Deep convolutional neural network-based skeletal classification of cephalometric image compared with automated-tracing software.

ABSTRACT: This study aimed to investigate deep convolutional neural network- (DCNN-) based artificial intelligence (AI) model using cephalometric images for the classification of sagittal skeletal relationships and compare the performance of the newly developed DCNN-based AI model with that of the automated-tracing AI software. A total of 1574 cephalometric images were included and classified based on the A-point-Nasion- (N-) point-B-point (ANB) angle (Class I being 0-4°, Class II > 4°, and Class III < 0°). The DCNN-based AI model was developed using training (1334 images) and validation (120 images) sets with a standard classification label for the individual images. A test set of 120 images was used to compare the AI models. The agreement of the DCNN-based AI model or the automated-tracing AI soft

SUBMITTER: Kim HJ 

PROVIDER: S-EPMC9270345 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature