AIMC Topic: Pregnancy

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Machine Learning for the Prediction of Surgical Morbidity in Placenta Accreta Spectrum.

American journal of perinatology
OBJECTIVE:  We sought to create a machine learning (ML) model to identify variables that would aid in the prediction of surgical morbidity in cases of placenta accreta spectrum (PAS).

Enhancing trainee performance in obstetric ultrasound through an artificial intelligence system: randomized controlled trial.

Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology
OBJECTIVE: Performing obstetric ultrasound scans is challenging for inexperienced operators; therefore, the prenatal screening artificial intelligence system (PSAIS) software was developed to provide real-time feedback and guidance for trainees durin...

Predicting newborn birth outcomes with prenatal maternal health features and correlates in the United States: a machine learning approach using archival data.

BMC pregnancy and childbirth
BACKGROUND: Newborns are shaped by prenatal maternal experiences. These include a pregnant person's physical health, prior pregnancy experiences, emotion regulation, and socially determined health markers. We used a series of machine learning models ...

Quality of birth care and risk factors of length of stay after birth: A machine learning approach.

The journal of obstetrics and gynaecology research
AIM: Length of stay (LOS) is an outcome measure and is assumed to be related to quality. The objective of this study is to examine the quality of birth care and risk factors associated with LOS after birth.

Prediction of gestational diabetes mellitus by multiple biomarkers at early gestation.

BMC pregnancy and childbirth
BACKGROUND: It remains unclear which early gestational biomarkers can be used in predicting later development of gestational diabetes mellitus (GDM). We sought to identify the optimal combination of early gestational biomarkers in predicting GDM in m...

Explainable artificial intelligence models for predicting pregnancy termination among reproductive-aged women in six east African countries: machine learning approach.

BMC pregnancy and childbirth
Pregnancy termination remains a complex and sensitive issue with approximately 45% of abortions worldwide being unsafe, and 97% of abortions occurring in developing countries. Unsafe pregnancy terminations have implications for women's reproductive h...

Artificial Intelligence-Augmented Clinical Decision Support Systems for Pregnancy Care: Systematic Review.

Journal of medical Internet research
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...

AI in obstetrics: Evaluating residents' capabilities and interaction strategies with ChatGPT.

European journal of obstetrics, gynecology, and reproductive biology
In line with the digital transformation trend in medical training, students may resort to artificial intelligence (AI) for learning. This study assessed the interaction between obstetrics residents and ChatGPT during clinically oriented summative eva...

Prediction of fetal brain gestational age using multihead attention with Xception.

Computers in biology and medicine
Accurate gestational age (GA) prediction is crucial for monitoring fetal development and ensuring optimal prenatal care. Traditional methods often face challenges in terms of precision and prediction efficiency. In this context, leveraging modern dee...

Ensemble machine learning framework for predicting maternal health risk during pregnancy.

Scientific reports
Maternal health risks can cause a range of complications for women during pregnancy. High blood pressure, abnormal glucose levels, depression, anxiety, and other maternal health conditions can all lead to pregnancy complications. Proper identificatio...