AI Medical Compendium Journal:
BMC pregnancy and childbirth

Showing 1 to 10 of 54 articles

Artificial intelligence-assisted chatbot: impact on breastfeeding outcomes and maternal anxiety.

BMC pregnancy and childbirth
BACKGROUND: Artificial intelligence (AI) is increasingly used in healthcare interventions to provide accessible, continuous, and personalized patient support. This study investigates the impact of a mobile breastfeeding counseling application develop...

Investigation of serum neuroserpin levels in pregnant women diagnosed with pre-eclampsia: a prospective case-control study.

BMC pregnancy and childbirth
OBJECTIVE: Neuroserpin, a serine protease inhibitor, is recognized for its anti-inflammatory and neuroprotective properties. Given the central role of inflammation and neurological involvement in the pathophysiology of preeclampsia, this study aimed ...

Maternal and umbilical cord plasma purine concentrations after oral carbohydrate loading prior to elective Cesarean delivery under spinal anesthesia: a randomized controlled trial.

BMC pregnancy and childbirth
OBJECTIVE: To evaluate the effect of preoperative intake of oral carbohydrates versus standard preoperative fasting prior to elective cesarean delivery on plasma purine levels (hypoxanthine, xanthine, and uric acid) and beta-hydroxybutyrate (β-HB) in...

Integrating SHAP analysis with machine learning to predict postpartum hemorrhage in vaginal births.

BMC pregnancy and childbirth
OBJECTIVE: This study aimed to develop a machine learning (ML) model integrated with SHapley Additive exPlanations (SHAP) analysis to predict postpartum hemorrhage (PPH) following vaginal deliveries, offering a potential tool for personalized risk as...

Exploring the potential of cell-free RNA and Pyramid Scene Parsing Network for early preeclampsia screening.

BMC pregnancy and childbirth
BACKGROUND: Circulating cell-free RNA (cfRNA) is gaining recognition as an effective biomarker for the early detection of preeclampsia (PE). However, the current methods for selecting disease-specific biomarkers are often inefficient and typically on...

Effectiveness and clinical impact of using deep learning for first-trimester fetal ultrasound image quality auditing.

BMC pregnancy and childbirth
BACKGROUND: Regular auditing of ultrasound images is required to maintain quality; however, manual auditing is time-consuming and can be inconsistent. We therefore aimed to develop and validate an artificial intelligence-based image quality audit (AI...

Accurate prediction of mediolateral episiotomy risk during labor: development and verification of an artificial intelligence model.

BMC pregnancy and childbirth
OBJECTIVE: The study developed an intelligent online evaluation system for mediolateral episiotomy, which incorporated machine learning algorithms and integrated maternal physiological data collected during delivery.

Machine learning combined with infrared spectroscopy for detection of hypertension pregnancy: towards newborn and pregnant blood analysis.

BMC pregnancy and childbirth
Biochemical changes in the cervix during labor are not well understood. This gap in knowledge is significant, as understanding the precise biochemical processes can provide critical insights into the mechanisms of labor and potentially inform better ...

Predictive modeling of pregnancy outcomes utilizing multiple machine learning techniques for in vitro fertilization-embryo transfer.

BMC pregnancy and childbirth
OBJECTIVE: This study aims to investigate the influencing factors of pregnancy outcomes during in vitro fertilization and embryo transfer (IVF-ET) procedures in clinical practice. Several prediction models were constructed to predict pregnancy outcom...