Pulp calcification identification on cone beam computed tomography: an artificial intelligence pilot study.

ABSTRACT:

Background

This study aims to verify the effectiveness of a deep neural network (DNN) in automatically identifying pulp calcification on cone beam computed tomography (CBCT) images.

Methods

This study retrospectively analysed 150 CBCT images. Pulp calcification was identified and manually annotated by three endodontists with 10 years of experience. A DNN model based on the U-Net architecture was constructed to identify pulp calcification, and ten rounds of fourfold cross-validation were conducted. The model performance was evaluated using sensitivity, specificity, accuracy, and the area under the receiver operating characteristic curve (AUC).

Results

The model achieved a sensitivity of 75.91 ± 2.84% in automatically identifying pulp calcification, with a specificity o

SUBMITTER: Ye L 

PROVIDER: S-EPMC11438316 | biostudies-literature | 2024 Sep

REPOSITORIES: biostudies-literature