• Journal of radiological science and technology
• /
• v.31 no.1
• /
• pp.25-31
• /
• 2008

Purpose : This study was conducted to understand for the diagnostic discordance of bone mineral density(BMD) in DEXA(Dual Energy X-ray Absorptiometry). And the diagnostic difference between lumbar spine and femur neck, forearm was evaluated using T-score. Materials and Methods : We studied 220 females measured BMD on lumbar spine, and femur neck, forearm including ward's triangle and ultra digital(UD). We were distinguished T-score into normal, osteopenia, osteoporosis(WHO classification) and evaluated discordance rate according to age and degree of bone loss. Correlation analysis and chi-square test between L-spine, L-4, femur neck, Ward, Forearm, UD were carried out. Results : In the lumbar spine, the number of normal were in 57(25.9%), osteopenia in 86(39.1%), osteoporosis in 77(35.0%). In the L-4 and ward's triangle, the number of osteoporosis were in 78(35.5%), in 126(57.3%). There was significant correlation between lumbar, femur neck and forearm BMD in all cases. The discordance of BMD between lumbar and femur were 57%, lumbar and forearm 43%, forearm and femur 51%. The discordance rates of normal, osteopenic, osteoporotic groups were 39%, 64%, 43%, respectively, showing the highest discordance rate in osteopenia patients. In normal group of lumbar spine, the discordance rate was 25%, 23%, 11%, 65%, 86% in 30', 40', 50', 60', 70', respectively. In osteopenia, osteoporosis group of lumbar spine, the discordance rate was 62%, 55%, 36%, 20%, 9% in 30', 40', 50', 60', 70', respectively. Conclusion : It was different of the results of BMD with lumbar, femur and forearm site. The discordance rate was decreased with age in osteopenia, osteoporosis lumbar spine. In osteopenia group, the discordance rate was the highest. So, it is necessary that the BMD of lumbar, femur neck and forearm should be checked.

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