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Pregnancy

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Associations of maternal stress, gene expression, and newborn birthweight in the Democratic Republic of Congo.

American journal of biological anthropology
OBJECTIVES: Maternal stress has long been associated with lower birthweight, which is associated with adverse health outcomes including many adult diseases. The underlying mechanisms remain elusive although changes in gene expression may play a role....

What is the future of artificial intelligence in obstetrics? A qualitative study among healthcare professionals.

BMJ open
OBJECTIVE: This work explores the perceptions of obstetrical clinicians about artificial intelligence (AI) in order to bridge the gap in uptake of AI between research and medical practice. Identifying potential areas where AI can contribute to clinic...

Machine learning based assessment of preclinical health questionnaires.

International journal of medical informatics
BACKGROUND: Within modern health systems, the possibility of accessing a large amount and a variety of data related to patients' health has increased significantly over the years. The source of this data could be mobile and wearable electronic system...

Transfer learning for accurate fetal organ classification from ultrasound images: a potential tool for maternal healthcare providers.

Scientific reports
Ultrasound imaging is commonly used to aid in fetal development. It has the advantage of being real-time, low-cost, non-invasive, and easy to use. However, fetal organ detection is a challenging task for obstetricians, it depends on several factors, ...

Deep Learning Model Based on Multisequence MRI Images for Assessing Adverse Pregnancy Outcome in Placenta Accreta.

Journal of magnetic resonance imaging : JMRI
BACKGROUND: Preoperative assessment of adverse outcomes risk in placenta accreta spectrum (PAS) disorders is of high clinical relevance for perioperative management and prognosis.

The neglected emotional drawbacks of the prioritization of embryos to transfer.

Reproductive biomedicine online
In recent years, increasing efforts have been made to develop advanced techniques that could predict the potential of implantation of each single embryo and prioritize the transfer of those at higher chance. The most promising include non-invasive pr...

Segment-Based Unsupervised Learning Method in Sensor-Based Human Activity Recognition.

Sensors (Basel, Switzerland)
Sensor-based human activity recognition (HAR) is a task to recognize human activities, and HAR has an important role in analyzing human behavior such as in the healthcare field. HAR is typically implemented using traditional machine learning methods....

A multimodal deep learning model for predicting severe hemorrhage in placenta previa.

Scientific reports
Placenta previa causes life-threatening bleeding and accurate prediction of severe hemorrhage leads to risk stratification and optimum allocation of interventions. We aimed to use a multimodal deep learning model to predict severe hemorrhage. Using M...

Deep learning for estimation of fetal weight throughout the pregnancy from fetal abdominal ultrasound.

American journal of obstetrics & gynecology MFM
BACKGROUND: Fetal weight is currently estimated from fetal biometry parameters using heuristic mathematical formulas. Fetal biometry requires measurements of the fetal head, abdomen, and femur. However, this examination is prone to inter- and intraob...

Development and validation of an artificial intelligence assisted prenatal ultrasonography screening system for trainees.

International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
OBJECTIVE: Fetal anomaly screening via ultrasonography, which involves capturing and interpreting standard views, is highly challenging for inexperienced operators. We aimed to develop and validate a prenatal-screening artificial intelligence system ...