ReviewHealthcare (Basel, Switzerland)2022
Application of Artificial Intelligence in Screening for Adverse Perinatal Outcomes-A Systematic Review.
Review in Healthcare (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Biomarkers for Pregnancy Latency Prediction after Preterm Premature Rupture of Membranes-A Systematic Review.International journal of molecular sciences · 2023Pooled it
- Immunology of pregnancy and sepsis: shared and specific pathways guiding future precision care.EBioMedicine · 2026Review
- The economic imperative of artificial intelligence in maternal and neonatal health: a review of evaluation benefits, frameworks, challenges, future perspectives, and limitations.Cost effectiveness and resource allocation : C/E · 2026Review
- Development of machine learning models for early prediction of small for-gestational-age births using maternal sociodemographic and obstetric data.BMC pregnancy and childbirth · 2026Article
- Transforming perinatal health with AI-predictive models in precision and digital innovations for maternal and fetal well-being: a systematic review.Oxford open digital health · 2026Article
- Does the Vaginal Microbiota Influence the Incidence of the Preterm Premature Rupture of Membranes?Journal of clinical medicine · 2025Review
- Developing and validating an artificial intelligence-based application for predicting some pregnancy outcomes: a multi-phase study protocol.Reproductive health · 2025Article
- Artificial Intelligence's Role in Improving Adverse Pregnancy Outcomes: A Scoping Review and Consideration of Ethical Issues.Journal of clinical medicine · 2025Review
- Use of artificial intelligence to study the hospitalization of women undergoing caesarean section.BMC public health · 2025Article
- Immune changes in pregnancy: associations with pre-existing conditions and obstetrical complications at the 20th gestational week-a prospective cohort study.BMC medicine · 2024Article
- Application of artificial neural networks to evaluate femur development in the human fetus.PloS one · 2024Article
- A Theoretical Exploration of Artificial Intelligence's Impact on Feto-Maternal Health from Conception to Delivery.International journal of women's health · 2024Review
- Placental Cannabinoid Receptor Expression in Preterm Birth.Journal of pregnancy · 2024Observational
- Machine learning applied in maternal and fetal health: a narrative review focused on pregnancy diseases and complications.Frontiers in endocrinology · 2023Review
- Evidence integration: The transformative role of artificial intelligence in maternal health.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
(1) Background: AI-based solutions could become crucial for the prediction of pregnancy disorders and complications. This study investigated the evidence for applying artificial intelligence methods in obstetric pregnancy risk assessment and adverse pregnancy outcome prediction. (2) Methods: Authors screened the following databases: Pubmed/MEDLINE, Web of Science, Cochrane Library, EMBASE, and Google Scholar. This study included all the evaluative studies comparing artificial intelligence methods in predicting adverse pregnancy outcomes. The PROSPERO ID number is CRD42020178944, and the study protocol was published before this publication. (3) Results: AI application was found in nine groups: general pregnancy risk assessment, prenatal diagnosis, pregnancy hypertension disorders, fetal growth, stillbirth, gestational diabetes, preterm deliveries, delivery route, and others. According to this systematic review, the best artificial intelligence application for assessing medical conditions is ANN methods. The average accuracy of ANN methods was established to be around 80-90%. (4) Conclusions: The application of AI methods as a digital software can help medical practitioners in their everyday practice during pregnancy risk assessment. Based on published studies, models that used ANN methods could be applied in APO prediction. Nevertheless, further studies could identify new methods with an even better prediction potential.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.