In vitro assessment of tooth color changes due to orthodontic treatment using knowledge discovery methods

Esthetic dentistry imposes several demands on the artistic abilities of the dentist, and knowledge of the underlying scientific principles of tooth color is considered to be essential by Sikri. The supervised classification methods, such as the artificial neural networks, the support vector machines, and also the Bayesian classifier, and the feature selection methods, such as decision trees, genetic algorithms and neural networks, as well as independent component analysis combined with least square support vector machines, were applied successfully in the medical field but were less implemented in the dental analysis domain. This study was conducted on extracted premolars from people who required orthodontic treatment. Data gathering was done using spectrophotometric recordings of tooth color parameters before and after accelerated bleaching, staining, and control procedures on extracted teeth on which was simulated orthodontic treatment. Comparison between data mining techniques and classical statistical interpretation of data was done. The results demonstrated the usefulness of these innovating data assessment techniques in the dental field.