Keywords: Diagnosis/Prediction, Radiomics, Placenta
Motivation: Placenta accrete spectrum disorder (PAS) is a dangerous pregnancy complication that posed a threat to the safety of pregnant women, and its incidence is still on the rise.
Goal(s): To develop a machine learning model for effective diagnosis of PAS.
Approach: We developed machine learning models based on T2WI radiomics, clinical features, and clinical-radiomics features in the diagnosis of PAS.
Results: Radiomics models have a great diagnostic performance for PAS, with sagittal-based model shows better performance. The clinical-radiomics model exhibits the highest diagnostic performance in this study.
Impact: Machine learning models that combined with radiomics and clinical features can improve the diagnosis of PAS, and benefit PAS patients. Furthermore, our results provide new insights for future research.
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