Accuracy of cephalometric landmark and cephalometric analysis from lateral facial photograph by using CNN-based algorithm.

Cephalometric analysis is the primary diagnosis method in orthodontics. In our previous study, the algorithm was developed to estimate cephalometric landmarks from lateral facial photographs of patients with normal occlusion. This study evaluates the estimation accuracy by the algorithm trained on a dataset of 2320 patients with added malocclusion patients and the analysis values.

Cephalometric landmark detection without X-rays combining coordinate regression and heatmap regression.

Fully automated techniques using convolutional neural networks for cephalometric landmark detection have recently advanced. However, all existing studies have adopted X-rays. The problem of direct exposure of patients to X-ray radiation remains unsolved.