Automatic analysis of lateral cephalograms based on high-resolution net

•The accuracy of the landmark detection model was improved by maintaining high resolution in the training process. •Influences of different input resolutions on accuracy were explored to guide the research design. •The established model is competitive in the same kind of research on accuracy. •The model performance is analyzed from the magnitude and distribution of errors. Introduction Cephalometric analysis is essential in orthodontic treatment, and it is progressing toward automatic cephalometric analysis. This study aimed to establish a cephalometric landmark detection model on the basis of a high-resolution net and improve the accuracy with high resolution. Methods A total of 2000 lateral cephalograms were collected to construct a dataset, and the number of target landmarks was 51. A high-resolution network model was applied to the landmark detection task. Four models were trained by adjusting different input resolutions to choose the most suitable resolution. A test set consisting of 300 lateral cephalograms was used for evaluation. The model was evaluated from the error size and distribution of each landmark. Results After 200 epochs of training, a landmark detection model was established. Under different resolutions of the input image, the mean model radial error decreased initially and then increased. At 680 × 920 pixels resolution, the minimum error and the highest detection success rate were obtained. The mean radial error was 1.08 ± 0.87 mm. The detection success rates of 2.0 mm, 2.5 mm, 3.0 mm, and 4.0 mm were 89.00%, 94.00%, 96.33%, and 98.67%, respectively. The mean radial errors of 22 landmarks were <1 mm, and the errors of other landmarks were <2 mm except for the pterion. The error distribution of landmarks followed a certain pattern. Conclusions An automatic landmark detection model based on a high-resolution net was established to recognize 51 landmarks. The model showed high detection accuracy, which provides a basis for further measurement application.