Bayesian-Based Decision Support System for Assessing the Needs for Orthodontic Treatment

Bhornsawan Thanathornwong, Bhornsawan Thanathornwong

Abstract

Objectives: In this study, a clinical decision support system was developed to help general practitioners assess the need for orthodontic treatment in patients with permanent dentition.

Methods: We chose a Bayesian network (BN) as the underlying model for assessing the need for orthodontic treatment. One thousand permanent dentition patient data sets chosen from a hospital record system were prepared in which one data element represented one participant with information for all variables and their stated need for orthodontic treatment. To evaluate the system, we compared the assessment results based on the judgements of two orthodontists to those recommended by the decision support system.

Results: In a BN decision support model, each variable is modelled as a node, and the causal relationship between two variables may be represented as a directed arc. For each node, a conditional probability table is supplied that represents the probabilities of each value of this node, given the conditions of its parents. There was a high degree of agreement between the two orthodontists (kappa value = 0.894) in their diagnoses and their judgements regarding the need for orthodontic treatment. Also, there was a high degree of agreement between the decision support system and orthodontists A (kappa value = 1.00) and B (kappa value = 0.894).

Conclusions: The study was the first testing phase in which the results generated by the proposed system were compared with those suggested by expert orthodontists. The system delivered promising results; it showed a high degree of accuracy in classifying patients into groups needing and not needing orthodontic treatment.

Keywords: Angle's Classification; Artificial Intelligence; Dental Informatics; Machine Learning; Malocclusion.

Conflict of interest statement

Conflict of Interest: No potential conflict of interest relevant to this article was reported.

Figures

Figure 1. Bayesian network representing the possible…
Figure 1. Bayesian network representing the possible relationships among factors that influence orthodontic treatment needs. Each arc indicates a causal relationship. Each node contains conditional probability distributions that were learned from the data.
Figure 2. Screen shot of the system…
Figure 2. Screen shot of the system user interface showing the prediction of need for orthodontic treatment.

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