This narrative review examines the integration of Artificial Intelligence (AI) in prenatal care, particularly in managing pregnancies complicated by Fetal Growth Restriction (FGR). AI provides a transformative approach to diagnosing and monitoring FG...
This study aimed to predict preterm birth in nulliparous women using machine learning and easily accessible variables from prenatal visits. Elastic net regularized logistic regression models were developed and evaluated using 5-fold cross-validation ...
Fetal health holds paramount importance in prenatal care and obstetrics, as it directly impacts the wellbeing of mother and fetus. Monitoring fetal health through pregnancy is crucial for identifying and addressing potential risks and complications t...
BACKGROUND: Anemia during pregnancy is a significant public health concern, particularly in resource-limited settings. Machine learning (ML) offers promising avenues for improved anemia detection and management. This study investigates the potential ...
BACKGROUND: Sub-Saharan Africa faces high neonatal and maternal mortality rates due to limited access to skilled healthcare during delivery. This study aims to improve the classification of health facilities and home deliveries using advanced machine...
International journal of medical informatics
Nov 7, 2024
BACKGROUND: As artificial intelligence AI-supported applications become integral to web-based information-seeking, assessing their impact on healthy nutrition and weight management during the antenatal period is crucial.
BACKGROUND: The WHO's recommendations on antenatal care underscore the need for ultrasound assessment during pregnancy. Given that maternal and perinatal mortality remains unacceptably high in underserved regions, these guidelines are imperative for ...
BACKGROUND: Despite the emerging application of clinical decision support systems (CDSS) in pregnancy care and the proliferation of artificial intelligence (AI) over the last decade, it remains understudied regarding the role of AI in CDSS specialize...
The value of machine learning capacity in maternal health, and in particular prediction of preeclampsia will only be realised when there are high quality clinical data provided, representative populations included, different health systems and models...
BACKGROUND: National-level coverage estimates of maternal and child health (MCH) services mask district-level and community-level geographical inequities. The purpose of this study is to estimate grid-level coverage of essential MCH services in Niger...
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