Bayesian networks are useful machine learning techniques that are able to combine quantitative modeling, through probability theory, with qualitative modeling, through graph theory for visualization. We apply Bayesian network classifiers to the facial biotype classification problem, an important stage during orthodontic treatment planning. For this, we present adaptations of classical Bayesian networks classifiers to handle continuous attributes; also, we propose an incremental tree construction procedure for tree like Bayesian network classifiers. We evaluate the performance of the proposed adaptations and compare them with other continuous Bayesian network classifiers approaches as well as support vector machines. The results under the classification performance measures, accuracy and kappa, showed the effectiveness of the continuous Bayesian network classifiers, especially for the case when a reduced number of attributes were used. Additionally, the resulting networks allowed visualizing the probability relations amongst the attributes under this classification problem, a useful tool for decision-making for orthodontists.

Objective To evaluate the validity of craniofacial growth predictors in class II and III malocclusion. Material and methods An electronic search was conducted until August 2020 in PubMed, Cochrane Library, Embase, EBSCOhost, ScienceDirect, Scopus, Bireme, Lilacs and Scielo including all languages. The articles were selected and analyzed by two authors independently and the selected studies was assessed using the 14‐item Quality Assessment Tool for Diagnostic Accuracy Studies (QUADAS‐2). The quality of evidence and strength of recommendation was assessed by the GRADE tool. Results In a selection process of two phases, 10 articles were included. The studies were grouped according to malocclusion growth predictor in (1) class II (n = 4); (2) class III (n = 5) and (3) class II and III (n = 1). The predictors were mainly based on data extracted from cephalometries and characterized by: equations, structural analysis, techniques and computer programs among others. The analyzed studies were methodologically heterogeneous and had low to moderate quality. For class II malocclusion, the predictors proposed in the studies with the best methodological quality were based on mathematical models and the Fishman system of maturation assessment. For class III malocclusion, the Fishman system could provide adequate growth prediction for short‐ and long‐term. Conclusions Because of the heterogeneity of the design, methodology and the quality of the articles reviewed, it is not possible to establish only a growth prediction system for class II and III malocclusion. High‐quality cohort studies are needed, well defined data extraction from cephalometries, radiographies and clinical characteristics are required to design a reliable predictor.

Background When designing a treatment in orthodontics, especially for children and teenagers, it is crucial to be aware of the changes that occur throughout facial growth because the rate and direction of growth can greatly affect the necessity of using different treatment mechanics. This paper presents a Bayesian network approach for facial biotype classification to classify patients’ biotypes into Dolichofacial (long and narrow face), Brachyfacial (short and wide face), and an intermediate kind called Mesofacial, we develop a novel learning technique for tree augmented Naive Bayes (TAN) for this purpose. Results The proposed method, on average, outperformed all the other models based on accuracy, precision, recall, $$F_{1}\hbox {-score}$$ F 1 -score , and kappa, for the particular dataset analyzed. Moreover, the proposed method presented the lowest dispersion, making this model more stable and robust against different runs. Conclusions The proposed method obtained high accuracy values compared to other competitive classifiers. When analyzing a resulting Bayesian network, many of the interactions shown in the network had an orthodontic interpretation. For orthodontists, the Bayesian network classifier can be a helpful decision-making tool.

ARAYA-DÍAZ, P.; RUZ, G. A. & PALOMINO, H. M.Discovering craniofacial patterns using multivariate cephalometric data for treatment decision making in orthodontics. Int. J. Morphol., 31(3):1109-1115, 2013. SUMMARY:The aim was to find craniofacial morphology patterns in a multivariate cephalometric database using a clustering technique. Cephalometric analysis was performed in a sample of 100 teleradiographs collected from Chilean orthodontic patients. Thirty cephalometric measurements were taken from commonly used analysis. The computed variables were used to perform a clustering analysis with the k-means algorithm to identify patterns of craniofacial morphology. The J48 decision tree was used to analyze each cluster, and the ANOVA test to determine the statistical differences between the clusters. Four clusters were found that had significant differences (P<0.001) in 24 of the 30 variables studied, suggesting that they represent different patterns of craniofacial form. Using the decision tree, 8 of the 30 variables appeared to be relevant for describing the clusters. The clustering analysis is effective in identifying different craniofacial patterns based on a multivariate database. The distinct clusters appear to be caused by differences in the compensation process of the facial structure responding to a genetically determined cranial and mandible form. The proposed method can be applied to several databases, creating specific classifications for each one of them.

RESUMEN: El objetivo de este trabajo fue evaluar el desplazamiento de los puntos craneales: Nasion, Silla, Basion, Porion, Orbitario y Pterigoideo, utilizados como referencia en los análisis cefalométricos de Jarabak y Ricketts durante el crecimiento activo. Se seleccionaron 120 telerradiografías de perfil en formato digital, correspondientes a 60 pacientes con 2 telerradiografías cada uno, tomadas con un intervalo de tiempo mínimo de 1 año (T1 y T2), en donde T1 se encuentra antes o durante el peak de crecimiento según el Estado de Maduración Cervical Vertebral (CVM) I, II ó III de Baccetti y T2 en estadio CVM IV,V,VI (después del peak de crecimiento). Un examinador previamente calibrado, ubicó los puntos analizados y para evaluar su desplazamiento, se realizaron mediciones en T1 y T2 (3 variables para cada punto), usando como referencia 2 planos que no se modifican a partir de los 5 años de edad (LCB y Vert-T). Para determinar el desplazamiento de los puntos, se calculó la variación promedio observada entre T1 y T2 y se realizó la prueba t para muestras pareadas o Wilcoxon (según distribución) para determinar la existencia de diferencias significativas. Además, se comparó la muestra por sexo, CVM inicial y CVM final. Se encontraron variaciones entre T1 y T2 en todas las medidas, aunque sólo en 5 de ellas se encontraron diferencias significativas; no se encontró diferencias al comparar por sexo, CVM inicial y final. Es así como podemos concluir que todos los puntos craneales analizados sufren desplazamiento durante el crecimiento. Los puntos Basion y Orbitario son los que sufren mayor desplazamiento. Es necesario analizar las implicancias de estas variaciones en los resultados obtenidos de los análisis cefalométrico y evaluar la necesidad de utilizar puntos de referencia alternativos.PALABRAS CLAVE: Puntos de referencia; Crecimiento Craneofacial; Cefalometría. INTRODUCCIÓNCon la introducción de la cefalometría radiológica en 1931 por Broadbent, se dió inicio a un gran desarrollo en el área de la ortodoncia, debido a que fue posible medir directamente las dimensiones esqueléticas óseas, obteniéndose una interpretación más objetiva de la morfología cráneofacial. Esto permitió el estudio de los múltiples cambios involucrados en el proceso de crecimiento y desarrollo, tanto como la evaluación de las variaciones producidas por el tratamiento de ortodoncia u ortopedia y su valoración clínica (Viazis, 1995;Celik et al., 2009).Diversos análisis cefalométricos descritos en la literatura como Ricketts, Steiner, Jarabak, entre otros, se basan en la identificación de ciertos puntos de referencia anatómi-cos o construidos en base a diversas estructuras craneofaciales, a partir de los cuales se establecen mediciones angulares y lineales (Aguila, 1991.) En general, estos análisis utilizan planos de referencia conformados por puntos ubicados en el cráneo como Silla-Nasion (base craneal anterior), Porión-Orbitario (Plano Frankfort), Basion-Nasion entre otros, que son considerados estables en el adulto, pero que podrían modifica...