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Araya-Diaz, P., Ruz, G. A., & Palomino, H. M. (2013). Discovering Craniofacial Patterns Using Multivariate Cephalometric Data for Treatment Decision Making in Orthodontics. Int. J. Morphol., 31(3), 1109–1115.
Abstract: 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.
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Ruz, G. A., & Araya-Diaz, P. (2018). Predicting Facial Biotypes Using Continuous Bayesian Network Classifiers. Complexity, 4075656, 14 pp.
Abstract: 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.
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Ruz, G. A., Araya-Diaz, P., & Henriquez, P. A. (2022). Facial biotype classification for orthodontic treatment planning using an alternative learning algorithm for tree augmented Naive Bayes. BMC Med. Inform. Decis. Mak., 22, 316.
Abstract: 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, F1-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.
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