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

Abstract

ObjectivesIn this study, a clinical decision support system was developed to help general practitioners assess the need for orthodontic treatment in patients with permanent dentition.MethodsWe 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.ResultsIn 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).ConclusionsThe 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.

Artificial Intelligence in Clinical Decision Support: a Focused Literature Survey

“…Out of the 75 papers we analysed, 26 reported the adoption of knowledge-based AI methods 60 – 85 , possibly in combination with data- driven methods 61 , 63 , 70 , 71 , 74 , 76 . This later group of 6 hybrid approaches will be specifically detailed in the next subsection.…”

“…The knowledge-based methods explicitly represent knowledge as symbols in the form of rules, ontologies, past cases, or other types of knowledge structures. Specifically, the surveyed approaches ranged from the use of ontologies 61 , 62 , 67 – 69 , to rule-based reasoning (including the adoption of fuzzy rules) 64 , 66 – 68 , 72 – 75 , 78 – 80 , 82 – 85 , to the definition of semantic or associative networks 76 , 77 , to temporal reasoning 65 , to probabilistic models such as Bayesian Networks 60 , 63 , 70 , 81 , and to case base reasoning CBR 61 , 71 .…”

“…As regards to the addressed task, most works deal with diagnosis 60 , 62 , 64 , 70 , 73 , 74 , 77 – 80 , 82 , 84 , 85 or therapy/treatment support 61 – 63 , 66 , 75 , 81 , while some works address classification 83 , patient’s behaviour tracking or activity scheduling 65 , and training 68 . A set of papers deals with computer-interpretable guidelines (CIGs) 67 , 69 , 71 , 72 .…”

Clinical Decision Support Functions and Digitalization of Clinical Documents of Electronic Medical Record Systems

“…Functions of CDS directly affect healthcare professionals' decision making [11]. They could help dentists assess patients' needs for orthodontic treatments [12]. According to several studies on the CDS of EMR systems, the CDS functionalities were closely associated with the prevention of medical errors, best practices, and the prevention of medical errors and inappropriate prescriptions [131415].…”

Predicting Facial Biotypes Using Continuous Bayesian Network Classifiers

“…Therefore, given that a Bayesian network satisfies the Markov condition, the joint probability distribution of all the attributes can be computed in a factorized form. Bayesian networks have been applied in the domain of dentistry, for example, a decisionmaking system for the treatment of dental caries [22], the assessment of tooth color changes due to orthodontic treatment [23], the evaluation of the relative role and possible causal relationships among various factors affecting the diagnosis and final treatment outcome of impacted maxillary canines [24], to establish a ranking in efficacy and the best technique for coronally advanced flap-based root coverage procedures [25], a minimally invasive technique for lateral maxillary sinus elevation and to identify the relationship between the involved factors [26], and the development of a clinical decision support system to help general practitioners assess the need for orthodontic treatment in patients with permanent dentition [27].…”