean spine geometry and property data could be used for research and development of medical device.

• Journal of Biomedical Engineering Research
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• v.44 no.2
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• pp.139-146
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• 2023

Osteoporosis is a disease in which the risk of bone fractures increases due to a decrease in bone density caused by aging. Osteoporosis is diagnosed by measuring bone density in the total hip, femoral neck, and lumbar spine. To accurately measure bone density in the lumbar spine, the vertebral region must be segmented from the lumbar X-ray image. Deep learning-based automatic spinal segmentation methods can provide fast and precise information about the vertebral region. In this study, we used 695 lumbar spine images as training and test datasets for a deep learning segmentation model. We proposed a lumbar automatic segmentation model, CM-Net, which combines the center point of the spine and the modified U-Net network. As a result, the average Dice Similarity Coefficient(DSC) was 0.974, precision was 0.916, recall was 0.906, accuracy was 0.998, and Area under the Precision-Recall Curve (AUPRC) was 0.912. This study demonstrates a high-performance automatic segmentation model for lumbar X-ray images, which overcomes noise such as spinal fractures and implants. Furthermore, we can perform accurate measurement of bone density on lumbar X-ray images using an automatic segmentation methodology for the spine, which can prevent the risk of compression fractures at an early stage and improve the accuracy and efficiency of osteoporosis diagnosis.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.37 no.10
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• pp.953-959
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• 2013

In this study, a multibody dynamic model of the cervical spine was developed for a virtual in-vitro cadaveric experiment. The dynamic cervical spine model was reconstructed based on Korean CT images and the material properties of joints and soft tissue obtained from in-vitro experimental literature. The model was validated by comparing the inter-segmental rotation, multi-segmental rotations, load-displacement behavior, ligament force, and facet contact force with the published in-vitro experimental data. The results from the model were similar to published experimental data. The developed dynamic model of the cervical spine can be useful for injury analysis to predict the loads and deformations of the individual soft-tissue elements as well as for virtual in-vitro cadaveric experiments.

• Journal of The Korean Society of Integrative Medicine
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• v.8 no.3
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• pp.1-10
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• 2020

Purpose : The purpose of this study is to analyze the corrective effect of the general coordinative manipulation (GCM) joint intervention model on distort leg with imbalance of the lower extremity joints, pelvic and shoulder girdles, and lumbar spine. Methods : The study used a comparative analysis of the size of the distort leg and the imbalance of the lower extremity joints, pelvic and shoulder girdles, and lumbar spine before and after the application of the GCM joint intervention model. A total of 31 subjects from movement center G and the department of physical therapy at university M were selected as research subjects, and they were divided into two groups. The GCM joint intervention model was applied to 18 subjects in the bow knee group and 13 subjects in the knock knee group. The two groups received daily intervention three times a week for four weeks. The corrective effect of the GCM joint intervention model for each type of distort leg was compared and analyzed. Results : The effects of the GCM joint intervention model in correcting bow knee and knock knee with knee deformation and imbalance of the lower extremity joints, pelvic and shoulder girdles, and lumbar spine were significant in most domains (p<.05). The correlation between the bow knee and knock knee groups showed significance in most domains (p<.05). Conclusion : The GCM joint intervention model showed significant corrective effect in the bow knee and knock knee groups in terms of knee deformation, lower extremity joints, pelvic and shoulder girdles, and lumbar spine (p<.05).

• Journal of Korean Neurosurgical Society
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• v.62 no.4
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• pp.442-449
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• 2019

Objective : Bone mineral density (BMD) is an important consideration during fusion surgery. Although dual X-ray absorptiometry is considered as the gold standard for assessing BMD, quantitative computed tomography (QCT) provides more accurate data in spine osteoporosis. However, QCT has the disadvantage of additional radiation hazard and cost. The present study was to demonstrate the utility of artificial intelligence and machine learning algorithm for assessing osteoporosis using Hounsfield units (HU) of preoperative lumbar CT coupling with data of QCT. Methods : We reviewed 70 patients undergoing both QCT and conventional lumbar CT for spine surgery. The T-scores of 198 lumbar vertebra was assessed in QCT and the HU of vertebral body at the same level were measured in conventional CT by the picture archiving and communication system (PACS) system. A multiple regression algorithm was applied to predict the T-score using three independent variables (age, sex, and HU of vertebral body on conventional CT) coupling with T-score of QCT. Next, a logistic regression algorithm was applied to predict osteoporotic or non-osteoporotic vertebra. The Tensor flow and Python were used as the machine learning tools. The Tensor flow user interface developed in our institute was used for easy code generation. Results : The predictive model with multiple regression algorithm estimated similar T-scores with data of QCT. HU demonstrates the similar results as QCT without the discordance in only one non-osteoporotic vertebra that indicated osteoporosis. From the training set, the predictive model classified the lumbar vertebra into two groups (osteoporotic vs. non-osteoporotic spine) with 88.0% accuracy. In a test set of 40 vertebrae, classification accuracy was 92.5% when the learning rate was 0.0001 (precision, 0.939; recall, 0.969; F1 score, 0.954; area under the curve, 0.900). Conclusion : This study is a simple machine learning model applicable in the spine research field. The machine learning model can predict the T-score and osteoporotic vertebrae solely by measuring the HU of conventional CT, and this would help spine surgeons not to under-estimate the osteoporotic spine preoperatively. If applied to a bigger data set, we believe the predictive accuracy of our model will further increase. We propose that machine learning is an important modality of the medical research field.