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Modelling the longevity of dental restorations by means of a CBR system.

Aliaga IJ, Vera V, De Paz JF, García AE, Mohamad MS - Biomed Res Int (2015)

Bottom Line: The data will be treated confidentially according to the Organic Law 15/1999 on 13 December on the Protection of Personal Data.This paper also presents a clustering technique capable of identifying the most significant cases with which to instantiate the case-base.In order to classify the cases, a mixture of experts is used which incorporates a Bayesian network and a multilayer perceptron; the combination of both classifiers is performed with a neural network.

View Article: PubMed Central - PubMed

Affiliation: Department of Conservative Dentistry, Complutense University of Madrid, Plaza Ramón y Cajal, s/n, 28040 Madrid, Spain.

ABSTRACT
The lifespan of dental restorations is limited. Longevity depends on the material used and the different characteristics of the dental piece. However, it is not always the case that the best and longest lasting material is used since patients may prefer different treatments according to how noticeable the material is. Over the last 100 years, the most commonly used material has been silver amalgam, which, while very durable, is somewhat aesthetically displeasing. Our study is based on the collection of data from the charts, notes, and radiographic information of restorative treatments performed by Dr. Vera in 1993, the analysis of the information by computer artificial intelligence to determine the most appropriate restoration, and the monitoring of the evolution of the dental restoration. The data will be treated confidentially according to the Organic Law 15/1999 on 13 December on the Protection of Personal Data. This paper also presents a clustering technique capable of identifying the most significant cases with which to instantiate the case-base. In order to classify the cases, a mixture of experts is used which incorporates a Bayesian network and a multilayer perceptron; the combination of both classifiers is performed with a neural network.

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(a) A failed amalgam restoration on a posterior tooth. (b) Posterior tooth amalgam restoration has been redone. (c) A failed composite restoration on a posterior tooth. (d) Posterior tooth composite restoration has been redone.
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fig2: (a) A failed amalgam restoration on a posterior tooth. (b) Posterior tooth amalgam restoration has been redone. (c) A failed composite restoration on a posterior tooth. (d) Posterior tooth composite restoration has been redone.

Mentions: For this study, a CBR tool was developed to identify the most adequate restoration (composite or amalgam) for a given posterior tooth and for monitoring and predicting the longevity of such restorations. Figure 2 presents molars which have been treated with amalgam and composite restorations. The retrieval of similar cases is carried out by a clustering process; following the arrival of a new case, the cluster is retrieved by applying the belonging probability according to the estimated parameters for expectation maximization. Once the cases that correspond to a particular cluster have been retrieved, the associated classifiers are also retrieved and recalculated in case they have not been previously trained. An estimate is then made followed by the remaining steps in the reasoning cycle.


Modelling the longevity of dental restorations by means of a CBR system.

Aliaga IJ, Vera V, De Paz JF, García AE, Mohamad MS - Biomed Res Int (2015)

(a) A failed amalgam restoration on a posterior tooth. (b) Posterior tooth amalgam restoration has been redone. (c) A failed composite restoration on a posterior tooth. (d) Posterior tooth composite restoration has been redone.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4383434&req=5

fig2: (a) A failed amalgam restoration on a posterior tooth. (b) Posterior tooth amalgam restoration has been redone. (c) A failed composite restoration on a posterior tooth. (d) Posterior tooth composite restoration has been redone.
Mentions: For this study, a CBR tool was developed to identify the most adequate restoration (composite or amalgam) for a given posterior tooth and for monitoring and predicting the longevity of such restorations. Figure 2 presents molars which have been treated with amalgam and composite restorations. The retrieval of similar cases is carried out by a clustering process; following the arrival of a new case, the cluster is retrieved by applying the belonging probability according to the estimated parameters for expectation maximization. Once the cases that correspond to a particular cluster have been retrieved, the associated classifiers are also retrieved and recalculated in case they have not been previously trained. An estimate is then made followed by the remaining steps in the reasoning cycle.

Bottom Line: The data will be treated confidentially according to the Organic Law 15/1999 on 13 December on the Protection of Personal Data.This paper also presents a clustering technique capable of identifying the most significant cases with which to instantiate the case-base.In order to classify the cases, a mixture of experts is used which incorporates a Bayesian network and a multilayer perceptron; the combination of both classifiers is performed with a neural network.

View Article: PubMed Central - PubMed

Affiliation: Department of Conservative Dentistry, Complutense University of Madrid, Plaza Ramón y Cajal, s/n, 28040 Madrid, Spain.

ABSTRACT
The lifespan of dental restorations is limited. Longevity depends on the material used and the different characteristics of the dental piece. However, it is not always the case that the best and longest lasting material is used since patients may prefer different treatments according to how noticeable the material is. Over the last 100 years, the most commonly used material has been silver amalgam, which, while very durable, is somewhat aesthetically displeasing. Our study is based on the collection of data from the charts, notes, and radiographic information of restorative treatments performed by Dr. Vera in 1993, the analysis of the information by computer artificial intelligence to determine the most appropriate restoration, and the monitoring of the evolution of the dental restoration. The data will be treated confidentially according to the Organic Law 15/1999 on 13 December on the Protection of Personal Data. This paper also presents a clustering technique capable of identifying the most significant cases with which to instantiate the case-base. In order to classify the cases, a mixture of experts is used which incorporates a Bayesian network and a multilayer perceptron; the combination of both classifiers is performed with a neural network.

Show MeSH