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 Show Affiliations
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