Deep Learning-Assisted Diagnosis of Placenta Accreta Spectrum Using the DenseNet-121 Model: A Multicenter, Retrospective Study.

ABSTRACT: To explore the diagnostic value of deep learning (DL) imaging based on MRI in predicting placenta accreta spectrum (PAS) in high-risk pregnant women. A total of 263 patients with suspected placenta accreta from Institution I and Institution II were retrospectively analyzed and divided into training (n = 170) and external verification sets (n = 93). Through imaging acquisition, feature extraction, and radiomic data processing, 15 radiomic features were used to train support vector machine (SVM), K-nearest neighbor (KNN), random forest (RF), light gradient boosting machine (LGBM), and DL models to predict PAS. The diagnostic performances of the models were evaluated in the training set using the area under the curve (AUC) and accuracy and further validated in the external verification set. U

SUBMITTER: Hu Y 

PROVIDER: S-EPMC12701209 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature