Healthc Inform Res.  2023 Jan;29(1):23-30. 10.4258/hir.2023.29.1.23.

Clinical Decision Support System for Geriatric Dental Treatment Using a Bayesian Network and a Convolutional Neural Network

• Thanathornwong B
• Suebnukarn S
• Ouivirach K

• KMID: 2539395
• DOI: http://doi.org/10.4258/hir.2023.29.1.23

Abstract

Objectives
The aim of this study was to evaluate the performance of a clinical decision support system (CDSS) for therapeutic plans in geriatric dentistry. The information that needs to be considered in a therapeutic plan includes not only the patient’s oral health status obtained from an oral examination, but also other related factors such as underlying diseases, socioeconomic characteristics, and functional dependency.
Methods
A Bayesian network (BN) was used as a framework to construct a model of contributing factors and their causal relationships based on clinical knowledge and data. The faster R-CNN (regional convolutional neural network) algorithm was used to detect oral health status, which was part of the BN structure. The study was conducted using retrospective data from 400 patients receiving geriatric dental care at a university hospital between January 2020 and June 2021.
Results
The model showed an F1-score of 89.31%, precision of 86.69%, and recall of 82.14% for the detection of periodontally compromised teeth. A receiver operating characteristic curve analysis showed that the BN model was highly accurate for recommending therapeutic plans (area under the curve = 0.902). The model performance was compared to that of experts in geriatric dentistry, and the experts and the system strongly agreed on the recommended therapeutic plans (kappa value = 0.905).
Conclusions
This research was the first phase of the development of a CDSS to recommend geriatric dental treatment. The proposed system, when integrated into the clinical workflow, is expected to provide general practitioners with expert-level decision support in geriatric dental care.

Keyword

Deep Learning, Machine Learning, Geriatrics, Dentists, Decision Making

Figure

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  Figure 1 (A) Original panoramic radiograph and (B) the output generated from the faster R-CNN object detection model showing correct predictions of periodontally compromised teeth. R-CNN: regional convolutional neural network.

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  Figure 2 Bayesian network with a conditional probability distribution representing the possible relationships among factors influencing geriatric dental treatment. Each arc indicates a causal relationship.

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  Figure 3 Conditional probability distributions of all nodes after entering the patient status data.

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  Figure 4 Receiver operating characteristic (ROC) curve of the Bayesian network model, showing an area under the curve (AUC) of 0.902.

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  Figure 5 Screenshot of the system user interface showing a recommendation for geriatric dental treatment.

Reference

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