• Korean Journal of Radiology
• /
• v.24 no.9
• /
• pp.827-837
• /
• 2023

Objective: To investigate the predictive value of radiomics features based on cardiac magnetic resonance (CMR) cine images for left ventricular adverse remodeling (LVAR) after acute ST-segment elevation myocardial infarction (STEMI). Materials and Methods: We conducted a retrospective, single-center, cohort study involving 244 patients (random-split into 170 and 74 for training and testing, respectively) having an acute STEMI (88.5% males, 57.0 ± 10.3 years of age) who underwent CMR examination at one week and six months after percutaneous coronary intervention. LVAR was defined as a 20% increase in left ventricular end-diastolic volume 6 months after acute STEMI. Radiomics features were extracted from the oneweek CMR cine images using the least absolute shrinkage and selection operator regression (LASSO) analysis. The predictive performance of the selected features was evaluated using receiver operating characteristic curve analysis and the area under the curve (AUC). Results: Nine radiomics features with non-zero coefficients were included in the LASSO regression of the radiomics score (RAD score). Infarct size (odds ratio [OR]: 1.04 (1.00-1.07); P = 0.031) and RAD score (OR: 3.43 (2.34-5.28); P < 0.001) were independent predictors of LVAR. The RAD score predicted LVAR, with an AUC (95% confidence interval [CI]) of 0.82 (0.75-0.89) in the training set and 0.75 (0.62-0.89) in the testing set. Combining the RAD score with infarct size yielded favorable performance in predicting LVAR, with an AUC of 0.84 (0.72-0.95). Moreover, the addition of the RAD score to the left ventricular ejection fraction (LVEF) significantly increased the AUC from 0.68 (0.52-0.84) to 0.82 (0.70-0.93) (P = 0.018), which was also comparable to the prediction provided by the combined microvascular obstruction, infarct size, and LVEF with an AUC of 0.79 (0.65-0.94) (P = 0.727). Conclusion: Radiomics analysis using non-contrast cine CMR can predict LVAR after STEMI independently and incrementally to LVEF and may provide an alternative to traditional CMR parameters.

• Korean Journal of Radiology
• /
• v.21 no.7
• /
• pp.869-879
• /
• 2020

Objective: To evaluate the performance of a convolutional neural network (CNN) model that can automatically detect and classify rib fractures, and output structured reports from computed tomography (CT) images. Materials and Methods: This study included 1079 patients (median age, 55 years; men, 718) from three hospitals, between January 2011 and January 2019, who were divided into a monocentric training set (n = 876; median age, 55 years; men, 582), five multicenter/multiparameter validation sets (n = 173; median age, 59 years; men, 118) with different slice thicknesses and image pixels, and a normal control set (n = 30; median age, 53 years; men, 18). Three classifications (fresh, healing, and old fracture) combined with fracture location (corresponding CT layers) were detected automatically and delivered in a structured report. Precision, recall, and F1-score were selected as metrics to measure the optimum CNN model. Detection/diagnosis time, precision, and sensitivity were employed to compare the diagnostic efficiency of the structured report and that of experienced radiologists. Results: A total of 25054 annotations (fresh fracture, 10089; healing fracture, 10922; old fracture, 4043) were labelled for training (18584) and validation (6470). The detection efficiency was higher for fresh fractures and healing fractures than for old fractures (F1-scores, 0.849, 0.856, 0.770, respectively, p = 0.023 for each), and the robustness of the model was good in the five multicenter/multiparameter validation sets (all mean F1-scores > 0.8 except validation set 5 [512 x 512 pixels; F1-score = 0.757]). The precision of the five radiologists improved from 80.3% to 91.1%, and the sensitivity increased from 62.4% to 86.3% with artificial intelligence-assisted diagnosis. On average, the diagnosis time of the radiologists was reduced by 73.9 seconds. Conclusion: Our CNN model for automatic rib fracture detection could assist radiologists in improving diagnostic efficiency, reducing diagnosis time and radiologists' workload.

• Journal of the Korean Data and Information Science Society
• /
• v.23 no.5
• /
• pp.895-904
• /
• 2012

This study aims to examine the effects of a 12-week compound exercise program (aerobic exercise+weight training) on obese middle school girls' leptin and insulin before and after the exercise. This is achieved by dividing obese middle school girls whose body fat percentage is over 30% into a compound exercise group (n=20) and a control group (n=20) and conducting comparative analysis on them.After the Shapiro-Wilk normality test of the variables, a two-sample t-test was performed to see if the variables have the same mean between the compound exercise and control groups. A paired t-test was also performed to see if the changes in the variables before and after the compound exercise program were statistically significant. For all the statistical analysis, the significance level was set at ${\alpha}=0.05$. The results of this study showed the leptin and insulin levels in the combined exercise group had been significantly decreased. The regular 12 weeks of combined exercise is considered to have a positive impact on leptin and insulin levels in obese schoolgirls.

