Challenges in measuring angles between craniofacial structures

Objective: Three-dimensional (3D) angular measurements between craniofacial planes pose challenges to quantify maxillary and mandibular skeletal discrepancies in surgical treatment planning. This study aims to compare the reproducibility and reliability of two modules to measure angles between planes or lines in 3D virtual surface models. Methodology: Twenty oriented 3D virtual surface models de-identified and constructed from CBCT scans were randomly selected. Three observers placed landmarks and oriented planes to determine angular measurements of pitch, roll and yaw using (1) 3D pre-existing planes, (2) 3D planes created from landmarks and (3) lines created from landmarks. Inter- and intra-observer reproducibility and repeatability were examined using the Intra-Class Correlation (ICC) test. One observer repeated the measurements with an interval of 15 days. ANOVA was applied to compare the 3 methods. Results: The three methods tested provided statistically similar, reproducible and reliable angular measurements of the facial structures. A strong ICC varying from 0.92 to 1.00 was found for the intra-observer agreement. The inter-observer ICC varied from 0.84 to 1.00. Conclusion: Measurements of 3D angles between facial planes in a common coordinate system are reproducible and repeatable either using 3D pre-existing planes, created based on landmarks or angles between lines created from landmarks

Automatic Three-Dimensional Cephalometric Annotation System Using Three-Dimensional Convolutional Neural Networks

Background: Three-dimensional (3D) cephalometric analysis using computerized tomography data has been rapidly adopted for dysmorphosis and anthropometry. Several different approaches to automatic 3D annotation have been proposed to overcome the limitations of traditional cephalometry. The purpose of this study was to evaluate the accuracy of our newly-developed system using a deep learning algorithm for automatic 3D cephalometric annotation. Methods: To overcome current technical limitations, some measures were developed to directly annotate 3D human skull data. Our deep learning-based model system mainly consisted of a 3D convolutional neural network and image data resampling. Results: The discrepancies between the referenced and predicted coordinate values in three axes and in 3D distance were calculated to evaluate system accuracy. Our new model system yielded prediction errors of 3.26, 3.18, and 4.81 mm (for three axes) and 7.61 mm (for 3D). Moreover, there was no difference among the landmarks of the three groups, including the midsagittal plane, horizontal plane, and mandible (p>0.05). Conclusion: A new 3D convolutional neural network-based automatic annotation system for 3D cephalometry was developed. The strategies used to implement the system were detailed and measurement results were evaluated for accuracy. Further development of this system is planned for full clinical application of automatic 3D cephalometric annotation

Artificial intelligence and 3D printing technology in orthodontics: future and scope

New digital technologies, like in other fields, have revolutionized the health care field and orthodontic practice in the 21st century. They can assist the health care professionals in working more efficiently by saving time and improving patient care. Recent advances in artificial intelligence (AI) and 3D printing technology are useful for improving diagnosis and treatment planning, creating algorithms and manufacturing customized orthodontic appliances. AI accomplishes the task of human beings with the help of machines and technology. In orthodontics, AI-based models have been used for diagnosis, treatment planning, clinical decision-making and prognosis prediction. It minimizes the required workforce and speeds up the diagnosis and treatment procedure. In addition, the 3D printing technology is used to fabricate study models, clear aligner models, surgical guides for inserting mini-implants, clear aligners, lingual appliances, wires components for removable appliances and occlusal splints. This paper is a review of the future and scope of AI and 3D printing technology in orthodontics

Current research opportunities of image processing and computer vision

Image processing and computer vision is an important and essential area in today’s scenario. Several problems can be solved through computer vision techniques. There are a large number of challenges and opportunities which require skills in the field of computer vision to address them. Computer vision applications cover each band of the electromagnetic spectrum and there are numerous applications in every band. This article is targeted to the research students, scholars and researchers who are interested to solve the problems in the field of image processing and computer vision. It addresses the opportunities and current trends of computer vision applications in all emerging domains. The research needs are identified through available literature survey and classified in the corresponding domains. The possible exemplary images are collected from the different repositories available for research and shown in this paper. The opportunities mentioned in this paper are explained through the images so that a naive researcher can understand it well before proceeding to solve the corresponding problems. The databases mentioned in this article could be useful for researchers who are interested in further solving the problem. The motivation of the article is to expose the current opportunities in the field of image processing and computer vision along with corresponding repositories. Interested researchers who are working in the field can choose a problem through this article and can get the experimental images through the cited references for working further.

3D cephalometric landmark detection by multiple stage deep reinforcement learning

The lengthy time needed for manual landmarking has delayed the widespread adoption of three-dimensional (3D) cephalometry. We here propose an automatic 3D cephalometric annotation system based on multi-stage deep reinforcement learning (DRL) and volume-rendered imaging. This system considers geometrical characteristics of landmarks and simulates the sequential decision process underlying human professional landmarking patterns. It consists mainly of constructing an appropriate two-dimensional cutaway or 3D model view, then implementing single-stage DRL with gradient-based boundary estimation or multi-stage DRL to dictate the 3D coordinates of target landmarks. This system clearly shows sufficient detection accuracy and stability for direct clinical applications, with a low level of detection error and low inter-individual variation (1.96 ± 0.78 mm). Our system, moreover, requires no additional steps of segmentation and 3D mesh-object construction for landmark detection. We believe these system features will enable fast-track cephalometric analysis and planning and expect it to achieve greater accuracy as larger CT datasets become available for training and testing.ope

