ReviewEuropean journal of obstetrics & gynecology and reproductive biology: X2025
Machine learning applications in placenta accreta spectrum disorders.
Review in European journal of obstetrics & gynecology and reproductive biology: X, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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
10 citing papers in PubMed.
- The quintessential high-risk profile: advanced management strategies for placenta accreta spectrum in patients with advanced maternal age and IVF conception.BMC pregnancy and childbirth · 2026Review
- Artificial intelligence and predictive analytics in obstetric anesthesia: early warning for maternal complications.Current opinion in anaesthesiology · 2026Review
- Hematologic markers and machine learning in predicting placenta accreta: A case-control study.International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics · 2026Article
- Review
- The Efficacy of a Super-Resolution Reconstruction Radiomics Model Based on T2WI for Predicting Placenta Accreta Spectrum Disorders: A Multicenter Study.Current medical imaging · 2026Article
- Comparison of clinical characteristics and perinatal outcomes between twin and singleton pregnancies with placenta accreta spectrum: a retrospective cohort study from a tertiary hospital in Eastern China.BMC pregnancy and childbirth · 2025Article
- Surgical Management of Placenta Accreta Spectrum: A Five-Year Institutional Experience.Cureus · 2025Article
- Review
- Advancements in machine learning and biomarker integration for prenatal Down syndrome screening.Turkish journal of obstetrics and gynecology · 2025Article
- Research advancements in the Use of artificial intelligence for prenatal diagnosis of neural tube defects.Frontiers in pediatrics · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
13 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This review examines the emerging applications of machine learning (ML) and radiomics in the diagnosis and prediction of placenta accreta spectrum (PAS) disorders, addressing a significant challenge in obstetric care. It highlights recent advancements in ML algorithms and radiomic techniques that utilize medical imaging modalities like magnetic resonance imaging (MRI) and ultrasound for effective classification and risk stratification of PAS. The review discusses the efficacy of various deep learning models, such as nnU-Net and DenseNet-PAS, which have demonstrated superior performance over traditional diagnostic methods through high AUC scores. Furthermore, it underscores the importance of integrating quantitative imaging features with clinical data to enhance diagnostic accuracy and optimize surgical planning. The potential of ML to predict surgical morbidity by analyzing demographic and obstetric factors is also explored. Emphasizing the need for standardized methodologies to ensure consistent feature extraction and model performance, this review advocates for the integration of radiomics and ML into clinical workflows, aiming to improve patient outcomes and foster a multidisciplinary approach in high-risk pregnancies. Future research should focus on larger datasets and validation of biomarkers to refine predictive models in obstetric care.
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Identifiers
What OpenQuestion 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 OpenQuestion graph.