Preterm labor and preterm birth

Şen, Cihat

doi:10.1515/jpm-2017-0298

Cited by 19 publications

(20 citation statements)

References 19 publications

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“…A short cervix [4, 68–71], a prior PTD [72–74] and maternal age [75, 76] are well-established risk factors for preterm delivery. Preterm birth is a leading cause of perinatal mortality and long-term disabilities [77, 78]. Vaginal progesterone reduces the risk of PTD in women who have a short cervix with and without previous history of PTD [14,79–82].…”

Section: Discussionmentioning

confidence: 99%

A soft cervix, categorized by shear-wave elastography, in women with short or with normal cervical length at 18–24 weeks is associated with a higher prevalence of spontaneous preterm delivery

Hernández‐Andrade

Maymon

Luewan

et al. 2018

Journal of Perinatal Medicine

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Section: Discussionmentioning

confidence: 99%

A soft cervix, categorized by shear-wave elastography, in women with short or with normal cervical length at 18–24 weeks is associated with a higher prevalence of spontaneous preterm delivery

Hernández‐Andrade

Maymon

Luewan

et al. 2018

Journal of Perinatal Medicine

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“…In most cases, it occurs unexpectedly in women at low risk; therefore, the identification of a biomarker able to predict spontaneous preterm delivery poses a significant challenge due to the diversity of clinical presentations. Preterm delivery is the consequence of four main mechanisms: activation of the maternal-fetal placental interaction with the hypothalamic-pituitary-adrenal axis, inflammation in the amniochorionic-decidual tissue, decidual hemorrhage, and pathological distention of the myometrium [85].…”

Section: Preterm Labor (Ptl)mentioning

confidence: 99%

Extracellular Vesicles in Feto–Maternal Crosstalk and Pregnancy Disorders

Buca

Bologna

D’Amico

et al. 2020

IJMS

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Extracellular vesicles (EVs) actively participate in inter-cellular crosstalk and have progressively emerged as key players of organized communities of cells within multicellular organisms in health and disease. For these reasons, EVs are attracting the attention of many investigators across different biomedical fields. In this scenario, the possibility to study specific placental-derived EVs in the maternal peripheral blood may open novel perspectives in the development of new early biomarkers for major obstetric pathological conditions. Here we reviewed the involvement of EVs in feto–maternal crosstalk mechanisms, both in physiological and pathological conditions (preeclampsia, fetal growth restriction, preterm labor, gestational diabetes mellitus), also underlining the usefulness of EV characterization in maternal–fetal medicine.

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“…Several diseases and adverse outcomes during pregnancy (e.g. preterm birth, preeclampsia and miscarriage) have complicated and difficult to understand etiology, leaving little to be done for prevention [ 5–7 ]. Deep phenotyping these patient states (i.e.…”

Section: Introductionmentioning

confidence: 99%

Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes

Davidson

Boland

2021

Briefings in Bioinformatics

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Objective Development of novel informatics methods focused on improving pregnancy outcomes remains an active area of research. The purpose of this study is to systematically review the ways that artificial intelligence (AI) and machine learning (ML), including deep learning (DL), methodologies can inform patient care during pregnancy and improve outcomes. Materials and methods We searched English articles on EMBASE, PubMed and SCOPUS. Search terms included ML, AI, pregnancy and informatics. We included research articles and book chapters, excluding conference papers, editorials and notes. Results We identified 127 distinct studies from our queries that were relevant to our topic and included in the review. We found that supervised learning methods were more popular (n = 69) than unsupervised methods (n = 9). Popular methods included support vector machines (n = 30), artificial neural networks (n = 22), regression analysis (n = 17) and random forests (n = 16). Methods such as DL are beginning to gain traction (n = 13). Common areas within the pregnancy domain where AI and ML methods were used the most include prenatal care (e.g. fetal anomalies, placental functioning) (n = 73); perinatal care, birth and delivery (n = 20); and preterm birth (n = 13). Efforts to translate AI into clinical care include clinical decision support systems (n = 24) and mobile health applications (n = 9). Conclusions Overall, we found that ML and AI methods are being employed to optimize pregnancy outcomes, including modern DL methods (n = 13). Future research should focus on less-studied pregnancy domain areas, including postnatal and postpartum care (n = 2). Also, more work on clinical adoption of AI methods and the ethical implications of such adoption is needed.

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Preterm labor and preterm birth

Cited by 19 publications

References 19 publications

A soft cervix, categorized by shear-wave elastography, in women with short or with normal cervical length at 18–24 weeks is associated with a higher prevalence of spontaneous preterm delivery

A soft cervix, categorized by shear-wave elastography, in women with short or with normal cervical length at 18–24 weeks is associated with a higher prevalence of spontaneous preterm delivery

Extracellular Vesicles in Feto–Maternal Crosstalk and Pregnancy Disorders

Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes

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