Automated landmarking for palatal shape analysis using geometric deep learning.

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. CONCLUSIONS: The automatic landmarking method shows excellent repeatability and promising accuracy, which can streamline patient assessment and research studies. However, landmark indications should be subject to visual quality control.

Exploring palatal and dental shape variation with 3D shape analysis and geometric deep learning.

OBJECTIVES: Palatal shape contains a lot of information that is of clinical interest. Moreover, palatal shape analysis can be used to guide or evaluate orthodontic treatments. A statistical shape model (SSM) is a tool that, by means of dimensionality reduction, aims at compactly modeling the variance of complex shapes for efficient analysis. In this report, we evaluate several competing approaches to constructing SSMs for the human palate. SETTING AND SAMPLE POPULATION: This study used a sample comprising digitized 3D maxillary dental casts from 1,324 individuals. MATERIALS AND METHODS: Principal component analysis (PCA) and autoencoders (AE) are popular approaches to construct SSMs. PCA is a dimension reduction technique that provides a compact description of shapes by uncorrelated variables. AEs are situated in the field of deep learning and provide a non-linear framework for dimension reduction. This work introduces the singular autoencoder (SAE), a hybrid approach that combines the most important properties of PCA and AEs. We assess the performance of the SAE using standard evaluation tools for SSMs, including accuracy, generalization, and specificity. RESULTS: We found that the SAE obtains equivalent results to PCA and AEs for all evaluation metrics. SAE scores were found to be uncorrelated and provided an optimally compact representation of the shapes. CONCLUSION: We conclude that the SAE is a promising tool for 3D palatal shape analysis, which effectively combines the power of PCA with the flexibility of deep learning. This opens future AI driven applications of shape analysis in orthodontics and other related clinical disciplines.

Principal component and linear discriminant analyses for the classification of hominoid primate specimens based on bone shape data.

In this study, we tested the hypothesis that machine learning methods can accurately classify extant primates based on triquetrum shape data. We then used this classification tool to observe the affinities between extant primates and fossil hominoids. We assessed the discrimination accuracy for an unsupervised and supervised learning pipeline, i.e. with principal component analysis (PCA) and linear discriminant analysis (LDA) feature extraction, when tasked with the classification of extant primates. The trained algorithm is used to classify a sample of known fossil hominoids. For the visualization, PCA and uniform manifold approximation and projection (UMAP) are used. The results show that the discriminant function correctly classified the extant specimens with an F1-score of 0.90 for both PCA and LDA. In addition, the classification of fossil hominoids reflects taxonomy and locomotor behaviour reported in literature. This classification based on shape data using PCA and LDA is a powerful tool that can discriminate between the triquetrum shape of extant primates with high accuracy and quantitatively compare fossil and extant morphology. It can be used to support taxonomic differentiation and aid the further interpretation of fossil remains. Further testing is necessary by including other bones and more species and specimens per species extinct primates.