Tehran University of Medical Sciences

Science Communicator Platform

Stay connected! Follow us on X network (Twitter):
Share By
Machine Learning Applications in Placenta Accreta Spectrum Disorders Publisher



Danaei M1 ; Yeganegi M2 ; Azizi S3 ; Jayervand F1 ; Shams SE4 ; Sharifi MH5 ; Bahrami R6 ; Masoudi A7 ; Shahbazi A8 ; Shiri A9 ; Rashnavadi H10 ; Aghili K11 ; Neamatzadeh H12
Authors

Source: European Journal of Obstetrics and Gynecology and Reproductive Biology: X Published:2025


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. © 2024 The Authors
Other Related Docs
8. Non-Invasive Pnet Grading Using Ct Radiomics and Machine Learning, Computer Methods in Biomechanics and Biomedical Engineering: Imaging and Visualization (2025)