2025/01/27 by Artur Agaronyan, Agaronyan, Artur, HyeRan Choo +5
Arts and Humanities · Biochemistry, Genetics and Molecular Biology · #Cleft Lip and Palate Research #FOS: Computer and information sciences #FOS: Electrical engineering #Forensic Anthropology and Bioarchaeology Studies #Image and Video Processing (eess.IV) #Machine Learning (cs.LG) #dental development and anomalies #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2501.15737
openalex publication_date 2025/01/27 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.