AIMC Topic: Prenatal Care

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Machine learning meets maternal health: Uncovering spatial blind spots in antenatal care quality in Bangladesh.

PloS one
BACKGROUND: High-quality antenatal care (ANC) is defined as four or more antenatal visits with at least one to a medically trained provider, measurement of weight and blood pressure, testing of blood and urine, and receipt of information on potential...

The Effectiveness of an Artificial Intelligence-Based Gamified Intervention for Improving Maternal Health Outcomes Among Refugees and Underserved Women in Lebanon: Community Interventional Trial.

JMIR mHealth and uHealth
BACKGROUND: In Lebanon, disadvantaged pregnant women show poor maternal outcomes due to limited access to antenatal care (ANC) and a strained health care system, compounded by ongoing conflicts and a significant refugee population. Despite substantia...

Machine learning algorithms for predicting and identifying the influencing predictors of antenatal care visits among women in Bangladesh: Evidence from BDHS 2022 data.

PloS one
BACKGROUND AND OBJECTIVE: Bangladesh, a South Asian country, continues to face significant challenges in maternal health, as reflected by its high maternal mortality ratio (MMR). According to the 2022 Bangladesh Demographic and Health Survey (BDHS), ...

Applying machine learning to predict quality ANC determinants in Bangladesh: a BDHS-2022 cross-sectional study.

Scientific reports
Quality antenatal care (ANC) is critical for maternal and neonatal health. Despite improvements in healthcare, disparities in ANC access and quality persist, particularly in underserved areas of Bangladesh. This study aimed to identify the key determ...

Influencing factors for childbirth readiness among pregnant women based on the reciprocal determinism theory and backpropagation neural network: a cross-sectional study in China.

BMC pregnancy and childbirth
BACKGROUND: Childbirth readiness is essential for improving maternal health outcomes and reducing mortality, yet preparedness remains low among pregnant women globally. This study aims to identify key factors influencing childbirth readiness among Ch...

AI-enabled obstetric point-of-care ultrasound as an emerging technology in low- and middle-income countries: provider and health system perspectives.

BMC pregnancy and childbirth
BACKGROUND: In many low- and middle-income countries (LMICs), widespread access to obstetric ultrasound is challenged by lack of trained providers, workload, and inadequate resources required for sustainability. Artificial intelligence (AI) is a powe...

Advancing prenatal healthcare by explainable AI enhanced fetal ultrasound image segmentation using U-Net++ with attention mechanisms.

Scientific reports
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)
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
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
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 ...