Geometric Deep Learning for Automated Landmarking of Maxillary Arches on 3D Oral Scans from Newborns with Cleft Lip and Palate
Journal:
arXiv
Published Date:
Jan 27, 2025
Abstract
Rapid advances in 3D model scanning have enabled the mass digitization of
dental clay models. However, most clinicians and researchers continue to use
manual morphometric analysis methods on these models such as landmarking. This
is a significant step in treatment planning for craniomaxillofacial conditions.
We aimed to develop and test a geometric deep learning model that would
accurately and reliably label landmarks on a complicated and specialized
patient population -- infants, as accurately as a human specialist without a
large amount of training data. Our developed pipeline demonstrated an accuracy
of 94.44% with an absolute mean error of 1.676 +/- 0.959 mm on a set of 100
models acquired from newborn babies with cleft lip and palate. Our proposed
pipeline has the potential to serve as a fast, accurate, and reliable
quantifier of maxillary arch morphometric features, as well as an integral step
towards a future fully automated dental treatment pipeline.
