AIMC Topic: Cesarean Section
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Prediction of caesarean section birth using machine learning algorithms among pregnant women in a district hospital in Ghana.
BMC pregnancy and childbirth
Jul 2, 2025
BACKGROUND: Machine learning algorithms may contribute to improving maternal and child health, including determining the suitability of caesarean section (CS) births in low-resource countries. Despite machine learning algorithms offering a more robus...
Analysis of maternal fetal outcomes and complete blood count parameters according to the stages of placental abruption: a retrospective study.
European journal of medical research
Jun 18, 2025
BACKGROUND: To compare the demographic characteristics, maternal and perinatal outcomes and hemoglobin parameters according to stages diagnosed with placental abruption.
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
May 10, 2025
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...
Development and validation of machine learning models for predicting post-cesarean pain and individualized pain management strategies: a multicenter study.
BMC anesthesiology
Apr 10, 2025
BACKGROUND: Effective management of postoperative pain remains a significant challenge in obstetric care due to the variability in pain perception and response influenced by physical, medical, and psychosocial factors. Current standardized pain manag...
Predicting high-risk factors for postoperative inadequate analgesia and adverse reactions in cesarean delivery surgery: a prospective study.
International journal of surgery (London, England)
Apr 3, 2025
BACKGROUND: Early identification of high-risk factors for inadequate analgesia and adverse reactions in obstetric patients is critical for improving outcomes. This study developed a machine learning model to predict these factors and optimize anesthe...
Use of artificial intelligence to study the hospitalization of women undergoing caesarean section.
BMC public health
Jan 20, 2025
OBJECTIVE: The incidence of caesarean sections (CSs) has increased significantly in recent years, especially in developed countries. This study aimed to identify the factors that most influence the length of hospital stay (LOS) after a CS, using data...
Predicting vaginal delivery after labor induction using machine learning: Development of a multivariable prediction model.
Acta obstetricia et gynecologica Scandinavica
Nov 27, 2024
INTRODUCTION: Induction of labor, often used for pregnancy termination, has globally rising rates, especially in high-income countries where pregnant women present with more comorbidities. Consequently, concerns on a potential rise in cesarean sectio...
Machine Learning for the Prediction of Surgical Morbidity in Placenta Accreta Spectrum.
American journal of perinatology
Sep 17, 2024
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).
Development of a machine learning approach for prediction of red blood cell transfusion in patients undergoing Cesarean section at a single institution.
Scientific reports
Jul 18, 2024
Despite recent advances in surgical techniques and perinatal management in obstetrics for reducing intraoperative bleeding, blood transfusion may occur during a cesarean section (CS). This study aims to identify machine learning models with an optima...
Quantitative prediction of postpartum hemorrhage in cesarean section on machine learning.
BMC medical informatics and decision making
Jun 13, 2024
BACKGROUND: Cesarean section-induced postpartum hemorrhage (PPH) potentially causes anemia and hypovolemic shock in pregnant women. Hence, it is helpful for obstetricians and anesthesiologists to prepare pre-emptive prevention when predicting PPH occ...
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