Cardiotocography (CTG) is a low-cost, non-invasive fetal health assessment technique used globally, especially in underdeveloped countries. However, it is currently mainly used to identify the fetus's current status (e.g., fetal acidosis or hypoxia), and the potential of CTG in predicting future adverse pregnancy outcomes has not been fully explored. We aim to develop an AI-based model that predicts biological age from CTG time series (named CTGage), then calculate the age gap between CTGage and actual age (named CTGage-gap), and use this gap as a new digital biomarker for future adverse pregnancy outcomes. The CTGage model is developed using 61,140 records from 11,385 pregnant women, collected at Peking University People's Hospital between 2018 and 2022. For model training, a structurally designed 1D convolutional neural network is used, incorporating distribution-aligned augmented regression technology. The CTGage-gap is categorized into five groups: < -21 days (underestimation group), -21 to -7 days, -7 to 7 days (normal group), 7 to 21 days, and > 21 days (overestimation group). We further defined the underestimation group and overestimation group together as the high-risk group. We then compare the incidence of adverse outcomes and maternal diseases across these groups. The average absolute error of the CTGage model is 10.91 days. When comparing the overestimation group with the normal group, premature infants incidence is 5.33% vs. 1.42% (p < 0.05) and gestational diabetes mellitus (GDM) incidence is 31.93% vs. 20.86% (p < 0.05). When comparing the underestimation group with the normal group, low birth weight incidence is 0.17% vs. 0.15% (p < 0.05) and anaemia incidence is 37.51% vs. 34.74% (p < 0.05). Artificial intelligence-derived CTGage can predict the future risk of adverse pregnancy outcomes and hold potential as a novel, non-invasive, and easily accessible digital biomarker.

The 3D ultrasound (US) entrance inspires a multitude of automated prenatal examinations. However, studies about the structuralized description of the whole fetus in 3D US are still rare. In this paper, we propose to estimate the 3D pose of fetus in US volumes to facilitate its quantitative analyses in global and local scales. Given the great challenges in 3D US, including the high volume dimension, poor image quality, symmetric ambiguity in anatomical structures and large variations of fetal pose, our contribution is three-fold. (i) This is the first work about 3D pose estimation of fetus in the literature. We aim to extract the skeleton of whole fetus and assign different segments/joints with correct torso/limb labels. (ii) We propose a self-supervised learning (SSL) framework to finetune the deep network to form visually plausible pose predictions. Specifically, we leverage the landmark-based registration to effectively encode case-adaptive anatomical priors and generate evolving label proxy for supervision. (iii) To enable our 3D network perceive better contextual cues with higher resolution input under limited computing resource, we further adopt the gradient check-pointing (GCP) strategy to save GPU memory and improve the prediction. Extensively validated on a large 3D US dataset, our method tackles varying fetal poses and achieves promising results. 3D pose estimation of fetus has potentials in serving as a map to provide navigation for many advanced studies.

Kilinc, who works in the lab of biological engineer Linda Griffith at MIT, is among a small group of scientists using new tools akin to miniature organs to study a poorly understood--and frequently problematic--part of human physiology: menstruation. Heavy, sometimes debilitating periods strike at least a third of people who menstruate at some point in their lives, causing some to miss weeks of work or school every year and jeopardizing their professional standing. Anemia threatens about two-thirds of people with heavy periods. And when menstrual blood flows through the fallopian tubes and into the body cavity, it's thought to sometimes create painful lesions--characteristics of a disease called endometriosis, which can require multiple surgeries to control. No one is entirely sure how--or why--the human body choreographs this monthly dance of cellular birth, maturation, and death.

Welcome to Pushing Buttons, the Guardian's gaming newsletter. If you'd like to receive it in your inbox every week, just pop your email in below – and check your inbox (and spam) for the confirmation email.Sign up for Pushing Buttons, our weekly guide to what's going on in video games. The New York Times's acquisition of viral word game Wordle has not been without its controversies: some players are convinced that the words have become more obscure (remember CAULK? I've felt a vague sense of dissatisfaction with it myself since late February, though I'm not sure how much of that is a natural drop-off from the times of Wordle mania, and how much has anything to do with the game itself. This week, though, there was a genuine controversy when the NYT decided to remove the word "fetus" as a solution to one of last week's puzzles.

To assess fetal health during pregnancy, doctors use the gestational age (GA) calculation based on the Crown Rump Length (CRL) measurement in order to check for fetal size and growth trajectory. However, GA estimation based on CRL, requires proper positioning of calipers on the fetal crown and rump view, which is not always an easy plane to find, especially for an inexperienced sonographer. Finding a slightly oblique view from the true CRL view could lead to a different CRL value and therefore incorrect estimation of GA. This study presents an AI-based method for a quality assessment of the CRL view by verifying 7 clinical scoring criteria that are used to verify the correctness of the acquired plane. We show how our proposed solution achieves high accuracy on the majority of the scoring criteria when compared to an expert. We also show that if such scoring system is used, it helps identify poorly acquired images accurately and hence may help sonographers acquire better images which could potentially lead to a better assessment of conditions such as Intrauterine Growth Restriction (IUGR).

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. Non-invasive prenatal testing (NIPT) on pregnant women to detect the risk of a fetus having rare genetic abnormalities may often be wrong, according to recent reports. These tests, according to multiple health experts, can actually give false positives, which can create significant angst in expecting parents. Health experts explained to Fox News that NIPT works by taking blood samples from the pregnant mother and then analyzing fragments of free-floating cell-free DNA (cfDNA).

In perinatal medicine, fetal weight is a sort of Goldilocks problem: it has to be just right. If the fetus is too small, it may not be developing properly; too large and the mother faces much greater risks in childbirth. The trouble is, there is no way to directly measure fetal weight. Instead, doctors must rely on estimates that are calculated using a formula that includes the fetus's head and abdominal circumference, and the length of the femur. Unfortunately, this formula isn't always as accurate as doctors would like.

Kirk Ouimet: Grateful to have the opportunity to speak with you today. Wise Being: I am always happy to help if I can. Kirk Ouimet: Do you know who Johnny Five is? Kirk Ouimet: Who is he? Wise Being: He is an artificial intelligence. Wise Being: He was created in a laboratory for a military application.

In this article we focus on detecting hypoxia (or oxygen deprivation), a very serious condition that can arise from different pathologies and can lead to lifelong disability and death. We present a novel approach to hypoxia detection based on recordings of the uterine pressure and fetal heart rate, which are obtained using standard labor monitoring devices. The key idea is to learn models of the fetal response to signals from its environment. Then, we use the parameters of these models as attributes in a binary classification problem. A running count of pathological classifications over several time periods is taken to provide the current label for the fetus.

This past week, physicians at Children's Hospital in Philadelphia announced that they'd had remarkable success with keeping lamb fetuses alive outside a womb--in a plastic bag filled with warm amniotic fluid, with the fetus' heart circulating blood through a filter to keep it oxygenated. Astonishing pictures of wee unborn laminated lambs quickly spun up the media science-fiction reference engine. Someday, that might be a human baby floating in a next-gen artificial uterus. Talking heads name-checked Gattaca and Brave New World. You could get a whiff of Blade Runner in there.