OBJECTIVE: To evaluate combinations of candidate biomarkers to develop a multiplexed prediction model for identifying the viability and location of an early pregnancy. In this study, we assessed 24 biomarkers with multiple machine learning-based meth...
OBJECTIVE: Many fetal anomalies can already be diagnosed by ultrasound in the first trimester of pregnancy. Unfortunately, in clinical practice, detection rates for anomalies in early pregnancy remain low. Our aim was to use an automated image segmen...
INTRODUCTION: Ultrasonography in the first trimester of pregnancy offers an early screening tool to identify high risk pregnancies. Artificial intelligence (AI) algorithms have the potential to improve the accuracy of diagnosis and assist the clinici...
RESEARCH QUESTION: Can an artificial intelligence embryo selection assistant predict the incidence of first-trimester spontaneous abortion using static images of IVF embryos?
Journal of child psychology and psychiatry, and allied disciplines
Jan 29, 2024
BACKGROUND: Understanding the prenatal origins of children's psychopathology is a fundamental goal in developmental and clinical science. Recent research suggests that inflammation during pregnancy can trigger a cascade of fetal programming changes t...
BACKGROUND/AIM: To evaluate whether there is a relationship between serum myosin-binding protein C (MyBP-C) levels measured in the first trimester and the timing of delivery, and, if a relationship is detected, the potential of this relationship in d...
Archives of gynecology and obstetrics
Jul 21, 2023
PURPOSE: Short- and long-term complications of gestational diabetes mellitus (GDM) involving pregnancies and offspring warrant the development of an effective individualized risk prediction model to reduce and prevent GDM together with its associated...
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 (...
OBJECTIVE: To develop and internally validate a deep-learning algorithm from fetal ultrasound images for the diagnosis of cystic hygromas in the first trimester.
IMPORTANCE: Accurate screening of trisomy 21 in the first trimester can provide an early opportunity for decision-making regarding reproductive choices.
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