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Automatic Estimation of Fetal Abdominal Circumference From Ultrasound Images
Jang, Jaeseong,Park, Yejin,Kim, Bukweon,Lee, Sung Min,Kwon, Ja-Young,Seo, Jin Keun IEEE 2018 IEEE Journal of Biomedical and Health Informatics Vol.22 No.5
<P>Ultrasound diagnosis is routinely used in obstetrics and gynecology for fetal biometry, and owing to its time-consuming process, there has been a great demand for automatic estimation. However, the automated analysis of ultrasound images is complicated because they are patient specific, operator dependent, and machine specific. Among various types of fetal biometry, the accurate estimation of abdominal circumference (AC) is especially difficult to perform automatically because the abdomen has low contrast against surroundings, nonuniform contrast, and irregular shape compared to other parameters. We propose a method for the automatic estimation of the fetal AC from two-dimensional ultrasound data through a specially designed convolutional neural network (CNN), which takes account of doctors’ decision process, anatomical structure, and the characteristics of the ultrasound image. The proposed method uses CNN to classify ultrasound images (stomach bubble, amniotic fluid, and umbilical vein) and Hough transformation for measuring AC. We test the proposed method using clinical ultrasound data acquired from 56 pregnant women. Experimental results show that, with relatively small training samples, the proposed CNN provides sufficient classification results for AC estimation through the Hough transformation. The proposed method automatically estimates AC from ultrasound images. The method is quantitatively evaluated and shows stable performance in most cases and even for ultrasound images deteriorated by shadowing artifacts. As a result of experiments for our acceptance check, the accuracies are 0.809 and 0.771 with expert 1 and expert 2, respectively, whereas the accuracy between the two experts is 0.905. However, for cases of oversized fetus, when the amniotic fluid is not observed or the abdominal area is distorted, it could not correctly estimate AC.</P>
대기 중 미세먼지를 포함한 태양광발전 예측 인공지능 모델 개발
윤재성(Jaeseong Yoon),김경민(Kyung-Min Kim),안종화(Johng-Hwa Ahn) 대한환경공학회 2022 대한환경공학회지 Vol.44 No.4
목적: 본 연구는 대기 중 미세먼지를 입력인자에 포함한 단일모델, 하이브리드(hybrid) 모델 등을 상호 비교하여 태양광 발전량 예측을 위한 최적 모델을 제시하고자 한다. 방법: 전라남도 진도군에 있는 1 MW급 태양광 발전량 자료와 목포 지점의 기상 자료와 미세먼지 자료를 2016년 12월에서 2019년 12월까지 사용하였다. 입력 인자는 일사량, 일조 시간, 기압, 기온, 습도, 풍속, 풍향, 적설량, 강수량, PM<SUB>10</SUB>, PM<SUB>2.5</SUB>를 사용하였다. 사용된 모델 중 단일모델로는 랜덤포레스트(random forest, RF), 인공신경망(artificial neural network, ANN), 장단기메모리(long-term dependency problem, LSTM), 게이트 순환 유닛(gate recurrent unit, GRU)을 사용하였으며, 하이브리드 모델로는 LSTM-ANN, GRU-ANN을 사용하였다. 모델의 예측 성능을 비교, 평가하기 위해 결정계수(coefficient of determination, R²), 평균 제곱근 오차(root mean square error, RMSE), 평균절대 오차(mean absolute error, MAE)를 사용하였다. 결과 및 토의: RF를 이용해서 중요도를 확인한 결과, 일사량(77.66%), 일조 시간(4.85%), 기압(4.16%), 기온(3.98%), 습도(2.25%), 풍속(2.21%), PM<SUB>10</SUB>(2.72%), PM<SUB>2.5</SUB>(1.65%), 풍향(1.44%), 적설량(0.05%), 강수량(0.02%)의 순으로 나타났다. GRU-ANN은 모델 중 가장 높은 R²(0.838)를 보였고 학습 조기 종료(early stop)를 사용하여 GRU보다 낮은 epoch(8)를 보였다. 결론: 미세먼지를 포함한 태양광 발전량 예측에 GRU-ANN 모델이 가장 우수하다. Purpose : This study aims to suggest an optimal model for predicting photovoltaic (PV) power generation by comparing single and hybrid models that include particulate matter in the atmosphere as input parameters. Methods : From December 2016 to December 2019, 1 MW-class PV power generation data in Jindo-gun, Jeollanam-do and meteorological data and particulate matter data from Mokpo were used. Radiation, sunshine time, pressure, temperature, humidity, wind speed, wind direction, snow load, precipitation, PM<SUB>10</SUB>, and PM<SUB>2.5</SUB> were used as input parameters. We used single models such as random forest (RF), artificial neural network (ANN), long short-term memory (LSTM), and gate recurrent unit (GRU) and hybrid model such as LSTM-ANN and GRU-ANN. Coefficient of determination (R²), root mean square error (RMSE), and mean absolute error (MAE) were used to compare and evaluate the prediction performance of the models. Results and Discussion : The variable importance through RF was as follows: radiation (77.66%), day light hours (4.85%), pressure (4.16%), temperature (3.98%), humidity (2.25%), wind speed (2.21%), PM<SUB>10</SUB> (2.72%), PM<SUB>2.5</SUB> (1.65%), wind direction (1.44%), snow cover (0.05%), and precipitation (0.02%). GRU-ANN showed the highest R² (0.838) among the models and lower epoch (8) than GRU using the early stop. Conclusion : The GRU-ANN model was the most suitable for forecasting PV power generation including particulate matter.
