Prenatal Diagnosis of Placenta Accreta Spectrum Disorders: Deep Learning Radiomics of Pelvic MRI

• Published in: Journal of magnetic resonance imaging Vol. 59; no. 2; pp. 496 - 509
• Main Authors: Peng, Lulu, Yang, Zehong, Liu, Jue, Liu, Yi, Huang, Jianwei, Chen, Junwei, Su, Yun, Zhang, Xiang, Song, Ting
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.02.2024; Wiley Subscription Services, Inc
• Subjects: Adult; Auditory discrimination; Chi-square test; Correlation coefficient; Correlation coefficients; Datasets; Decision analysis; Deep Learning; Diagnosis; Female; Field strength; Humans; Magnetic Resonance Imaging; Placenta; placenta accreta; Placenta Accreta - diagnostic imaging; Placenta Diseases; Population studies; Pregnancy; Prenatal Diagnosis; Radiomics; Reclassification; Regression analysis; Retrospective Studies; Statistical analysis; Statistical tests; Training; placenta accreta; deep learning; radiomics

Background Diagnostic performance of placenta accreta spectrum (PAS) by prenatal MRI is unsatisfactory. Deep learning radiomics (DLR) has the potential to quantify the MRI features of PAS. Purpose To explore whether DLR from MRI can be used to identify pregnancies with PAS. Study Type Retrospective....