Clinical Decision Support System for Geriatric Dental Treatment Using a Bayesian Network and a Convolutional Neural Network
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Objectives: The aim of this study was to evaluate the performance of a clinical decision support system (CDSS) for therapeuticplans in geriatric dentistry. The information that needs to be considered in a therapeutic plan includes not only thepatient’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 frameworkto construct a model of contributing factors and their causal relationships based on clinical knowledge and data. The fasterR-CNN (regional convolutional neural network) algorithm was used to detect oral health status, which was part of the BNstructure. The study was conducted using retrospective data from 400 patients receiving geriatric dental care at a universityhospital between January 2020 and June 2021. Results: The model showed an F1-score of 89.31%, precision of 86.69%, andrecall of 82.14% for the detection of periodontally compromised teeth. A receiver operating characteristic curve analysisshowed that the BN model was highly accurate for recommending therapeutic plans (area under the curve = 0.902). Themodel performance was compared to that of experts in geriatric dentistry, and the experts and the system strongly agreed onthe recommended therapeutic plans (kappa value = 0.905). Conclusions: This research was the first phase of the developmentof a CDSS to recommend geriatric dental treatment. The proposed system, when integrated into the clinical workflow, is expectedto provide general practitioners with expert-level decision support in geriatric dental care.
Abstract
Objectives: The aim of this study was to evaluate the performance of a clinical decision support system (CDSS) for therapeuticplans in geriatric dentistry. The information that needs to be considered in a therapeutic plan includes not only thepatient’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 frameworkto construct a model of contributing factors and their causal relationships based on clinical knowledge and data. The fasterR-CNN (regional convolutional neural network) algorithm was used to detect oral health status, which was part of the BNstructure. The study was conducted using retrospective data from 400 patients receiving geriatric dental care at a universityhospital between January 2020 and June 2021. Results: The model showed an F1-score of 89.31%, precision of 86.69%, andrecall of 82.14% for the detection of periodontally compromised teeth. A receiver operating characteristic curve analysisshowed that the BN model was highly accurate for recommending therapeutic plans (area under the curve = 0.902). Themodel performance was compared to that of experts in geriatric dentistry, and the experts and the system strongly agreed onthe recommended therapeutic plans (kappa value = 0.905). Conclusions: This research was the first phase of the developmentof a CDSS to recommend geriatric dental treatment. The proposed system, when integrated into the clinical workflow, is expectedto provide general practitioners with expert-level decision support in geriatric dental care.
참고문헌 (21)
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