Korea University · 医学
Professor Ki Hoon Ahn's research lab specializes in maternal-fetal medicine with a strong focus on leveraging artificial intelligence and advanced medical imaging to improve the prediction and diagnosis of pregnancy complications. The lab investigates the application of machine learning models—such as neural networks, support vector machines, and random forests—to diverse clinical data types, including ultrasound, electrohysterographic signals, and fetal imaging, for early detection of preterm birth, fetal growth restriction, and other adverse outcomes. The lab also explores anatomical and placental markers, such as placental thickness-to-weight ratios and placenta previa, to predict neonatal morbidity like respiratory distress syndrome. Ethical considerations in AI-driven perinatal care are also a growing focus in their translational research.
Figures are computed from collected data and may differ slightly.
This study reviews the current status and future prospective of knowledge on the use of artificial intelligence for the prediction of spontaneous preterm labor and birth ("preterm birth" hereafter). The summary of review suggests that different machine learning approaches would be optimal for different types of data regarding the prediction of preterm birth: the artificial neural network, logistic regression and/or the random forest for numeric data; the support vector machine for electrohystero
This study reviews recent advances on the application of artificial intelligence for the early diagnosis of various maternal-fetal conditions such as preterm birth and abnormal fetal growth. It is found in this study that various machine learning methods have been successfully employed for different kinds of data capture with regard to early diagnosis of maternal-fetal conditions. With the more popular use of artificial intelligence, ethical issues should also be considered accordingly.
The last step of a successful pregnancy is the safe delivery of the fetus. An important question is if the delivery should vaginal or operative. In addition to the use of conventional antenatal ultrasound, the use of intrapartum ultrasound to evaluate fetal head station, position, cervical ripening, and placental separation is promising. This review evaluates and summarizes the usefulness of intrapartum ultrasound for the evaluation of labor progress and predicting successful operative vaginal d
This study investigated whether anterior placenta previa in the second trimester is associated with neonatal respiratory distress syndrome (RDS). The neonates delivered by 2067 women between 2007 and 2015 were evaluated for the presence of RDS through birth records. The location of the placenta and the presence of placenta previa during the second and third trimesters were assessed and recorded. Demographic, prenatal, and perinatal records were reviewed. Anterior placenta previa in the second an
We report a case of prenatally diagnosed congenital perineal mass which was combined with anorectal malformation. The mass was successfully treated with posterior sagittal anorectoplasty postnatally. On ultrasound examination at a gestational age of 23 weeks the fetal perineal mass were found on the right side. Any other defects were not visible on ultrasonography during whole gestation. Amniocentesis was performed to evaluate the fetal karyotyping and acetylcholinesterase which were also normal
This study aimed to determine the correlation between the placental thickness-to-estimated foetal weight ratio on midterm ultrasonography and small-for-gestational-age (SGA) infants. In this retrospective study, the placental thickness at the umbilical cord insertion site was measured and adjusted for foetal body weight at 18-24 weeks gestation. Investigators compared the data of women who delivered SGA infants (birth weight <10th percentile) with those of women who delivered non-SGA infants. Am
The results of this meta-analysis suggest that administration of preventive progestogen treatment to women at risk for preterm birth does not appear to negatively affect neonatal mortality in single or multiple pregnancies regardless of the route of administration.
To the best of our knowledge, this is the first study to systematically analyze COVID-19 and vaccination details during pregnancy using the national health insurance claims data in Korea. The obstetric outcomes in pregnant women with and without COVID-19 and their newborns were similar.
This study uses machine learning and population data to analyze major determinants of preterm birth including depression and particulate matter. Retrospective cohort data came from Korea National Health Insurance Service claims data for 405,586 women who were aged 25-40 years and gave births for the first time after a singleton pregnancy during 2015-2017. The dependent variable was preterm birth during 2015-2017 and 90 independent variables were included (demographic/socioeconomic information, p
We investigated changes in gene expression of cervical collagens, matrix metalloproteinases (MMPs), and tissue inhibitors of metalloproteinases (TIMPs) during pre-gestational uterine cervical excision and/or inflammation-induced preterm labor in mice. Forty sexually mature female mice were uniformly divided into four groups: sham, cervical excision, lipopolysaccharide (LPS) injection, and cervical excision plus LPS injection. Partial cervical tissue excision was performed at five weeks of age be
This study presents the most comprehensive machine-learning analysis for the predictors of blood transfusion, all-cause mortality, and hospitalization period in COVID-19 patients. Data came from Korea National Health Insurance claims data with 7943 COVID-19 patients diagnosed during November 2019−May 2020. The dependent variables were all-cause mortality and the hospitalization period, and their 28 independent variables were considered. Random forest variable importance (GINI) was introduced for
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