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.
Estimates of individual-level genomic ancestry are routinely used in human genetics, and related fields. The analysis of population structure and genomic ancestry can yield insights in terms of modern and ancient populations, allowing us to address questions regarding admixture, and the numbers and identities of the parental source populations. Unrecognized population structure is also an important confounder to correct for in genome-wide association studies. However, it remains challenging to work with heterogeneous datasets from multiple studies collected by different laboratories with diverse genotyping and imputation protocols. This work presents a new approach and an accompanying open-source toolbox that facilitates a robust integrative analysis for population structure and genomic ancestry estimates for heterogeneous datasets. We show robustness against individual outliers and different protocols for the projection of new samples into a reference ancestry space, and the ability to reveal and adjust for population structure in a simulated case-control admixed population. Given that visually evident and easily recognizable patterns of human facial characteristics co-vary with genomic ancestry, and based on the integration of three different sources of genome data, we generate average 3D faces to illustrate genomic ancestry variations within the 1,000 Genome project and for eight ancient-DNA profiles, respectively. Scientists today have access to large heterogeneous datasets from many studies collected by different laboratories with diverse genotyping and imputation protocols. Therefore, the joint analysis of these datasets requires a robust and consistent inference of ancestry across all datasets involved, where one common strategy is to yield an ancestry space generated by a reference set of individuals 1. Based on open-research initiatives such as the 1,000 Genome project (1KGP) 2 , HapMap project 3 , Human Genome Diversity project (HGDP) 4 , and the POPRES dataset 5 , the potential exists to create reference ancestry latent-spaces at different levels of interest, from worldwide inter-continental to fine-scale intra-continental ancestry. A reference ancestry space allows the researcher to collate multiple datasets facilitating analyses that are more advanced. For example, reference ancestry spaces
23Accurate inference of genomic ancestry is critically important in human genetics, epidemiology, and 24 related fields. Geneticists today have access to multiple heterogeneous population-based datasets 25 from studies collected under different protocols. Therefore, joint analyses of these datasets require 26 robust and consistent inference of ancestry, where a common strategy is to yield an ancestry space 27 generated by a reference dataset. However, such a strategy is sensitive to batch artefacts introduced 28 by different protocols. In this work, we propose a novel robust genome-wide ancestry inference 29 method; referred to as SUGIBS, based on an unnormalized genomic (UG) relationship matrix whose 30 spectral (S) decomposition is generalized by an Identity-by-State (IBS) similarity degree matrix. SUGIBS 31 robustly constructs an ancestry space from a single reference dataset, and provides a robust 32 projection of new samples, from different studies. In experiments and simulations, we show that, 33 SUGIBS is robust against individual outliers and batch artifacts introduced by different genotyping 34 protocols. The performance of SUGIBS is equivalent to the widely used principal component analysis 35 (PCA) on normalized genotype data in revealing the underlying structure of an admixed population 36 and in adjusting for false positive findings in a case-control admixed GWAS. We applied SUGIBS on the 37
Treatment of large acetabular defects and discontinuities remains challenging and relies on the accurate restoration of the native anatomy of the patient. This study introduces and validates a statistical shape model for the reconstruction of acetabular discontinuities with severe bone loss through a two-sided Markov Chain Monte Carlo reconstruction method. The performance of the reconstruction algorithm was evaluated using leave-one-out cross-validation in three defect types with varying severity as well as severe defects with discontinuities. The two-sided reconstruction method was compared to a one-sided methodology. Although, reconstruction errors increased with defect size and this increase was most pronounced for pelvic discontinuities, the two-sided reconstruction method was able to reconstruct the native anatomy with higher accuracy than the one-sided reconstruction method. These findings can improve the preoperative planning and custom implant design in patients with large pelvic defects, both with and without discontinuities.
Identification and delineation of craniofacial characteristics support the clinical and molecular diagnosis of genetic syndromes. Deep learning (DL) frameworks for syndrome identification from 2D facial images are trained on large clinical datasets using standard convolutional neural networks for classification. In contrast, despite the increased availability of 3D scanners in clinical setups, similar frameworks remain absent for 3D facial photographs. The main challenges involve working with smaller datasets and the need for DL operations applicable to 3D geometric data. Therefore, to date, most 3D methods refrain from working across multiple syndromic groups and/or are solely based on traditional machine learning. The first contribution of this work is the use of geometric deep learning with spiral convolutions in a triplet-loss architecture. This geometric encoding (GE) learns a lower dimensional metric space from 3D facial data that is used as input to linear discriminant analysis (LDA) performing multiclass classification. Benchmarking is done against principal component analysis (PCA), a common technique in 3D facial shape analysis, and related work based on 65 distinct 3D facial landmarks as input to LDA. The second contribution of this work involves a part-based implementation to 3D facial shape analysis and multi-class syndrome classification, and this is applied to both GE and PCA. Based on 1,786 3D facial photographs of controls and individuals from 13 different syndrome classes, a five-fold cross-validation was used to investigate both contributions. Results indicate that GE performs better than PCA as input to LDA, and this especially so for more compact (lower dimensional) spaces. In addition, a part-based approach increases performance significantly for both GE and PCA, with a more significant improvement for the latter. I.e., this contribution enhances the power of the dataset. Finally, and interestingly, according to ablation studies within the part-based approach, the upper lip is the most distinguishing facial segment for classifying genetic syndromes in our dataset, which follows clinical expectation. This work stimulates an enhanced use of advanced part-based geometric deep learning methods for 3D facial imaging in clinical genetics.
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.
Face recognition is a widely accepted biometric verification tool, as the face contains a lot of information about the identity of a person. In this study, a 2-step neural-based pipeline is presented for matching 3D facial shape to multiple DNA-related properties (sex, age, BMI and genomic background). The first step consists of a triplet loss-based metric learner that compresses facial shape into a lower dimensional embedding while preserving information about the property of interest. Most studies in the field of metric learning have only focused on 2D Euclidean data. In this work, geometric deep learning is employed to learn directly from 3D facial meshes. To this end, spiral convolutions are used along with a novel mesh-sampling scheme that retains uniformly sampled 3D points at different levels of resolution. The second step is a multi-biometric fusion by a fully connected neural network. The network takes an ensemble of embeddings and property labels as input and returns genuine and imposter scores. Since embeddings are accepted as an input, there is no need to train classifiers for the different properties and available data can be used more efficiently. Results obtained by a 10-fold crossvalidation for biometric verification show that combining multiple properties leads to stronger biometric systems. Furthermore, the proposed neural-based pipeline outperforms a linear baseline, which consists of principal component analysis, followed by classification with linear support vector machines and a Naïve Bayes-based score-fuser.
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