An Automatic Grading System for Orthodontically Induced External Root Resorption Based on Deep Convolutional Neural Network.

ABSTRACT: Orthodontically induced external root resorption (OIERR) is a common complication of orthodontic treatments. Accurate OIERR grading is crucial for clinical intervention. This study aimed to evaluate six deep convolutional neural networks (CNNs) for performing OIERR grading on tooth slices to construct an automatic grading system for OIERR. A total of 2146 tooth slices of different OIERR grades were collected and preprocessed. Six pre-trained CNNs (EfficientNet-B1, EfficientNet-B2, EfficientNet-B3, EfficientNet-B4, EfficientNet-B5, and MobileNet-V3) were trained and validated on the pre-processed images based on four different cross-validation methods. The performances of the CNNs on a test set were evaluated and compared with those of orthodontists. The gradient-weighted class activation m

SUBMITTER: Xu S 

PROVIDER: S-EPMC11300848 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature