Preterm birth is a major cause of mortality and lifelong morbidity in childhood. Its complex and multifactorial origins limit the effectiveness of current clinical predictors and impede optimal care. In this study, a dual-branch deep learning architecture (PUUMA) was developed to predict gestational age (GA) at birth using T2* fetal MRI data from 295 pregnancies, encompassing a heterogeneous and imbalanced population. The model integrates both global whole-uterus and local placental features. Its performance was benchmarked against linear regression using cervical length measurements obtained by experienced clinicians from anatomical MRI and other Deep Learning architectures. The GA at birth predictions were assessed using mean absolute error. Accuracy, sensitivity, and specificity were used to assess preterm classification. Both the fully automated MRI-based pipeline and the cervical length regression achieved comparable mean absolute errors (3 weeks) and good sensitivity (0.67) for detecting preterm birth, despite pronounced class imbalance in the dataset. These results provide a proof of concept for automated prediction of GA at birth from functional MRI, and underscore the value of whole-uterus functional imaging in identifying at-risk pregnancies. Additionally, we demonstrate that manual, high-definition cervical length measurements derived from MRI, not currently routine in clinical practice, offer valuable predictive information. Future work will focus on expanding the cohort size and incorporating additional organ-specific imaging to improve generalisability and predictive performance.

A deep learning model predicts preterm births by analyzing electrical activity in the woman's uterus during pregnancy. Click the article to learn more. OH, BABY! – New AI technology could help ensure healthier preterm births. AI FAIL – Find out how a chatbot flunked a public health crisis test. Adults over age 60 who want to stave off memory loss could benefit from taking a daily multivitamin supplement, suggests a recent study.

Fox News medical contributor Dr. Marc Siegel joins'Fox & Friends' to discuss the benefits of artificial intelligence in the medical industry if used with caution. About 10% of all infants born in the U.S. in 2021 were preterm -- which means they were delivered earlier than 37 weeks of pregnancy, per the Centers for Disease Control and Prevention (CDC). Preterm births also make up about 16% of infant deaths. Now, researchers from Washington University in St. Louis, Missouri, are looking to improve those odds through the use of artificial intelligence. They developed a deep learning model that can predict preterm births by analyzing electrical activity in the woman's uterus during pregnancy -- then they tested the model in a study that was published in the medical journal PLOS One.

