Automated landmarking for palatal shape analysis using geometric deep learning

Abstract

Objectives: To develop and evaluate a geometric deep-learning network to automatically place seven palatal landmarks on digitized maxillary dental casts. Settings and Sample Population:The sample comprised individuals with permanent dentition of various ethnicities. The network was trained from manual landmark annotations on 732 dental casts and evaluated on 104 dental casts. Materials and Methods:A geometric deep-learning network was developed to hierarchically learn features from point-clouds representing the 3D surface of each cast.These features predict the locations of seven palatal landmarks.Results: Repeat-measurement reliability was <0.3 mm for all landmarks on all casts.Accuracy is promising. The proportion of test subjects with errors less than 2 mm was between 0.93 and 0.68, depending on the landmark. Unusually shaped and large palates generate the highest errors. There was no evidence for a difference in mean palatal shape estimated from manual compared to the automatic landmarking. The automatic landmarking reduces sample variation around the mean and reduces measurements of palatal size.

Trends and Application of Artificial Intelligence Technology in Orthodontic Diagnosis and Treatment Planning—A Review

“…AI models developed for application in orthodontics have mainly focused on: the automated identification of cephalometric landmarks [15][16][17][18][19][20][21][22][23][24][25][26][27][28][29][30][31]; the estimation of bone age and maturity using cervical vertebra and hand-wrist radiographs [32][33][34][35][36][37][38][39][40][41][42][43]; palatal shape analysis [44,45]; determining the need for orthodontic tooth extractions [46][47][48][49][50][51][52]; automated skeletal classification [53,54]; and the diagnosis and planning of orthognathic surgeries [55][56][57][58][59][60][61]…”

“…The recent technological advancements have resulted in the development of AI based models designed for palatal shape analysis. Croquet B et al [44] reported on a deep learning model designed for analyzing the palatal shape. This automatic model demonstrated excellent repeatability with promising accuracy.…”

Use of Advanced Artificial Intelligence in Forensic Medicine, Forensic Anthropology and Clinical Anatomy

“…Automatized 3D cephalometric landmark annotation [41][42][43][44][45][46][47][48][49][50][51][52][53][54][55][56][57][58]…”

Artificial intelligence and machine learning in orthodontics

“…Although essential for the success of AI‐based diagnosis, the objective extraction of these morphological features is not easy. Nauwelaers et al developed a novel AI‐based method for the analysis of palatal 3D shape 26 and Croquet et al developed methods of automatic landmarking for analysis of the palatal shape 27 …”