• 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.

• 한국식품위생안전성학회지
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• 제25권1호
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• pp.24-29
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• 2010

한국인의 주류 섭취로 인한 중금속 위해 영향 여부를 파악하기 위하여 국내산 및 수입산 주류를 대상으로 납, 카드뮴, 비소 및 수은을 분석하고, 일일식품섭취량을 고려하여 이 들 식품 섭취에 대한 위해 평가를 수행하였다. 시료 분석결과로 발효주류 중 납 함량은(${\mu}g/kg$) 11.1(N.D~66.5), 카드뮴 7.6(N.D~47.1), 비소 24.6(1~140.0), 수은 $0.7\;{\pm}\;1.2$(N.D~10.6), 증류주류 중 납 함량은(${\mu}g/kg$) 9.1(N.D~33.4), 카드뮴 1.8(N.D~19.6), 비소 14.9(N.D~209.7), 수은 0.3(N.D~2.3)이었으며 기타주류 중 납 함량은(${\mu}g/kg$) 14.0(N.D~37.8), 카드뮴 10.3(0.6~17.4), 비소 33.6(0.5~103.3), 수은 0.3(0.1~0.5)이었다. 본 연구에서 얻어진 탁주, 약주, 청주, 맥주, 과실주, 소주, 위스키, 브랜디, 일반증류주, 리큐르, 기타주류 중 중금속 함량은 국내 외 문헌들과 유사하거나 낮게 확인 되었으며, 식품별 1일섭취량을 고려한 납, 카드뮴, 비소 및 수은의 주간섭취량은 FAO/WHO의 PTWI와 비교할 때 각각 0.03%, 0.06%, 0.01%, 0.01%이하로 안전한 수준으로 판단된다.