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

Morphological evaluation of head in Turkman males in Gorgan-North of Iran

Cephalometry or measurement of human head is used in identification, forensic medicine, plastic surgery, orthodontics, archeology and examine the differences between races and ethnicities. This descriptive investigation was undertaken on 198 young Turkman males to determine the cephalic index and head phenotype among them in Gorgan, North of Iran. In this study cephalic index was determined by classic cephalometric method. Mean and standard deviation of cephalic index was 80.4 ± 4. Based on the cephalic index, the head shape of 42.4% of individuals were brachycephalic, 7.6% hyperbrachycephalic, 40.9% mesocephalic and 8.1% dolicocephalic. This research showed that Turkman individuals have typical brachycephalic phenotype. In comparison to other studies, we can conclude that the ethnic factor has an effective role on head phenotype in North of Iran

The effect of ethnic factor on cephalic index in 17-20 years old females of north of Iran [Efecto del factor étnico en el índice cefálico en mujeres entre 17 y 20 años de edad del norte de Irán]

Cephalic index and head shape are affected by geographical, gender, age, racial and ethnic factors. This study was carried out to determine cephalic index and head shape in 17-20 years old female in Gorgan, North of Iran. This descriptive and cross sectional study is undertaken on 410 normal 17-20 years old female (Turkman group: n=203, Fars group: n=207). The study was done by classic cephalometry in Gorgan North of Iran. Means and SD of cephalic index was 85 ± 4.5 and 82.8 ± 3.6 in native Fars and Turkman groups, respectively. Dominant and rare type of head shape in native Fars group were hyperbrachycephalic (53.6%) and dolichocephalic (15%), and in Turkman group were brachycephalic (58.1%) and dolichocephalic (0.05%), respectively. With noticing of our results and other studies in the world, we can conclude that the role of ethnic factor on head dimensions. © 2007 Sociedad Chilena de Anatomía

The reliability of cephalometric tracing using AI

Introduction : L'objectif de cette étude est de comparer la différence entre l'analyse céphalométrique manuelle et l'analyse automatisée par l’intelligence artificielle afin de confirmer la fiabilité de cette dernière. Notre hypothèse de recherche est que la technique manuelle est la plus fiable des deux méthodes. Méthode : Un total de 99 radiographies céphalométriques latérales sont recueillies. Des tracés par technique manuelle (MT) et par localisation automatisée par intelligence artificielle (AI) sont réalisés pour toutes les radiographies. La localisation de 29 points céphalométriques couramment utilisés est comparée entre les deux groupes. L'erreur radiale moyenne (MRE) et un taux de détection réussie (SDR) de 2 mm sont utilisés pour comparer les deux groupes. Le logiciel AudaxCeph version 6.2.57.4225 est utilisé pour l'analyse manuelle et l'analyse AI. Résultats : Le MRE et SDR pour le test de fiabilité inter-examinateur sont respectivement de 0,87 ± 0,61mm et 95%. Pour la comparaison entre la technique manuelle MT et le repérage par intelligence artificielle AI, le MRE et SDR pour tous les repères sont respectivement de 1,48 ± 1,42 mm et 78 %. Lorsque les repères dentaires sont exclus, le MRE diminue à 1,33 ± 1,39 mm et le SDR augmente à 84 %. Lorsque seuls les repères des tissus durs sont inclus (excluant les points des tissus mous et dentaires), le MRE diminue encore à 1,25 ± 1,09 mm et le SDR augmente à 85 %. Lorsque seuls les points de repère des tissus mous sont inclus, le MRE augmente à 1,68 ± 1,89 mm et le SDR diminue à 78 %. Conclusion: La performance du logiciel est similaire à celles précédemment rapportée dans la littérature pour des logiciels utilisant un cadre de modélisation similaire. Nos résultats révèlent que le repérage manuel a donné lieu à une plus grande précision. Le logiciel a obtenu de très bons résultats pour les points de tissus durs, mais sa précision a diminué pour les tissus mous et dentaires. Nous concluons que cette technologie est très prometteuse pour une application en milieu clinique sous la supervision du docteur.Introduction: The objective of this study is to compare the difference between manual cephalometric analysis and automatic analysis by artificial intelligence to confirm the reliability of the latter. Our research hypothesis is that the manual technique is the most reliable of the methods and is still considered the gold standard. Method: A total of 99 lateral cephalometric radiographs were collected in this study. Manual technique (MT) and automatic localization by artificial intelligence (AI) tracings were performed for all radiographs. The localization of 29 commonly used landmarks were compared between both groups. Mean radial error (MRE) and a successful detection rate (SDR) of 2mm were used to compare both groups. AudaxCeph software version 6.2.57.4225 (Audax d.o.o., Ljubljana, Slovenia) was used for both manual and AI analysis. Results: The MRE and SDR for the inter-examinator reliability test were 0.87 ± 0.61mm and 95% respectively. For the comparison between the manual technique MT and landmarking with artificial intelligence AI, the MRE and SDR for all landmarks were 1.48 ± 1.42mm and 78% respectively. When dental landmarks are excluded, the MRE decreases to 1.33 ± 1.39mm and the SDR increases to 84%. When only hard tissue landmarks are included (excluding soft tissue and dental points) the MRE decreases further to 1.25 ± 1.09mm and the SDR increases to 85%. When only soft tissue landmarks are included the MRE increases to 1.68 ± 1.89mm and the SDR decreases to 78%. Conclusion: The software performed similarly to what was previously reported in literature for software that use analogous modeling framework. Comparing the software’s landmarking to manual landmarking our results reveal that the manual landmarking resulted in higher accuracy. The software operated very well for hard tissue points, but its accuracy went down for soft and dental tissue. Our conclusion is this technology shows great promise for application in clinical settings under the doctor’s supervision

