Machine learning models for predicting preeclampsia: a systematic review.

Background: This systematic review provides an overview of machine learning (ML) approaches for predicting preeclampsia.

Method: This review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyzes (PRISMA) guidelines. We searched the Cochrane Central Register, PubMed, EMBASE, ProQuest, Scopus, and Google Scholar up to February 2023.

Machine learning-based approach for predicting low birth weight.

Background: Low birth weight (LBW) has been linked to infant mortality. Predicting LBW is a valuable preventative tool and predictor of newborn health risks. The current study employed a machine learning model to predict LBW.

Maternal and Neonatal Outcomes of Sub-clinical Hypothyroidism Treated With Levothyroxine in Pregnancy: A Retrospective Cohort Study.

Introduction: The effect of sub-clinical hypothyroidism (SCH) in pregnancy has been controversial. Furthermore, the impact of levothyroxine replacement on improving outcomes in pregnant women with SCH is unknown. This study aimed to assess the maternal and neonatal outcomes of pregnant women with SCH who were treated with levothyroxine replacement.

Manifestation of Spontaneous Rupture of the Urinary Bladder in Pregnancy: A Systematic Review of the Literature.

Spontaneous rupture of the urinary bladder (SRUB) during pregnancy is a potentially fatal event that necessitates immediate surgery. The aim of this systematic review is to determine the symptoms, causes, associated factors, and prognosis of SRUB in pregnancy. We searched the literature from inception until December 2022 using the Cochrane Central Register, PubMed, EMBASE, ProQuest, Scopus, and Google Scholar.

Machine learning models for predicting pre-eclampsia: a systematic review protocol.

Introduction: Pre-eclampsia is one of the most serious clinical problems of pregnancy that contribute significantly to maternal mortality worldwide. This systematic review aims to identify and summarise the predictive factors of pre-eclampsia using machine learning models and evaluate the diagnostic accuracy of machine learning models in predicting pre-eclampsia.

Methods And Analysis: This review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines.

Machine Learning-Based Approach to Predict Intrauterine Growth Restriction.

Introduction: Creating a prediction model incorporating multiple risk factors for intrauterine growth restriction is vital. The current study employed a machine learning model to predict intrauterine growth restriction.

Methods: This cross-sectional study was carried out in a tertiary hospital in Bandar Abbas, Iran, from January 2020 to January 2022.

Prediction of postpartum hemorrhage using traditional statistical analysis and a machine learning approach.

Background: Early detection of postpartum hemorrhage risk factors by healthcare providers during pregnancy and the postpartum period may allow healthcare providers to act to prevent it. Developing a prediction model that incorporates several risk factors and accurately calculates the overall risk for postpartum hemorrhage is necessary.

Objective: This study used a traditional analytical approach and a machine learning model to predict postpartum hemorrhage.

Application of machine learning to identify risk factors of birth asphyxia.

Background: Developing a prediction model that incorporates several risk factors and accurately calculates the overall risk of birth asphyxia is necessary. The present study used a machine learning model to predict birth asphyxia.

Methods: Women who gave birth at a tertiary Hospital in Bandar Abbas, Iran, were retrospectively evaluated from January 2020 to January 2022.

The Incidence and Outcomes of Late-Term Pregnancy.

Background: Little is known about the outcomes of late-term pregnancy. In this study, we aim to assess the incidence and adverse prenatal outcomes associated with late-term pregnancy.

Methods: We retrospectively assessed all singleton pregnant mothers who gave birth at Khalij-e-Fars Hospital in Bandar Abbas, Iran, between January 2020 and 2022.

Machine learning approach to predict postpartum haemorrhage: a systematic review protocol.

Introduction: Postpartum haemorrhage (PPH) is the most serious clinical problem of childbirth that contributes significantly to maternal mortality worldwide. This systematic review aims to identify predictors of PPH based on a machine learning (ML) approach.

Methods And Analysis: This review adhered to the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Protocol.

Vaginal Laser Therapy for Stress Urinary Incontinence: A Systematic Review of Prospective Randomized Clinical Trials.

The most common type of urinary incontinence in women is stress urinary incontinence (SUI) which negatively impacts several aspects of life. The newly introduced vaginal laser therapy is being considered for treating SUI. This systematic review aimed to evaluate the efficacy of vaginal laser therapy for stress urinary incontinence in menopausal women.

Spontaneous bladder rupture and associated factors during pregnancy:a systematic review and metanalysis protocol.

Introduction: Spontaneous bladder rupture during pregnancy is a potentially life-threatening event requiring immediate surgery to reduce morbidity and mortality. This systematic review aims to identify associated factors of spontaneous bladder rupture during pregnancy and propose a diagnostic and therapeutic algorithm.

Methods And Analysis: To improve the reporting of this protocol, the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) 2020 statement was used.

Comparison the efficacy of vaginal progesterone 17-alpha-hydroxyprogesterone caproate to prevent preterm birth in high-risk pregnant women undergo cerclage: a randomized clinical trial.

Objective: The aim of this study was to compare the effect of vaginal progesterone with 17-alpha-hydroxyprogesterone caproate (17OHP-C) in prevention of preterm birth in high-risk pregnant women undergo cerclage.

Materials And Methods: This prospective randomized clinical trial registered in the Iranian Registry of Clinical Trials (IRCT20181107041585N4), was performed from May 2017 to August 2018 in Bandar Abbas, Iran. Fifty-eight eligible women who were scheduled for cervical cerclage due to a history of two or more previous preterm birth <28 weeks or a cervical length less than 25 mm with at least one previous preterm birth <34 weeks were randomly divided into two groups.