Ryu, Mikyung,Jo, Jaeseong,Lee, Yunhwan,Chung, Yoon-Sok,Kim, Kwang-Min,Baek, Weon-Chil Oxford University Press 2013 Age and ageing Vol.42 No.6
<P><B>Objective:</B> this study examined the association of physical activity with sarcopenia and sarcopenic obesity among the community-dwelling Korean elderly.</P><P><B>Methods:</B> subjects consisted of 2,264 aged 65 years or older in the 2008–09 Korea National Health and Nutrition Examination Survey. Sarcopenia was defined as 2 SD below the mean of the appendicular skeletal muscle/weight for healthy young adults. Obesity was defined as waist circumference ≥90 cm for men and ≥85 cm for women. Levels of physical activity were classified using the metabolic equivalent task method.</P><P><B>Results:</B> the prevalence of sarcopenia was 12.1% in men and 11.9% in women. Among those with sarcopenia, obesity was prevalent in 68.3% of men and 65.0% of women. Adjusting for all covariates, compared with those with low physical activity, men who engaged in moderate and high activity were 38% and 74%, respectively, less likely to have sarcopenia (<I>P</I><SUB>trend</SUB> < 0.001). In women, the relationship between physical activity and sarcopenia was not significant. For sarcopenic obesity, men participating in moderate [odds ratio (OR) = 0.47; 95% confidence interval (CI) 0.26–0.87] and high (OR = 0.27; 95% CI: 0.12–0.60) physical activity, compared with low activity, had significantly lower risk (<I>P</I><SUB>trend</SUB> = 0.001). In women, high physical activity was associated with a lower risk of sarcopenic obesity (OR = 0.43; 95% CI: 0.22–0.86).</P><P><B>Conclusion:</B> physical activity is associated with a reduced risk of sarcopenia and sarcopenic obesity in older Korean adults. There were gender differences in the relationship, with stronger associations observed in men than in women.</P>
Jang, Kyungho,Kim, Min-Kyoung,Oh, Jaeseong,Lee, SeungHwan,Cho, Joo-Youn,Yu, Kyung-Sang,Choi, Tai Kiu,Lee, Sang-Hyuk,Lim, Kyoung Soo Dove Medical Press 2017 Drug design, development and therapy Vol.11 No.-
<P><B>Purpose</B></P><P>Oseltamivir is widely used in the treatment and prophylaxis of influenza A and B viral infections. It is ingested as an oral prodrug that is rapidly metabolized by carboxylesterase 1 (CES1) to its active form, oseltamivir carboxylate. Dexamethasone is also used in the treatment of acute respiratory distress syndrome, a severe complication of influenza; however, its influence on the pharmacokinetics (PK) of oseltamivir is controversial. The aim of this study was to investigate the effects of coadministering oseltamivir and dexamethasone on the PK of oseltamivir in healthy volunteers.</P><P><B>Methods</B></P><P>An open-label, two-period, one-sequence, multiple-dose study was conducted in 19 healthy male volunteers. Oseltamivir (75 mg) was orally administered on Day 1 and Day 8, and dexamethasone (1.5 mg) was administered once daily from Day 3 to Day 8. Serial blood and urine samples were collected for PK analysis of oseltamivir and oseltamivir carboxylate on Day 1 and Day 8. Oseltamivir and oseltamivir carboxylate concentrations in plasma and urine were determined using liquid chromatography–tandem mass spectrometry.</P><P><B>Results</B></P><P>Area under the plasma concentration–time curve (AUC) of oseltamivir and oseltamivir carboxylate decreased after dexamethasone treatment for 6 days. The geometric mean ratio (90% confidence interval) of the metabolic ratio (oseltamivir carboxylate AUC<SUB>0–48h</SUB>/oseltamivir AUC<SUB>0–48h</SUB>) was 0.92 (0.87–0.97). The amount of unchanged oseltamivir excreted in urine increased by 14% after dexamethasone treatments.</P><P><B>Conclusion</B></P><P>Coadministration of dexamethasone with oseltamivir slightly decreased systemic exposure to oseltamivir and oseltamivir carboxylate in healthy volunteers. This result suggests that CES1 is inhibited by dexamethasone in humans. However, coadministration of oseltamivir and dexamethasone did not appear to have a clinically relevant effect on the PK of oseltamivir; based on these results, dexamethasone can be coadministered with oseltamivir.</P>