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

A Survey on Artificial Intelligence Techniques for Biomedical Image Analysis in Skeleton-Based Forensic Human Identification

This paper represents the first survey on the application of AI techniques for the analysis of biomedical images with forensic human identification purposes. Human identification is of great relevance in today’s society and, in particular, in medico-legal contexts. As consequence, all technological advances that are introduced in this field can contribute to the increasing necessity for accurate and robust tools that allow for establishing and verifying human identity. We first describe the importance and applicability of forensic anthropology in many identification scenarios. Later, we present the main trends related to the application of computer vision, machine learning and soft computing techniques to the estimation of the biological profile, the identification through comparative radiography and craniofacial superimposition, traumatism and pathology analysis, as well as facial reconstruction. The potentialities and limitations of the employed approaches are described, and we conclude with a discussion about methodological issues and future research.Spanish Ministry of Science, Innovation and UniversitiesEuropean Union (EU) PGC2018-101216-B-I00Regional Government of Andalusia under grant EXAISFI P18-FR-4262Instituto de Salud Carlos IIIEuropean Union (EU) DTS18/00136European Commission H2020-MSCA-IF-2016 through the Skeleton-ID Marie Curie Individual Fellowship 746592Spanish Ministry of Science, Innovation and Universities-CDTI, Neotec program 2019 EXP-00122609/SNEO-20191236European Union (EU)Xunta de Galicia ED431G 2019/01European Union (EU) RTI2018-095894-B-I0

A pilot study for the digital replacement of a distorted dentition acquired by Cone Beam Computed Tomography (CBCT)

Abstract Introduction: Cone beam CT (CBCT) is becoming a routine imaging modality designed for the maxillofacial region. Imaging patients with intra-oral metallic objects cause streak artefacts. Artefacts impair any virtual model by obliterating the teeth. This is a major obstacle for occlusal registration and the fabrication of orthognathic wafers to guide the surgical correction of dentofacial deformities. Aims and Objectives: To develop a method of replacing the inaccurate CBCT images of the dentition with an accurate representation and test the feasibility of the technique in the clinical environment. Materials and Method: Impressions of the teeth are acquired and acrylic baseplates constructed on dental casts incorporating radiopaque registration markers. The appliances are fitted and a preoperative CBCT is performed. Impressions are taken of the dentition with the devices in situ and subsequent dental models produced. The models are scanned to produce a virtual model. Both images of the patient and the model are imported into a virtual reality software program and aligned on the virtual markers. This allows the alignment of the dentition without relying on the teeth for superimposition. The occlusal surfaces of the dentition can be replaced with the occlusal image of the model. Results: The absolute mean distance of the mesh between the markers in the skulls was in the region of 0.09mm ± 0.03mm; the replacement dentition had an absolute mean distance of about 0.24mm ± 0.09mm. In patients the absolute mean distance between markers increased to 0.14mm ± 0.03mm. It was not possible to establish the discrepancies in the patient’s dentition, since the original image of the dentition is inherently inaccurate. Conclusion: It is possible to replace the CBCT virtual dentition of cadaveric skulls with an accurate representation to create a composite skull. The feasibility study was successful in the clinical arena. This could be a significant advancement in the accuracy of surgical prediction planning, with the ultimate goal of fabrication of a physical orthognathic wafer using reverse engineering

Craniofacial Growth Series Volume 56

https://deepblue.lib.umich.edu/bitstream/2027.42/153991/1/56th volume CF growth series FINAL 02262020.pdfDescription of 56th volume CF growth series FINAL 02262020.pdf : Proceedings of the 46th Annual Moyers Symposium and 44th Moyers Presymposiu

A theoretical and empirical study on unbiased boundary-extended crossover for real-valued representation

Copyright © 2012 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Information Sciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Sciences Vol. 183 Issue 1 (2012), DOI: 10.1016/j.ins.2011.07.013We present a new crossover operator for real-coded genetic algorithms employing a novel methodology to remove the inherent bias of pre-existing crossover operators. This is done by transforming the topology of the hyper-rectangular real space by gluing opposite boundaries and designing a boundary extension method for making the fitness function smooth at the glued boundary. We show the advantages of the proposed crossover by comparing its performance with those of existing ones on test functions that are commonly used in the literature, and a nonlinear regression on a real-world dataset

MusMorph, a database of standardized mouse morphology data for morphometric meta-analyses.

