• Journal of the Korean Society of Radiology
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• v.18 no.3
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• pp.187-201
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• 2024

Osteoporosis is a major health issue globally, often remaining undetected until a fracture occurs. To facilitate early detection, deep learning (DL) models were developed to classify osteoporosis using abdominal computed tomography (CT) scans. This study was conducted using retrospectively collected data from 3,012 contrast-enhanced abdominal CT scans. The DL models developed in this study were constructed for using image data, demographic/clinical information, and multi-modality data, respectively. Patients were categorized into the normal, osteopenia, and osteoporosis groups based on their T-scores, obtained from dual-energy X-ray absorptiometry, into normal, osteopenia, and osteoporosis groups. The models showed high accuracy and effectiveness, with the combined data model performing the best, achieving an area under the receiver operating characteristic curve of 0.94 and an accuracy of 0.80. The image-based model also performed well, while the demographic data model had lower accuracy and effectiveness. In addition, the DL model was interpreted by gradient-weighted class activation mapping (Grad-CAM) to highlight clinically relevant features in the images, revealing the femoral neck as a common site for fractures. The study shows that DL can accurately identify osteoporosis stages from clinical data, indicating the potential of abdominal CT scans in early osteoporosis detection and reducing fracture risks with prompt treatment.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.135-149
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• 2020

Artificial intelligences are changing world. Financial market is also not an exception. Robo-Advisor is actively being developed, making up the weakness of traditional asset allocation methods and replacing the parts that are difficult for the traditional methods. It makes automated investment decisions with artificial intelligence algorithms and is used with various asset allocation models such as mean-variance model, Black-Litterman model and risk parity model. Risk parity model is a typical risk-based asset allocation model which is focused on the volatility of assets. It avoids investment risk structurally. So it has stability in the management of large size fund and it has been widely used in financial field. XGBoost model is a parallel tree-boosting method. It is an optimized gradient boosting model designed to be highly efficient and flexible. It not only makes billions of examples in limited memory environments but is also very fast to learn compared to traditional boosting methods. It is frequently used in various fields of data analysis and has a lot of advantages. So in this study, we propose a new asset allocation model that combines risk parity model and XGBoost machine learning model. This model uses XGBoost to predict the risk of assets and applies the predictive risk to the process of covariance estimation. There are estimated errors between the estimation period and the actual investment period because the optimized asset allocation model estimates the proportion of investments based on historical data. these estimated errors adversely affect the optimized portfolio performance. This study aims to improve the stability and portfolio performance of the model by predicting the volatility of the next investment period and reducing estimated errors of optimized asset allocation model. As a result, it narrows the gap between theory and practice and proposes a more advanced asset allocation model. In this study, we used the Korean stock market price data for a total of 17 years from 2003 to 2019 for the empirical test of the suggested model. The data sets are specifically composed of energy, finance, IT, industrial, material, telecommunication, utility, consumer, health care and staple sectors. We accumulated the value of prediction using moving-window method by 1,000 in-sample and 20 out-of-sample, so we produced a total of 154 rebalancing back-testing results. We analyzed portfolio performance in terms of cumulative rate of return and got a lot of sample data because of long period results. Comparing with traditional risk parity model, this experiment recorded improvements in both cumulative yield and reduction of estimated errors. The total cumulative return is 45.748%, about 5% higher than that of risk parity model and also the estimated errors are reduced in 9 out of 10 industry sectors. The reduction of estimated errors increases stability of the model and makes it easy to apply in practical investment. The results of the experiment showed improvement of portfolio performance by reducing the estimated errors of the optimized asset allocation model. Many financial models and asset allocation models are limited in practical investment because of the most fundamental question of whether the past characteristics of assets will continue into the future in the changing financial market. However, this study not only takes advantage of traditional asset allocation models, but also supplements the limitations of traditional methods and increases stability by predicting the risks of assets with the latest algorithm. There are various studies on parametric estimation methods to reduce the estimated errors in the portfolio optimization. We also suggested a new method to reduce estimated errors in optimized asset allocation model using machine learning. So this study is meaningful in that it proposes an advanced artificial intelligence asset allocation model for the fast-developing financial markets.

• Radiation Oncology Journal
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• v.18 no.2
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• pp.157-166
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• 2000

Purpose : This investigation was peformed in order to improve the health care of radiation workers, to predict a risk, to minimize the radiation exposure hazard to them and for them to realize radiation exposure danger when they work in radiation area in hospital. Methods and Materials : The documentations checked regularly for personal radiation exposure in four university hospitals in Pusan city in Korea between January 1, 1993 and December 31, 1997 were analyzed. There were 458 persons in this documented but 111 persons who worked less then one year were excluded and only 347 persons were included in this study. Results : The average of yearly radiation exposure of 347 persons was 1.52$\pm$1.35 mSv. Though it was less than 50mSv, the limitaion of radiation in law but 125 (36%) people received higher radiation exposure than non-radiation workers. Radiation workers under 30 year old have received radiation exposure of mean 1.87$\pm$1.01 mSv/year, mean 1.22$\pm$0.69 mSv between 31 and 40 year old and mean 0.97$\pm$0.43 mSv/year over 41year old (p<0.001). Men received mean 1.67$\pm$1.54 mSv/year were higher than women who received mean 1.13$\pm$0.61 mSv/year (p<0.01). Radiation exposure in the department of nuclear modicine department in spite of low energy sources is higher than other departments that use radiations in hospital (p<0.05). And the workers who received mean 3.59$\pm$1.81 msv/year in parts of management of radiation sources and injection of sources to patient receive high radiation exposure in nuclear medicine department (p<0.01). In department of diagnostic radiology high radiation exposure is in barium enema rooms where workers received mean 3.74$\pm$1.74 mSv/year and other parts where they all use fluoroscopy such as angiography room of mean 1.17$\pm$0.35 mSv/year and upper gastrointestinal room of mean 1.74$\pm$1.34 mSv/year represented higher radiation exposure than average radiation exposure in diagnostic radiology (p<0.01). Doctors and radiation technologists received higher radiation exposure of each mean 1.75$\pm$1.17 mSv/year and mean 1.50$\pm$1.39 mSv/year than other people who work in radiation area in hospital (p<0.05). Especially young doctors and technologists have the high opportunity to receive higher radiation exposure. Conclusions : The training and education of radiation workers for radiation exposure risks are important and it is necessary to rotate worker in short period in high risk area. The hospital management has to concern health of radiation workers more and to put an effort to reduce radiation exposure as low as possible in radiation areas in hospital.

