Background: Three-dimensional (3D) cephalometric analysis using computerized
tomography data has been rapidly adopted for dysmorphosis and anthropometry.
Several different approaches to automatic 3D annotation have been proposed to
overcome the limitations of traditional cephalometry. The purpose of this study
was to evaluate the accuracy of our newly-developed system using a deep
learning algorithm for automatic 3D cephalometric annotation. Methods: To
overcome current technical limitations, some measures were developed to
directly annotate 3D human skull data. Our deep learning-based model system
mainly consisted of a 3D convolutional neural network and image data
resampling. Results: The discrepancies between the referenced and predicted
coordinate values in three axes and in 3D distance were calculated to evaluate
system accuracy. Our new model system yielded prediction errors of 3.26, 3.18,
and 4.81 mm (for three axes) and 7.61 mm (for 3D). Moreover, there was no
difference among the landmarks of the three groups, including the midsagittal
plane, horizontal plane, and mandible (p>0.05). Conclusion: A new 3D
convolutional neural network-based automatic annotation system for 3D
cephalometry was developed. The strategies used to implement the system were
detailed and measurement results were evaluated for accuracy. Further
development of this system is planned for full clinical application of
automatic 3D cephalometric annotation