Odontology & artificial intelligence

Neste trabalho avaliam-se os três fatores que fizeram da inteligência artificial uma tecnologia essencial hoje em dia, nomeadamente para a odontologia: o desempenho do computador, Big Data e avanços algorítmicos. Esta revisão da literatura avaliou todos os artigos publicados na PubMed até Abril de 2019 sobre inteligência artificial e odontologia. Ajudado com inteligência artificial, este artigo analisou 1511 artigos. Uma árvore de decisão (If/Then) foi executada para selecionar os artigos mais relevantes (217), e um algoritmo de cluster k-means para resumir e identificar oportunidades de inovação. O autor discute os artigos mais interessantes revistos e compara o que foi feito em inovação durante o International Dentistry Show, 2019 em Colónia. Concluiu, assim, de forma crítica que há uma lacuna entre tecnologia e aplicação clínica desta, sendo que a inteligência artificial fornecida pela indústria de hoje pode ser considerada um atraso para o clínico de amanhã, indicando-se um possível rumo para a aplicação clínica da inteligência artificial.There are three factors that have made artificial intelligence (AI) an essential technology today: the computer performance, Big Data and algorithmic advances. This study reviews the literature on AI and Odontology based on articles retrieved from PubMed. With the help of AI, this article analyses a large number of articles (a total of 1511). A decision tree (If/Then) was run to select the 217 most relevant articles-. Ak-means cluster algorithm was then used to summarize and identify innovation opportunities. The author discusses the most interesting articles on AI research and compares them to the innovation presented during the International Dentistry Show 2019 in Cologne. Three technologies available now are evaluated and three suggested options are been developed. The author concludes that AI provided by the industry today is a hold-up for the praticioner of tomorrow. The author gives his opinion on how to use AI for the profit of patients

Optimization of Cephalometric Analysis

Cílem této bakalářská práce je vytvořit aplikaci pro optimlizovanou kefalometrickou analýzu dle vybraných geometrických parametrů zadaných ortodontisty. Klíčovými prvky této práce je zjištění potřebných geometrických parametrů, návrh a implementace analytické matematiky dle zjištěného a následné vytvoření jednoduchého uživatelského rozhraní. Software má umožnit uživateli načtení telerentgenových (dRTG) snímků lebky s následnou semiautomatizovanou analýzou důležitých kefalometrických bodů, které byly z hlediska ortodontického plánování léčby stanoveny jako klíčové. Vytvořená aplikace byla testována na vybraných kefalometrických snímcích na ortodontickém pracovišti. Výstupem bakalářské práce je programová aplikace, která umožní uživateli provést kefalometrickou analýzou vybraných parametrů na dRTG snímku, uložit výsledky a následně porovnat získaná data v rámci zhodnocení přínosnosti léčby. Programové řešení umožní uživateli analyzovat pacientův snímek před léčbou, uložit tyto analyzované kefalometrické parametry, následně načíst nový snímek po léčbě a předešlá data původního snímku nahrát do nového snímku. Znovunačtení má ukázat uživateli, kde se původní kefalometrické body nacházely.The aim of this thesis is to create an application for accelerated practice. The key elements of this work are significant geometric parameters, draft and implementing analytical mathematical system using a simple interface. The software is intended to allow the user to read cephalometric images that are related to subsequent planning information. The application was tested on selected cephalometric data at an orthodontic workplace. The output of the bachelor thesis is an application that enables the user to perform cephalometric analysis of selected geometrical parameters on a cephalometric X-ray image, save the results and then compare the obtained data as part of the evaluation of the benefit of treatment. Thus, the software will allow the user to analyse the patient's image prior to treatment, store these analysed cephalometric parameters, then load a new post-treatment image and upload the previous data from the original image to a new one. Refreshing should show the user where the original cephalometric points were located.450 - Katedra kybernetiky a biomedicínského inženýrstvívýborn

Accuracy of 3D cephalometric measurements based on an automatic knowledge-based landmark detection algorithm

PURPOSE: To evaluate the accuracy of three-dimensional cephalometric measurements obtained through an automatic landmark detection algorithm compared to those obtained through manual identification. METHODS: The study demonstrates a comparison of 51 cephalometric measurements (28 linear, 16 angles and 7 ratios) on 30 CBCT (cone beam computed tomography) images. The analysis was performed to compare measurements based on 21 cephalometric landmarks detected automatically and those identified manually by three observers. RESULTS: Inter-observer ICC for each landmark was found to be excellent ([Formula: see text]) among three observers. The unpaired t-test revealed that there was no statistically significant difference in the measurements based on automatically detected and manually identified landmarks. The difference between the manual and automatic observation for each measurement was reported as an error. The highest mean error in the linear and angular measurements was found to be 2.63 mm ([Formula: see text] distance) and [Formula: see text] ([Formula: see text]-Me angle), respectively. The highest mean error in the group of distance ratios was 0.03 (for N-Me/N-ANS and [Formula: see text]). CONCLUSION: Cephalometric measurements computed from automatic detection of landmarks on 3D CBCT image were as accurate as those computed from manual identification