• Journal of Life Science
• /
• v.18 no.2
• /
• pp.193-199
• /
• 2008

The purpose of this study was to demonstrate the effectiveness of walking and band exercise for 12 weeks on c-reactive protein and cardiovascular disease risk factor in overweight and obese children. Body composition, blood lipids, insulin sensitivity, c-reactive protein (CRP) were assessed before, after 4 weeks and after 12 weeks of combined exercise. Sixteen participants (BMI${\geq}$21.3) were randomly allocated to exercise group (n=8) and control group (n=8). The exercise group participated in 50 minutes of walking exercise and band exercises as resistance training two days a week for 12 weeks. There were significant different on weight (p<0.001), BMI (p<0.001), fat mass (p<0.001), fat percentage (p<0.001), LBM percentage (p<0.001), TG (p<0.05), HDL-C (p<0.01), insulin (p<0.05), HOMA-IR (p<0.05) in exercise group after intervention. And the change of weight (p<0.001), BMI (p<0.001), fat mass (p<0.001), fat percentage (p<0.001), LBM mass (p<0.05), LBM percentage (p<0.001), insulin (p<0.05), HOMA-IR (p<0.05) were significant difference between groups after intervention. These findings suggest that 12 weeks of walking and band exercise can be useful intervention in the improvement of cardiovascular disease risk factor in overweight and obese children. But c-reactive protein was no change.

• Journal of National Security and Military Science
• /
• s.7
• /
• pp.271-303
• /
• 2009

"Ubiquitous sensor network" definition is this-Someone attaches electro-magnetic tag everything which needs communication between man to man, man to material and material to material(Ubiquitous). By using attached every electro-magnetic tag, someone detects it's native information as well as environmental information such as temperature, humidity, pollution and infiltration information(Sensor). someone connects it realtime network and manage generated information(Network). 21st century's war is joint combined operation connecting with ground, sea and air smoothly in digitalized war field, and is systematic war provided realtime information from sensor to shooter. So, it needs dramatic development on watch reconnaissance, command and control, pinpoint strike etc. Ubiquitous computing and network technologies are essential in national defense to operate 21st century style war. It is possible to use many parts such as USN combined smart dust and sensor network to protect friend unit as well as to watch enemy's deep area by unmanned reconnaissance, wearable computer upgrading soldier's operational ability and combat power dramatically, RFID which can be used material management as well as on time support. Especially, unmanned watch system using USN is core part to transit network centric military service and to get national defense efficiency which overcome the dilemma of national defense person resource reducing, and upgrade guard quality level, and improve combat power by normalizing guardian's bio rhythm. According to the test result of sensor network unmanned watch system, it needs more effort and time to stabilize because of low USN technology maturity and using maturity. In the future, USN unmanned watch system project must be decided the application scope such as application area and starting point by evaluating technology maturity and using maturity. And when you decide application scope, you must consider not only short period goal as cost reduction, soldier decrease and guard power upgrade but also long period goal as advanced defense ability strength. You must build basic infra in advance such as light cable network, frequency allocation and power facility etc. First of all, it must get budget guarantee and driving force for USN unmanned watch system project related to defense policy. You must forwarded the USN project assuming posses of operation skill as procedure, system, standard, training in advance. Operational skill posses is come from step by step application strategy such as test phase, introduction phase, spread phase, stabilization phase and also repeated test application taking example project.

• Journal of The Korean Society of Integrative Medicine
• /
• v.8 no.3
• /
• pp.181-188
• /
• 2020

Purpose : The purpose of this study is to compare the effects on abdominal muscle thickness and breathing by applying trunk strength exercise and deep stabilization exercise along with breathing exercise, which is the main respiratory muscle during breathing, to present an efficient exercise method with diaphragm breathing. Methods : This study was performed on normal 6 females and 14 males subjects. They were divided into 2 groups which trunk strength exercise and deep stabilization exercise group. The trunk strength exercise group (TSE) attended prone press-up, crunch and pelvic tiling. The deep stabilization exercise group (DSE) attended abdominal drawing, horizontal side-support and bridging exercise. Breathing exercise was performed for each set break time for 1 minute. Results : First, in the comparison of the change in the thickness of the abdominal muscle between the trunk strength training group and the deep stabilization group before and after exercise, there was a statistically significant difference in the comparison of transverse abdominis (TrA), rectus femoris (RF), external oblique (EO), internal oblique (IO) (p<.05). However, there was no significant difference in any comparison between groups (p>.05). Second, in the comparison of changes in respiratory function between the trunk strength exercise group and the deep stabilization exercise group before and after exercise, there were statistically significant differences in the exerted forced vital capacity (FVC), forced expiratory volume at one second (FEV₁), peak expiratory flow (PEF) in the comparison before and after the experiment (p<.05). However, there was no significant difference in any comparison between groups (p>.05). Conclusion : As a result of this study, it can be said that both trunk strength exercises and deep stabilization exercises along with diaphragm breathing are exercises that strengthen deep and superficial muscles, and have a positive effect on breathing function as well as muscle strength. However, it is not known which exercise was more effective, and because it was combined with breathing exercise, the interference effect appeared.