Over 800 women died from pregnancy-related complications in 2020 in the United States, and well over half of these deaths were preventable. This isn’t news for professionals who work in this space because maternal deaths in the U.S. outnumber those in most industrialized nations in the west. Maternal outcomes for Black women in the US are even worse. Black mothers die from pregnancy-related complications nearly three times more frequently than white and Hispanic women. Despite efforts to prioritize improved maternal mortality, there’s been very little progress. In fact, maternal mortality actually increased over 18% between 2019 and 2020. Much of this increase is a result of the Covid-19 pandemic. For excess deaths that aren’t attributable to complications from Covid-19, technology may hold the solution. Artificial Intelligence may improve maternal health outcomes in the US Many tech companies focused on developing and implementing AI-driven solutions to healthcare problems in the last few years. These recent advancements in AI give patients and their healthcare providers hope amidst worsening maternal health outcomes in the United States. Some of the newest iterations of AI for healthcare identify pregnant women who are at risk for premature birth and other complications. In doing so, it allows healthcare providers and patient care teams to intervene before it’s too late. As a result, their pregnant patients benefit from health education, as well as medical care and access to social services to lower their risk for negative outcomes. There’s even an element of this technology that leverages the social determinants of health (SDOH). If a pregnant woman has a history of preterm birth or preeclampsia, is struggling to find food, or they can’t get to their appointments because of inadequate transportation, AI helps their healthcare provider understand how those influence maternal and infant health outcomes. This way, healthcare providers can address them early, giving pregnant patients their best chance at positive outcomes. Identifying at-risk pregnant women earlier with AI Traditional methods of care limited healthcare providers’ efforts to identify at-risk pregnant women especially in comparison to what we’ve been able to accomplish with AI and other technology in recent years. Before these advances, OB/GYNs and midwives were tasked with using often incomplete data along with persistence and sheer luck. They did their best to call patients with no guarantee that they would reach them. Many OB/GYNs and midwives did their best, but pregnant moms deserve so much more than that—especially as we face climbing maternal mortality around the world. Now, maternal care providers don’t have to spend long hours rounding up patient data and hoping to catch pregnant patients on the phone. AI has the power to provide all of that data, along with a complete analysis that identifies which patients are at the highest risk. The result? Identifying pregnancies sooner and targeted outreach efforts that prioritize high-risk patients. Healthcare providers who use AI in maternal care can identify over 70% of at-risk moms during the first trimester. This incredible benefit allows these parents to get the care they need from the very beginning of their prenatal journey. Earlier access to care allows for better outcomes and interventions for both mothers and their babies. And AI prepares the folks providing care and doing patient outreach ahead of time so they know exactly how to support each and every patient. Insurance plans are also using this AI to significantly reduce preterm birth disparities for pregnant Black women across the nation. There was a 10% reduction in preterm births and low birth weight among babies whose mothers received care and support leveraging AI tools. This helps avoid lifelong consequences for newborns, including breathing, hearing, and vision issues, developmental delays, and other health complications. The benefits of AI don’t end when a pregnant woman gives birth, though. It continues collecting and analyzing patient data into the fourth trimester, when women are still at risk for life-threatening pregnancy-related complications, like preeclampsia and mental health challenges like postpartum depression or psychosis. Lawmakers are taking maternal mortality to task Tech companies and healthcare providers aren’t the only stakeholders trying to improve maternal mortality rates in the US. It’s become a legislative priority for many folks on Capitol Hill, including President Joe Biden and Vice President Kamala Harris. Together with many federal agencies, they released the White House Blueprint for Addressing the Maternal Health Crisis just last year. This historic effort aims to improve maternal health outcomes by providing more economic and social support to women before, during, and after pregnancy while also prioritizing research and data collection among other key agenda items. Recently, the National Governors Association (NGA) Chair New Jersey Governor Phil Murphy and First Lady Tammy Snyder Murphy hosted a roundtable discussion about efforts to improve birth outcomes and reduce overall maternal and infant mortality and morbidity. One area of focus that Mrs. Murphy highlighted included the significant influence “data holds to transform our national maternal health landscape [ … ] with up-to-date information, we can create policies that are informed by reality. We can direct our strategy to target specific goals. And, most importantly, we can measure our performance and make essential revisions, change direction and learn from our experiences.” Additionally, administrations in states such as Texas and Florida are including maternal health goals in their Medicaid managed care contracts. As the government continues working toward better maternal healthcare, AI and other technology will undoubtedly be a key piece of the puzzle to improve health outcomes and reduce health disparities. Photo: FatCamera, Getty Images

Perinatal, as per the Oxford Advanced Learner's Dictionary, means at or around the time of birth. The Farlex Partner Medical Dictionary defined it as, "occurring during, or pertaining to, the periods before, during, or after the time of birth; that is, before delivery from the 22nd week of gestation through the first 28 days after delivery." Perinatal period care, or the care provided to expectant mothers and the respective child during pregnancy, childbirth, and the postpartum period, is a crucial aspect of the healthcare domain. One key aspect of perinatal care is the use of USG or ultrasound scans, which use high-frequency sound waves to create images of the developing foetus, placenta, and surrounding maternal structures. Analysis of a USG scan, like many other medical scans, requires the expert eyes of a radiologist or gynaecologist to decipher various symptoms beyond the overall well-being parameters mapping the gestation period. Artificial intelligence (AI), Computer Vision (CV) and Machine Learning (ML), offer groundbreaking possibilities in analysing data from USG scans in order to improve the quality and effectiveness of perinatal care.

Almost 400,000 babies were born prematurely--before 37 weeks gestation--in 2018 in the United States. One of the leading causes of newborn deaths and long-term disabilities, preterm birth (PTB) is considered a public health problem with deep emotional and challenging financial consequences to families and society. If doctors were able to use data and artificial intelligence (AI) to predict which pregnant women might be at risk, many of these premature births might be avoided. "Premature birth prediction has been an exceedingly challenging problem," said Ansaf Salleb-Aouissi, a senior lecturer in discipline from the computer science department. "But we are now at a point where we can use machine learning to develop a dynamic risk prediction system for pregnant women. Creating a system that can process large models of data with AI algorithms we develop would be a great benefit to supplement physicians' 'real-life' expertise."