• Radiation Oncology Journal
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• v.23 no.3
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• pp.143-156
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• 2005

Background: The best dose-fractionation regimen of the definitive radiotherapy for cervix cancer remains to be clearly determined. It seems to be partially attributed to the complexity of the affecting factors and the lack of detailed information on external and intra-cavitary fractionation. To find optimal practice guidelines, our experiences of the combination of external beam radiotherapy (EBRT) and high-dose-rate intracavitary brachytherapy (HDR-ICBT) were reviewed with detailed information of the various treatment parameters obtained from a large cohort of women treated homogeneously at a single institute. Materials and Methods: The subjects were 743 cervical cancer patients (Stage IB 198, IIA 77, IIB 364, IIIA 7, IIIB 89 and IVA 8) treated by radiotherapy alone, between 1990 and 1996. A total external beam radiotherapy (EBRT) dose of $23.4\~59.4$ Gy (Median 45.0) was delivered to the whole pelvis. High-dose-rate intracavitary brachytherapy (HDR-IBT) was also peformed using various fractionation schemes. A Midline block (MLB) was initiated after the delivery of $14.4\~43.2$ Gy (Median 36.0) of EBRT in 495 patients, while In the other 248 patients EBRT could not be used due to slow tumor regression or the huge initial bulk of tumor. The point A, actual bladder & rectal doses were individually assessed in all patients. The biologically effective dose (BED) to the tumor ($\alpha/\beta$=10) and late-responding tissues ($\alpha/\beta$=3) for both EBRT and HDR-ICBT were calculated. The total BED values to point A, the actual bladder and rectal reference points were the summation of the EBRT and HDR-ICBT. In addition to all the details on dose-fractionation, the other factors (i.e. the overall treatment time, physicians preference) that can affect the schedule of the definitive radiotherapy were also thoroughly analyzed. The association between MD-BED $Gy_3$ and the risk of complication was assessed using serial multiple logistic regression models. The associations between R-BED $Gy_3$ and rectal complications and between V-BED $Gy_3$ and bladder complications were assessed using multiple logistic regression models after adjustment for age, stage, tumor size and treatment duration. Serial Coxs proportional hazard regression models were used to estimate the relative risks of recurrence due to MD-BED $Gy_{10}$, and the treatment duration. Results: The overall complication rate for RTOG Grades $1\~4$ toxicities was $33.1\%$. The 5-year actuarial pelvic control rate for ail 743 patients was $83\%$. The midline cumulative BED dose, which is the sum of external midline BED and HDR-ICBT point A BED, ranged from 62.0 to 121.9 $Gy_{10}$ (median 93.0) for tumors and from 93.6 to 187.3 $Gy_3$ (median 137.6) for late responding tissues. The median cumulative values of actual rectal (R-BED $Gy_3$) and bladder Point BED (V-BED $Gy_3$) were 118.7 $Gy_3$ (range $48.8\~265.2$) and 126.1 $Gy_3$ (range: $54.9\~267.5$), respectively. MD-BED $Gy_3$ showed a good correlation with rectal (p=0.003), but not with bladder complications (p=0.095). R-BED $Gy_3$ had a very strong association (p=<0.0001), and was more predictive of rectal complications than A-BED $Gy_3$. B-BED $Gy_3$ also showed significance in the prediction of bladder complications in a trend test (p=0.0298). No statistically significant dose-response relationship for pelvic control was observed. The Sandwich and Continuous techniques, which differ according to when the ICR was inserted during the EBRT and due to the physicians preference, showed no differences in the local control and complication rates; there were also no differences in the 3 vs. 5 Gy fraction size of HDR-ICBT. Conclusion: The main reasons optimal dose-fractionation guidelines are not easily established is due to the absence of a dose-response relationship for tumor control as a result of the high-dose gradient of HDR-ICBT, individual differences In tumor responses to radiation therapy and the complexity of affecting factors. Therefore, in our opinion, there is a necessity for individualized tailored therapy, along with general guidelines, in the definitive radiation treatment for cervix cancer. This study also demonstrated the strong predictive value of actual rectal and bladder reference dosing therefore, vaginal gauze packing might be very Important. To maintain the BED dose to less than the threshold resulting in complication, early midline shielding, the HDR-ICBT total dose and fractional dose reduction should be considered.