• Journal of Life Science
• /
• v.20 no.1
• /
• pp.33-39
• /
• 2010

The purpose of this study was to investgate combined effects of L-carnitine supplementation and exercise on body composition, serum lipids and adiponectin in obese high school female students. Eighteen female students with 35% fat in body weight participated in the study. Students were randomly divided into the following three groups; exercise and L-carnitine supplementation group (ELG: n=5), exercise group (EG: n=6) and control group (CG: n=7), each with seven students. They underwent 10 weeks of exercise (50 min/day, 5 times/wk, 10 wk, RPE 11~16). ELG was given L-carnitine (1 g/day), EG and CG were given placebos. Before and after this period, body composition, serum lipids and adiponectin in plasma were measured. The results of the study in the three groups were as follows: Fat mass and %BF were significantly decreased in ELG. On the other hand, free fat mass was significantly increased in ELG, however, other groups showed no changes. Total cholesterol was significantly increased in the control group. High density lipoprotein cholesterol and low density lipoprotein cholesterol were not different in the three groups. Triglyceride was significantly decreased in ELG. Adiponectin was significantly increased in ELG. This study demonstrated that exercise and carnitine supplementation have a positive effect on fat mass, %BF, free fat mass and adiponetin. Thus, we can improve proper dietary and training programs for obese students.

• Journal of Navigation and Port Research
• /
• v.38 no.5
• /
• pp.437-442
• /
• 2014

Ocean Literacy is an understanding of the ocean's influence on you and your influence on the ocean. This research developed the 7 ocean education model courses using ocean literacy based on the analysis of ocean education programs which executed 23 agencies in Busan. These model courses are combined in the type of indoor theory, indoor experience, field study and field experience. Also, this makes the guide map for ocean education in a 76cm*56cm size to distinguish and choose the course easily. This map is the format combined in geological location and tourist attraction spots in Busan, includes education centers, contents, lead time and so on, and it is possible for educatees to handle their preference and seasonality elastically. This map including ocean education model course is a milestone to activate ocean education, and is helpful to reach the goal of ocean education and to lead ocean professionals. In addition, this research presents the development of teaching materials, training aids to complement the weakness of indoor education, the development of cyber education through making video contents as the activation measures of ocean education.

• Journal of Korean Tunnelling and Underground Space Association
• /
• v.20 no.6
• /
• pp.1161-1175
• /
• 2018

An unexpected event could be easily followed by a large secondary accident due to the limitation in sight of drivers in road tunnels. Therefore, a series of automated incident detection systems have been under operation, which, however, appear in very low detection rates due to very low image qualities on CCTVs in tunnels. In order to overcome that limit, deep learning based tunnel incident detection system was developed, which already showed high detection rates in November of 2017. However, since the object detection process could deal with only still images, moving direction and speed of moving vehicles could not be identified. Furthermore it was hard to detect stopping and reverse the status of moving vehicles. Therefore, apart from the object detection, an object tracking method has been introduced and combined with the detection algorithm to track the moving vehicles. Also, stopping-reverse discrimination algorithm was proposed, thereby implementing into the combined incident detection processes. Each performance on detection of stopping, reverse driving and fire incident state were evaluated with showing 100% detection rate. But the detection for 'person' object appears relatively low success rate to 78.5%. Nevertheless, it is believed that the enlarged richness of image big-data could dramatically enhance the detection capacity of the automatic incident detection system.

• Journal of Wetlands Research
• /
• v.25 no.4
• /
• pp.267-273
• /
• 2023

Algae are an important component of the ecosystem. However, the excessive growth of cyanobacteria has various harmful effects on river environments, and diatoms affect the management of water supply processes. Algal monitoring is essential for sustainable and efficient algae management. In this study, an object detection model was developed that detects and classifies images of four types of harmful cyanobacteria used for the criteria of the algae alert system, and one diatom, Synedra sp.. You Only Look Once(YOLO) v8, the latest version of the YOLO model, was used for the development of the model. The mean average precision (mAP) of the base model was analyzed as 64.4. Five models were created to increase the diversity of the input images used for model training by performing rotation, magnification, and reduction of original images. Changes in model performance were compared according to the composition of the input images. As a result of the analysis, the model that applied rotation, magnification, and reduction showed the best performance with mAP 86.5. The mAP of the model that only used image rotation, combined rotation and magnification, and combined image rotation and reduction were analyzed as 85.3, 82.3, and 83.8, respectively.