Information extracted from electrohysterography recordings could potentially prove to be an interesting additional source of information to estimate the risk on preterm birth. Recently, a large number of studies have reported near-perfect results to distinguish between recordings of patients that will deliver term or preterm using a public resource, called the Term/Preterm Electrohysterogram database. However, we argue that these results are overly optimistic due to a methodological flaw being made. In this work, we focus on one specific type of methodological flaw: applying oversampling before partitioning the data into mutually exclusive training and testing sets. We show how this causes the results to be biased using two artificial datasets and reproduce results of studies in which this flaw was identified. Moreover, we evaluate the actual impact of oversampling on predictive performance, when applied prior to data partitioning, using the same methodologies of related studies, to provide a realistic view of these methodologies' generalization capabilities. We make our research reproducible by providing all the code under an open license. Keywords: preterm birth risk estimation · oversampling · electrohysterogra-phy 1 Introduction Giving birth before 37 weeks of pregnancy, which is referred to as preterm birth, has a significant negative impact on the expected outcome of the neonate. According to the World Health Organization (WHO), preterm birth is one of the arXiv:2001.06296v1

In this paper we are interested in the prediction of preterm birth based on diagnosis codes from longitudinal EHR. We formulate the prediction problem as a supervised classification with noisy labels. Our base classifier is a Recurrent Neural Network with an attention mechanism. We assume the availability of a data subset with both noisy and clean labels. For the cohort definition, most of the diagnosis codes on mothers' records related to pregnancy are ambiguous for the definition of full-term and preterm classes. On the other hand, diagnosis codes on babies' records provide fine-grained information on prematurity. Due to data de-identification, the links between mothers and babies are not available. We developed a heuristic based on admission and discharge times to match babies to their mothers and hence enrich mothers' records with additional information on delivery status. The obtained additional dataset from the matching heuristic has noisy labels and was used to leverage the training of the deep learning model. We propose an Alternating Loss Correction (ALC) method to train deep models with both clean and noisy labels. First, the label corruption matrix is estimated using the data subset with both noisy and clean labels. Then it is used in the model as a dense output layer to correct for the label noise. The network is alternately trained on epochs with the clean dataset with a simple cross-entropy loss and on next epoch with the noisy dataset and a loss corrected with the estimated corruption matrix. The experiments for the prediction of preterm birth at 90 days before delivery showed an improvement in performance compared with baseline and state of-the-art methods.

We describe an application of machine learning to the problem of predicting preterm birth. We conduct a secondary analysis on a clinical trial dataset collected by the National In- stitute of Child Health and Human Development (NICHD) while focusing our attention on predicting different classes of preterm birth. We compare three approaches for deriving predictive models: a support vector machine (SVM) approach with linear and non-linear kernels, logistic regression with different model selection along with a model based on decision rules prescribed by physician experts for prediction of preterm birth. Our approach highlights the pre-processing methods applied to handle the inherent dynamics, noise and gaps in the data and describe techniques used to handle skewed class distributions. Empirical experiments demonstrate significant improvement in predicting preterm birth compared to past work.

Preterm births occur at an alarming rate of 10-15%. Preemies have a higher risk of infant mortality, developmental retardation and long-term disabilities. Predicting preterm birth is difficult, even for the most experienced clinicians. The most well-designed clinical study thus far reaches a modest sensitivity of 18.2-24.2% at specificity of 28.6-33.3%. We take a different approach by exploiting databases of normal hospital operations. We aims are twofold: (i) to derive an easy-to-use, interpretable prediction rule with quantified uncertainties, and (ii) to construct accurate classifiers for preterm birth prediction. Our approach is to automatically generate and select from hundreds (if not thousands) of possible predictors using stability-aware techniques. Derived from a large database of 15,814 women, our simplified prediction rule with only 10 items has sensitivity of 62.3% at specificity of 81.5%.