Preeclampsia is one of the leading causes of maternal and fetal morbidity and mortality. Due to the lack of effective preventive measures, its prediction is essential to its prompt management. This study aimed to develop models using machine learning to predict late-onset preeclampsia using hospital electronic medical record data. The performance of the machine learning based models and models using conventional statistical methods were also compared. A total of 11,006 pregnant women who received antenatal care at Yonsei University Hospital were included. Maternal data were retrieved from electronic medical records during the early second trimester to 34 weeks. The prediction outcome was late-onset preeclampsia occurrence after 34 weeks’ gestation. Pattern recognition and cluster analysis were used to select the parameters included in the prediction models. Logistic regression, decision tree model, naïve Bayes classification, support vector machine, random forest algorithm, and stochastic gradient boosting method were used to construct the prediction models. C-statistics was used to assess the performance of each model. The overall preeclampsia development rate was 4.7% (474 patients). Systolic blood pressure, serum blood urea nitrogen and creatinine levels, platelet counts, serum potassium level, white blood cell count, serum calcium level, and urinary protein were the most influential variables included in the prediction models. C-statistics for the decision tree model, naïve Bayes classification, support vector machine, random forest algorithm, stochastic gradient boosting method, and logistic regression models were 0.857, 0.776, 0.573, 0.894, 0.924, and 0.806, respectively. The stochastic gradient boosting model had the best prediction performance with an accuracy and false positive rate of 0.973 and 0.009, respectively. The combined use of maternal factors and common antenatal laboratory data of the early second trimester through early third trimester could effectively predict late-onset preeclampsia using machine learning algorithms. Future prospective studies are needed to verify the clinical applicability algorithms.

Recent advances in canine intestinal organoids have expanded the option for building a better in vitro model to investigate translational science of intestinal physiology and pathology between humans and animals. However, the three-dimensional geometry and the enclosed lumen of canine intestinal organoids considerably hinder the access to the apical side of epithelium for investigating the nutrient and drug absorption, host-microbiome crosstalk, and pharmaceutical toxicity testing. Thus, the creation of a polarized epithelial interface accessible from apical or basolateral side is critical. Here, we demonstrated the generation of an intestinal epithelial monolayer using canine biopsy-derived colonic organoids (colonoids). We optimized the culture condition to form an intact monolayer of the canine colonic epithelium on a nanoporous membrane insert using the canine colonoids over 14 days. Transmission and scanning electron microscopy revealed a physiological brush border interface covered by the microvilli with glycocalyx, as well as the presence of mucin granules, tight junctions, and desmosomes. The population of stem cells as well as differentiated lineagedependent epithelial cells were verified by immunofluorescence staining and RNA in situ hybridization. The polarized expression of P-glycoprotein efflux pump was confirmed at the apical membrane. Also, the epithelial monolayer formed tight-and adherence-junctional barrier within 4 days, where the transepithelial electrical resistance and apparent permeability were inversely correlated. Hence, we verified the stable creation, maintenance, differentiation, and physiological function of a canine intestinal epithelial barrier, which can be useful for pharmaceutical and biomedical researches.

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.

Our method achieved a Dice similarity metric of [Formula: see text] for AC measurement and an accuracy of 87.10% for our acceptance check of the fetal abdominal standard plane.

Ultrasound-based fetal biometric measurements, such as head circumference (HC) and biparietal diameter (BPD), are commonly used to evaluate the gestational age and diagnose fetal central nervous system (CNS) pathology. Since manual measurements are operator-dependent and time-consuming, there have been numerous researches on automated methods. However, existing automated methods still are not satisfactory in terms of accuracy and reliability, owing to difficulties in dealing with various artifacts in ultrasound images. This paper focuses on fetal head biometry and develops a deep-learning-based method for estimating HC and BPD with a high degree of accuracy and reliability. The proposed method effectively identifies the head boundary by differentiating tissue image patterns with respect to the ultrasound propagation direction. The proposed method was trained with 102 labeled data set and tested to 70 ultrasound images. We achieved a success rate of 92.31% for HC and BPD estimations, and an accuracy of 87.14% for the plane acceptance check.Index Terms-ultrasound, fetal head biometry, machine learning arXiv:1808.06150v1 [physics.med-ph]