Contributions to the three-dimensional virtual treatment planning of orthognathic surgery

Orientadores: José Mario De Martino, Luis Augusto PasseriTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: A tecnologia mais recente à disposição da Cirurgia Ortognática possibilita que o diagnóstico e o planejamento do tratamento das deformidades dentofaciais sejam realizados sob uma representação virtual tridimensional (3D) da cabeça do paciente. Com o propósito de contribuir para o aperfeiçoamento desta tecnologia, o trabalho apresentado nesta tese identificou e tratou quatro problemas. A primeira contribuição consistiu na verificação da validade da hipótese de que a mudança de definição do plano horizontal de Frankfort não produz diferenças de medição clinicamente relevantes quando sob indivíduos cujos crânios são consideravelmente simétricos. Os resultados da análise realizada no contexto deste tese indicam que, ao contrário do que se presumia, a hipótese é falsa. A segunda contribuição consistiu na extensão do método de análise cefalométrica de McNamara para que ele pudesse produzir valores 3D. Ao contrário de outros métodos de análise cefalométrica 3D, a extensão criada produz valores verdadeiramente 3D, não perde as informações do método original e preserva as definições geométricas originais das linhas e planos cefalométricos. A terceira contribuição consistiu a) no estabelecimento de normas cefalométricas para brasileiros adultos de ascendência europeia, a partir de imagens de tomografia computadorizada de feixe cônico, que produz uma imagem craniofacial mais precisa e confiável do que a telerradiografia; e b) na avaliação de dimorfismo sexual, para a identificação de características anatômicas diferenciadas entre homens e mulheres desta população. A quarta e última contribuição consistiu na automatização da principal etapa da tecnologia em questão, na qual o cirurgião executa o reposicionamento dos segmentos ósseos maxilares no crânio. O método criado é capaz de corrigir automaticamente os problemas dentofaciais mais comuns tratados pela Cirurgia Ortognática, que envolvem maloclusão esquelética, assimetria facial e discrepância de maxilares. Todas as contribuições deste trabalho foram publicadas em periódicos internacionais do campo da Odontologia e afinsAbstract: The latest technology available for orthognathic surgery allows the diagnosis and treatment planning of dentofacial deformities based on a three-dimensional (3D) virtual representation of the patient's head. In order to contribute to the improvement of this technology, the work presented in this thesis identified and treated four problems. The first contribution consisted in testing the validity of the hypothesis that changing the definition of the Frankfort horizontal plane does not produce clinically relevant measurement differences for subjects whose skulls are considerably symmetrical. The results of the analysis performed in this thesis indicate that, contrary to what was presumed, the hypothesis is false. The second contribution is an extension of the McNamara's method of cephalometric analysis to produce 3D values. Unlike other methods of 3D cephalometric analysis, the extension produces true 3D values, does not lose information captured by the original method, and preserves the original geometric definitions of the cephalometric lines and planes. The third contribution consisted in a) establishing cephalometric norms for Brazilian adults of European descent, based on images from cone-beam computed tomography, which produce a more accurate and reliable craniofacial image than cephalometric radiography; and b) evaluating sexual dimorphism, for the identification of distinct anatomic features between males and females of this population. The fourth contribution consisted in automating the main stage of the technology in question, in which the surgeon performs the positioning of jaw bone segments in the skull. The created method is able to automatically correct the most common dentofacial problems treated by orthognathic surgery, which involves skeletal malocclusion, facial asymmetry, and jaw discrepancy. The contributions of this work were published in international journals of the field of Dentistry and relatedDoutoradoEngenharia de ComputaçãoDoutor em Engenharia ElétricaCAPE

