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Cited by 10Maternal and fetal healthcareRadiomics and Machine Learning in Medical ImagingArtificial Intelligence in Healthcare and Education

Machine learning applications in placenta accreta spectrum disorders

Open access
Danaei, Mahsa, Yeganegi, Maryam, Azizi, Sepideh, Jayervand, Fatemeh, Shams, Seyedeh Elham, Sharifi, Mohammad Hossein, Bahrami, Reza, Masoudi, Ali +5 more·2025·European Journal of Obstetrics & Gynecology and Reproductive Biology: X
DOI 10.1016/j.eurox.2024.100362PMC PMC11751428
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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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  • Danaei et al.'s model maintained robust performance across varied patient demographics and imaging protocols for PAS diagnosis.

    “By leveraging a curated dataset from multiple tertiary care centers, the model maintained robust performance across varied patient demographics and imaging protocols.”

    From Machine Learning in Maternity: Early Diagnosis of Placenta Accreta Spectrum (2026) · cites this paper as [36]

    The Radiomics-based prediction models section reports a multicenter study with good predictive accuracy, but it does not say that Danaei et al. developed that model or that its performance held across different patient demographics and imaging protocols. The Challenges and limitations section instead identifies generalizability across populations and variability in imaging protocols as unresolved concerns. No page numbers are available in our copy. (LLM, conf=0.88)

    Experimental — accuracy not yet measured

  • Danaei et al. applied CNNs to ultrasound data, supporting the real-world feasibility of automated PAS diagnosis in high-volume obstetric settings.

    “Danaei et al. emphasized the practical applications of ML in PAS by applying convolutional neural networks (CNNs) to ultrasound data, further supporting the real-world feasibility of automated diagnosis in high-volume obstetric settings.”

    From Machine Learning in Maternity: Early Diagnosis of Placenta Accreta Spectrum (2026) · cites this paper as [36]

    The Introduction identifies this as a review, and the ‘Texture analysis of ultrasound and MRI in ML’ section describes Young et al.’s ultrasound-based ML models for PAS detection. It does not say Danaei applied CNNs to ultrasound data or establish feasibility in high-volume obstetric settings; the Discussion instead notes remaining challenges with validation, generalizability, and clinical acceptance. Page numbers are unavailable in our copy. (LLM, conf=0.87)

    Experimental — accuracy not yet measured

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Authors
Danaei, M., Yeganegi, M., Azizi, S., Jayervand, F., Shams, S. E., Sharifi, M. H., Bahrami, R., Masoudi, A., Shahbazi, A., Shiri, A., Rashnavadi, H., Aghili, K., & Neamatzadeh, H.
Affiliations
  • Iran University of Medical Sciences (IR)
  • Iranshahr University (IR)
  • Hamedan University of Medical Sciences (IR)
  • Shiraz University of Medical Sciences (IR)
  • Shahid Sadoughi University of Medical Sciences and Health Services (IR)
  • Medical University of Ilam (IR)
  • Tehran University of Medical Sciences (IR)
Corresponding author
Yeganegi, Maryam
ORCID
Venue
European Journal of Obstetrics & Gynecology and Reproductive Biology: X
Publisher
Elsevier BV
Published
2025-03
Volume
25
Pages
100362
ISSN
2590-1613 (ISSN-L)
DOI
10.1016/j.eurox.2024.100362
PMC
PMC11751428
OpenAlex
W4405746678
Cited by
8
References
38
Citations by year
2026: 3 · 2025: 5
FWCI
0.94
Type
journal article
Language
en
Open access
Licence
Locations
  • European Journal of Obstetrics & Gynecology and Reproductive Biology X (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Topics
  • Health Sciences › Medicine › Pediatrics, Perinatology and Child Health › Maternal and fetal healthcare
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
Keywords
placenta accreta spectrum, radiomics, machine learning, deep learning, magnetic resonance imaging, ultrasound, risk stratification, high-risk pregnancy, surgical planning, biomarkers

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