AIMC Topic: Prenatal Care
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Advancing prenatal healthcare by explainable AI enhanced fetal ultrasound image segmentation using U-Net++ with attention mechanisms.
Scientific reports
Jun 4, 2025
Prenatal healthcare development requires accurate automated techniques for fetal ultrasound image segmentation. This approach allows standardized evaluation of fetal development by minimizing time-exhaustive processes that perform poorly due to human...
Leveraging artificial intelligence for inclusive maternity care: Enhancing access for mothers with disabilities in Africa.
Women's health (London, England)
Mar 16, 2025
Women with disabilities face significant barriers in accessing maternal healthcare, which increases their risk of adverse pregnancy outcomes, particularly in Africa, where resources are limited. Artificial intelligence (AI) presents a unique opportun...
Causal machine learning models for predicting low birth weight in midwife-led continuity care intervention in North Shoa Zone, Ethiopia.
BMC medical informatics and decision making
Feb 7, 2025
BACKGROUND: Low birth weight (LBW) is a critical global health issue that affects infants disproportionately, particularly in developing countries. This study adopted causal machine learning (CML) algorithms for predicting LBW in newborns, drawing fr...
A survey of obstetric ultrasound uses and priorities for artificial intelligence-assisted obstetric ultrasound in low- and middle-income countries.
Scientific reports
Jan 31, 2025
Obstetric ultrasound (OBUS) is recommended as part of antenatal care for pregnant individuals worldwide. To better understand current uses of OBUS in low- and middle-income countries and perceptions regarding potential use of artificial intelligence ...
Artificial Intelligence in Fetal Growth Restriction Management: A Narrative Review.
Journal of clinical ultrasound : JCU
Jan 29, 2025
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...
Predicting preterm birth using electronic medical records from multiple prenatal visits.
BMC pregnancy and childbirth
Dec 21, 2024
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 ...
Pregnancy
Female
Infant, Newborn
Adult
Sensitivity and Specificity
Premature Birth
Young Adult
Humans
Machine Learning
Electronic Health Records
Gestational Age
Cervical Length Measurement
Ultrasonography, Prenatal
Logistic Models
Prenatal Care
Predictive Value of Tests
Risk Assessment
Area Under Curve
Automated approach for fetal and maternal health management using light gradient boosting model with SHAP explainable AI.
Frontiers in public health
Dec 20, 2024
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...
Leveraging machine learning models for anemia severity detection among pregnant women following ANC: Ethiopian context.
BMC public health
Dec 18, 2024
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 ...
Predicting place of delivery choice among childbearing women in East Africa: a comparative analysis of advanced machine learning techniques.
Frontiers in public health
Nov 27, 2024
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...
Healthy nutrition and weight management for a positive pregnancy experience in the antenatal period: Comparison of responses from artificial intelligence models on nutrition during pregnancy.
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.
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