• 한국운동역학회지
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• 제24권4호
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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.

• 한국정밀공학회지
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• 제23권3호
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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.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2004년도 추계학술대회 논문집
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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.

• 한국정밀공학회:학술대회논문집
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• 한국정밀공학회 2013년도 춘계학술대회 논문집
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• pp.965-966
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• 2013

• Journal of Electrical Engineering and Technology
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• 제10권6호
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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.

• 한국콘텐츠학회논문지
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• 제11권10호
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• pp.488-493
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• 2011

한국과학기술정보연구원과 가톨릭대학교 의과대학 가톨릭응용해부연구소에서는 척추 연구자들이 쉽게 사용할 수 있는 기초 자료를 구축하고 있다. 척추 형상 정보를 제공하기 위해 60-80대 기증시신 20여 표본을 활용하여 고해상도 척추 (whole spine) CT (pixel dimension : 0.4x mm, thickness: 0.6mm)를 촬영하고 이를 3차원 모델링 소프트웨어(Mimics, Ver.14, Materialise, Belgium)를 사용하여 3차원 형상 모델(shell model, STL format)로 구축하고, 목, 등, 허리 척추의 주요 부위를 계측하여 수치화 하였다. 시신기반 자료의 한계를 극복하기 위해 고령자 호발 질환을 중심으로 대상 환자를 선정하여 X-Ray, CT, BMD 자료를 구축하여 보강하고 있다. 물리적 성질 정보 구축은 기증시신 10여 표본을 활용하여 임상적, 물리적 골밀도를 측정하고, 목척추(cervical), 등척추(thoracic), 허리척추(lumbar) 부분의 굽힘-폄(flexion-extension), 가쪽 굽힘(lateral bending), 회전(torsion), 압축(body/disc compression) 시험을 수행하여 작용력과 굽힘량의 관계를 구축하고 있다. 구축된 물성 시험 결과는 형상 모델과 함께 제공되어 자료의 활용도를 높이고 있으며, 이를 이용하여 한국인 특성이 반영된 척추 관련 연구 및 제품 개발에 활용 될 수 있다.

• 대한의용생체공학회:의공학회지
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• 제44권2호
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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.

• 대한기계학회논문집B
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• 제37권10호
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• pp.953-959
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• 2013

본 연구에서는 가상 생체외 사체실험을 수행할 수 있는 경추 다관절 동역학 모델을 개발하였다. 평균크기 한국인 의료영상과 관절 및 연부조직의 물성 정보를 기반으로 하여 경추 동역학 모델을 개발하였다. 개발된 모델의 검증을 위하여 경추 단분절 및 다분절 모멘트-각도 관계, 인대 하중 및 후관절 접촉력 등을 문헌의 사체실험 결과와 비교한 결과 매우 유사한 경향을 확인하였다. 본 연구에서 개발된 경추 동역학 모델은 앞으로 경추 사체실험 연구 뿐만 아니라 자동차 충돌시 경추 상해 분석 등의 다양한 경추 생체역학 연구 연구에 활용될 수 있을 것이다.

• 대한통합의학회지
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• 제8권3호
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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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• 제62권4호
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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.