  Three-dimensional facial stereophotogrammetry provides a detailed
representation of craniofacial soft tissue without the use of ionizing
radiation. While manual annotation of landmarks serves as the current gold
standard for cephalometric analysis, it is a time-consuming process and is
prone to human error. The aim in this study was to develop and evaluate an
automated cephalometric annotation method using a deep learning-based approach.
Ten landmarks were manually annotated on 2897 3D facial photographs by a single
observer. The automated landmarking workflow involved two successive
DiffusionNet models and additional algorithms for facial segmentation. The
dataset was randomly divided into a training and test dataset. The training
dataset was used to train the deep learning networks, whereas the test dataset
was used to evaluate the performance of the automated workflow. The precision
of the workflow was evaluated by calculating the Euclidean distances between
the automated and manual landmarks and compared to the intra-observer and
inter-observer variability of manual annotation and the semi-automated
landmarking method. The workflow was successful in 98.6% of all test cases. The
deep learning-based landmarking method achieved precise and consistent landmark
annotation. The mean precision of 1.69 (+/-1.15) mm was comparable to the
inter-observer variability (1.31 +/-0.91 mm) of manual annotation. The
Euclidean distance between the automated and manual landmarks was within 2 mm
in 69%. Automated landmark annotation on 3D photographs was achieved with the
DiffusionNet-based approach. The proposed method allows quantitative analysis
of large datasets and may be used in diagnosis, follow-up, and virtual surgical
planning.
