![]() In 1956, computer scientist John McCarthy defined AI as the science and engineering of making highly intelligent computing machines or computer programs. Recently, artificial intelligence- (AI-) based diagnosis has been performed in the treatment planning, increasingly drawing the attention of orthodontists. Based on the ANB angle, the patients can be categorized as having skeletal Class I, II, and III relationships, which may affect the decision-making in treatment planning. Among clinical parameters for diagnosis, the A-, Nasion- (N-), and B-points (ANB) angle is generally measured on lateral cephalometric images to evaluate the sagittal skeletal relationship that is closely related to occlusal relationship and facial appearance. In the field of orthodontics, accurate diagnosis is of clinical importance because it is closely associated with treatment planning and subsequent outcomes. With regard to the sagittal skeletal classification using cephalometric images, the DCNN-based AI model outperformed the automated-tracing AI software. In terms of their performances, the micro-average values of the DCNN-based AI model (sensitivity, 0.94 specificity, 0.97 precision, 0.94 accuracy, 0.96) were higher than those of the automated-tracing AI software (sensitivity, 0.85 specificity, 0.93 precision, 0.85 accuracy, 0.90). The agreement of the DCNN-based AI model or the automated-tracing AI software with a standard classification label was measured using Cohen’s kappa coefficient (0.913 for the DCNN-based AI model 0.775 for the automated-tracing AI software). ![]() A test set of 120 images was used to compare the AI models. 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 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°). 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. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. Archives
March 2023
Categories |