Estimation of the cranial capacity and brain weight of Iranian female newborns

Anthropological measurements such as brain weight and cranial capacity are of practical use for the evaluation the health of newborns and also as a basis for the cranium alterations in future years of life. The present research was carried out to determine brain weight and cranial capacity and the effect of the ethnic factor on them in female newborns in northern Iran. In this study, 423 normal female newborns in Turkman (n=211) and Native Fars (n=212) groups were evaluated by classic cephalometry. The means and SD of the cranial capacity in the native Fars and Turkman groups were 438.16 ± 63.5 and 418.84 ± 33.19 milliliters, respectively (p<0.05). The means and SD of brain weight in Fars and Turkman newborns were 453.50 ± 65.72 and 433.50 ± 34.35 grams respectively (p<0.05). The results of this investigation show that the ethnic factor could influence brain weight and cranial capacity in Iranian female newborns

An Approach for Efficient Detection of Cephalometric Landmarks

AbstractIn this paper, a method is developed for the automated identification of cephalometric landmarks in orthodontics. The process of soft tissue edge detection is divided into two steps: detecting the sub-images that contained the required landmarks using combination of the Histograms of Oriented Gradients (HOG) descriptor with the Support Vector Machine (SVM), then utilizing Thresholding and Mathematical Morphological (TMM) algorithm to trace soft tissue profile. In addition, the mandible's edge is detected by the Active contours without edges (Chan-Vese method). Finally, the landmarks of soft tissue profile and the mandible's edge are pinned based on analyzing the contour plot of these lines. The simulation results have high accuracy

Accuracy of automated 3D cephalometric landmarks by deep learning algorithms: systematic review and meta-analysis

Objectives The aim of the present systematic review and meta-analysis is to assess the accuracy of automated landmarking using deep learning in comparison with manual tracing for cephalometric analysis of 3D medical images. Methods PubMed/Medline, IEEE Xplore, Scopus and ArXiv electronic databases were searched. Selection criteria were: ex vivo and in vivo volumetric data images suitable for 3D landmarking (Problem), a minimum of five automated landmarking performed by deep learning method (Intervention), manual landmarking (Comparison), and mean accuracy, in mm, between manual and automated landmarking (Outcome). QUADAS-2 was adapted for quality analysis. Meta-analysis was performed on studies that reported as outcome mean values and standard deviation of the difference (error) between manual and automated landmarking. Linear regression plots were used to analyze correlations between mean accuracy and year of publication. Results The initial electronic screening yielded 252 papers published between 2020 and 2022. A total of 15 studies were included for the qualitative synthesis, whereas 11 studies were used for the meta-analysis. Overall random effect model revealed a mean value of 2.44 mm, with a high heterogeneity (I-2 = 98.13%, tau(2) = 1.018, p-value &lt; 0.001); risk of bias was high due to the presence of issues for several domains per study. Meta-regression indicated a significant relation between mean error and year of publication (p value = 0.012). Conclusion Deep learning algorithms showed an excellent accuracy for automated 3D cephalometric landmarking. In the last two years promising algorithms have been developed and improvements in landmarks annotation accuracy have been done

Morphometric study of cephalo-facial indices among Bini children in southern Nigeria

Cephalometry is an important branch of anthropometry which involves the morphological study of structures present in the human head or scientific measurement of the dimensions of the head. Some of the most important cephalometric parameters include the length/height and breadth/width of the head, the face and the nose as well as their respective indices. These cephalometric parameters are vital in the description of variation which is a common phenomenon that characterizes human physiognomy. They are also useful in the description of human inter-racial and intra-racial  similarities both within and across gender. This study involved 450 Bini children (235 males and 215 females) between ages 5-12 years. The length and width of the head and face of each subject was measured between the appropriate anatomical landmarks using spreading and sliding calipers. The measurements were used to calculate the cephalic and facial indices for each subject. The result showed sexual variation in both cephalic and facial indices among the Bini children with the males having higher values than the females. Also, the result of this study showed that prevalence of brachycephalic head type among both male (51.1%) and female (49.8%) Bini children. The mesoproscopic face type was the most prevalent face type among both male (62.6%) and female (47.4%) Bini children. The cephalo-facial indices are vital in demonstrating similarity and variation in physical morphologies of individuals or group of people of different ethnicity, races, gender and geographical locations. Keywords: Cephalometry, Cephalic index, facial index, Bini children, Nigeri