Complex morphological traits are the product of many genes with transient or lasting developmental effects that interact in anatomical context. Mouse models are a key resource for disentangling such effects, because they offer myriad tools for manipulating the genome in a controlled environment. Unfortunately, phenotypic data are often obtained using laboratory-specific protocols, resulting in self-contained datasets that are difficult to relate to one another for larger scale analyses. To enable meta-analyses of morphological variation, particularly in the craniofacial complex and brain, we created MusMorph, a database of standardized mouse morphology data spanning numerous genotypes and developmental stages, including E10.5, E11.5, E14.5, E15.5, E18.5, and adulthood. To standardize data collection, we implemented an atlas-based phenotyping pipeline that combines techniques from image registration, deep learning, and morphometrics. Alongside stage-specific atlases, we provide aligned micro-computed tomography images, dense anatomical landmarks, and segmentations (if available) for each specimen (N = 10,056). Our workflow is open-source to encourage transparency and reproducible data collection. The MusMorph data and scripts are available on FaceBase ( www.facebase.org , https://doi.org/10.25550/3-HXMC ) and GitHub ( https://github.com/jaydevine/MusMorph )

MusMorph, a database of standardized mouse morphology data for morphometric meta-analyses

Complex morphological traits are the product of many genes with transient or lasting developmental effects that interact in anatomical context. Mouse models are a key resource for disentangling such effects, because they offer myriad tools for manipulating the genome in a controlled environment. Unfortunately, phenotypic data are often obtained using laboratory-specific protocols, resulting in self-contained datasets that are difficult to relate to one another for larger scale analyses. To enable meta-analyses of morphological variation, particularly in the craniofacial complex and brain, we created MusMorph, a database of standardized mouse morphology data spanning numerous genotypes and developmental stages, including E10.5, E11.5, E14.5, E15.5, E18.5, and adulthood. To standardize data collection, we implemented an atlas-based phenotyping pipeline that combines techniques from image registration, deep learning, and morphometrics. Alongside stage-specific atlases, we provide aligned micro-computed tomography images, dense anatomical landmarks, and segmentations (if available) for each specimen (N = 10,056). Our workflow is open-source to encourage transparency and reproducible data collection. The MusMorph data and scripts are available on FaceBase (www.facebase.org, https://doi.org/10.25550/3-HXMC) and GitHub (https://github.com/jaydevine/MusMorph)

MORPHOLOGY OF THE FACE AS A POSTMORTEM PERSONAL IDENTIFIER

The human face carries some of the most individualizing features suitable for the personal identification. Facial morphology is used for the face matching of living. An extensive research is conducted to develop the matching algorithm to mimic the human ability to recognize and match faces. Human ability to recognize and match faces, however, is not errorless and it serves as the main argument precluding the visual facial matching from its use as an identification tool. The human face keeps its individuality after death. Compared to the faces of living, the faces of deceased are rarely used or researched for the face matching. Different factors influence the appearance of the face of the deceased compared to the face of the living, namely the early postmortem changes and decomposition process. On the other hand, the literature review showed the use of visual recognition in multiple cases of identity assessment after the natural disasters. Presented dissertation thesis is composed of several projects focused on the possibility of personal identification of the decedents solely based on the morphology of their face. Dissertation explains the need for such identification and explores the error rates of the visual recognition of deceased, the progress of facial changes due to the early decomposition and the possibility of utilization of soft biometric traits, specifically facial moles. Lastly, the dissertation presents the use of shape index (s) as a quality indicator of three different 3D scanners aimed towards the most suitable method for obtaining facial postmortem 3D images