OBJECTIVES: Congenital heart defects (CHDs) are the most common birth defects. Recently, artificial intelligence (AI) was used to assist in CHD diagnosis. No comparison has been made among the various types of algorithms that can assist in the prenat...
Medical Assisted Reproduction proved its efficacy to treat the vast majority forms of infertility. One of the key procedures in this treatment is the selection and transfer of the embryo with the highest developmental potential. To assess this potent...
About one in ten babies is born preterm, i.e., before completing 37 weeks of gestation, which can result in permanent neurologic deficit and is a leading cause of child mortality. Although imminent preterm labor can be detected, predicting preterm bi...
Hypertension research : official journal of the Japanese Society of Hypertension
May 9, 2023
In the first trimester of pregnancy, accurately predicting the occurrence of pregnancy-induced hypertension (PIH) is important for both identifying high-risk women and adopting early intervention. In this study, we used four machine-learning models (...
American journal of obstetrics and gynecology
Apr 26, 2023
OBJECTIVE: This study aimed to investigate the accuracy of convolutional neural network models in the assessment of embryos using time-lapse monitoring.
OBJECTIVES: To study whether the free energy principle can explain fetal brain activity and the existence of fetal consciousness via a chaotic dimension derived using artificial intelligence.
Ultrasound imaging is a valuable tool for assessing the development of the fetal during pregnancy. However, interpreting ultrasound images manually can be time-consuming and subject to variability. Automated image categorization using machine learnin...
CTG (Cardiotocography) is an effective tool for fetal status assessment. Clinically, doctors mainly evaluate the health of fetus by observing FHR (fetal heart rate). The rapid development of Artificial Intelligence has led realization of computer-aid...
RESEARCH QUESTION: Can a multi-modal fusion model based on ultrasound-based deep learning radiomics combined with clinical parameters provide personalized evaluation of endometrial receptivity and predict the occurrence of clinical pregnancy after fr...
While domain-specific data augmentation can be useful in training neural networks for medical imaging tasks, such techniques have not been widely used to date. Our objective was to test whether domain-specific data augmentation is useful for medical ...