Quantitative cephalometry plays an essential role in clinical diagnosis, treatment, and surgery. Development of fully automated techniques for these procedures is important to enable consistently accurate computerized analyses. We study the application of deep convolutional neural networks (CNNs) for fully automated quantitative cephalometry for the first time. The proposed framework utilizes CNNs for detection of landmarks that describe the anatomy of the depicted patient and yield quantitative estimation of pathologies in the jaws and skull base regions. We use a publicly available cephalometric x-ray image dataset to train CNNs for recognition of landmark appearance patterns. CNNs are trained to output probabilistic estimations of different landmark locations, which are combined using a shape-based model. We evaluate the overall framework on the test set and compare with other proposed techniques. We use the estimated landmark locations to assess anatomically relevant measurements and classify them into different anatomical types. Overall, our results demonstrate high anatomical landmark detection accuracy ([Formula: see text] to 2% higher success detection rate for a 2-mm range compared with the top benchmarks in the literature) and high anatomical type classification accuracy ([Formula: see text] average classification accuracy for test set). We demonstrate that CNNs, which merely input raw image patches, are promising for accurate quantitative cephalometry.

Fully automated quantitative cephalometry using convolutional neural networks.

Do dental esthetics have any influence on finding a job

Applications of artificial intelligence and machine learning in orthodontics: a scoping review

Machine learning and orthodontics, current trends and the future opportunities: A scoping review

Objectives: To (1) introduce a novel machine learning method and (2) assess maxillary structure variation in unilateral canine impaction for advancing clinically viable information. Materi...

Machine learning in orthodontics: Introducing a 3D auto-segmentation and auto-landmark finder of CBCT images to assess maxillary constriction in unilateral impacted canine patients.

Objective: To establish an objective method for evaluating facial attractiveness from a set of orthodontic photographs. Materials and Methods: One hundred eight malocclusion patients randomly selected from six universities in China were randomly divided into nine groups, with each group containing an equal number of patients with Class I, II, and III malocclusions. Sixty-nine expert Chinese orthodontists ranked photographs of the patients (frontal, lateral, and frontal smiling photos) before and after orthodontic treatment from “most attractive” to “least attractive” in each group. A weighted mean ranking was then calculated for each patient, based on which a three-point scale was created. Procrustes superimposition was conducted on 101 landmarks identified on the photographs. A support vector regression (SVR) function was set up according to the coordinate values of identified landmarks of each photographic set and its corresponding grading. Its predictive ability was tested for each group in tu...

Evaluation of facial attractiveness for patients with malocclusion: A machine-learning technique employing Procrustes

Lateral cephalogram X-ray (LCX) images are essential to provide patientspecific morphological information of anatomical structures The automatic annotation of anatomical structures in cephalograms has been performed in the biomedical engineering for nearly twenty years Most systems only handle a portion of salient craniofacial landmark set [1, 2, 3] Although model-based methods can produce a full set of markers [5, 7], the pattern fitting can fail to converge in blurry images It is challenging to annotate LCX images with high fidelity In this work, we propose a novel cephalogram sketcher system as shown in Fig 1 for the automatic anatomical-structure annotation, especially for the blemished images due to structure overlappings and devicespecific distortions during projection Firstly, we introduce an hierarchical extension of a pictorial model to detect anatomical structures Secondly, the bimodal deep Boltzmann machine (DBM) is employed to sketch the structure contours Specifically, the contour sketcher takes advantages of the path in the DBM to extract the contour definitions from the patch textures by alternating Gibbs sampling Given a cephalogram I, the structure definition S, and the parameters Θ = (Θq,Θr) with respect to the intraand inter-layer correlations, the posterior probability distribution according to the Bayes rule is defined as P(S|I,Θ) ∝ P(I|S,Θ)P(S|Θ), where P(S|Θ) is a shape prior distribution P(I|S,Θ) is the image likelihood given the hierarchical architecture and the model parameters The likelihood can be factorized as a product of likelihoods of local structures

/pdf/anatomical-structure-sketcher-for-cephalograms-by-bimodal-162h6xmgsz.pdf

Anatomical Structure Sketcher for Cephalograms by Bimodal Deep Learning.

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Evaluation of facial attractiveness for patients with malocclusion: A machine-learning technique employing Procrustes

Anatomical Structure Sketcher for Cephalograms by Bimodal Deep Learning.