Enhancing Periodontal Treatment Through the Integration of Deep Learning-Based Detection with Bayesian Network Models.

This study incorporated deep learning for periodontal disease detection into a Bayesian network (BN) clinical decision support model for comprehensive periodontal care. BN structure and probabilities were based on clinical data and Faster R-CNN-detected radiographic images. Receiver operating characteristic curve analysis confirmed the model's high accuracy in treatment plan recommendations.

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

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.

Decision support system for predicting color change after tooth whitening.

Tooth whitening is becoming increasingly popular among patients and dentists since it is a relatively noninvasive approach. However, the degree of color change after tooth whitening is known to vary substantially between studies. The present study aims to develop a clinical decision support system for predicting color change after in-office tooth whitening.

A system for predicting musculoskeletal disorders among dental students.

Objective: This study aimed to develop a system for predicting work-related musculoskeletal disorders (WMSD) among dental students.

Materials And Methods: The system comprised 2 accelerometer sensors to register neck and upper back postures and movements, and software developed to collect and process the data. Hidden Markov models (HMMs) were used to predict the likelihood of WMSD in dental students by comparing their neck and upper back movement patterns with WMSD and non-WMSD HMMs learned from previous data.

A system for predicting and preventing work-related musculoskeletal disorders among dentists.

Work-related musculoskeletal disorders (WMSDs) have become increasingly common among dentists and initiate a series of events that could result in a career ending. This study aims to construct a system for predicting and preventing WMSD among dentists. We used Bayesian network (BN) that describes the mutual relationships among multiple variables contributing to WMSDs.