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.

Highlights

  • The longevity of dental restorations is essentially defined by the material used, other contributing factors include the characteristics of the cavity, the patient’s personal habits, and the dentist’s ability [1]

  • We have demonstrated a new technique for identifying important cases, which could be used to construct Case-based reasoning (CBR) systems

  • The developed CBR system was evaluated because it has been operational for 11 years, and on 207 occasions it predicted that the longevity of a given restoration would not last for more than 4 years successfully

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Summary

Introduction

The longevity of dental restorations is essentially defined by the material used, other contributing factors include the characteristics of the cavity, the patient’s personal habits, and the dentist’s ability [1]. An estimate is made followed by the remaining steps in the reasoning cycle This tool guides the practitioners in their work and has proved to be a suitable system for monitoring the state of the art in the field and guiding dentists in the use of different restoration materials. The study of these technological changes could be used to identify the rate of change and the evolution of the complementary restoration techniques: composite and amalgam.

Clustering
Prediction Models
Applying Case-Based Reasoning System to Dental Restoration
1–32 Real number
Results
Conclusions
Full Text
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