Relationship between anterior occlusion, arch dimension, and mandibular movement during speech articulation: A three-dimensional analysis.

Statement Of Problem: Studies correlating occlusal morphology from 3-dimensional intraoral scans with both soft and hard tissue dynamic landmark tracking within the same participant population are lacking.

Purpose: The purpose of this clinical study was to use 3-dimensional intraoral scanning, computer-aided design, electrognathography, and artificial intelligence to investigate the relationships between anterior occlusion and arch parameters with hard and soft tissue displacements during speech production.

Material And Methods: An artificial intelligence (AI) driven software program and electrognathography was used to record the phonetic activities in 62 participants for soft tissue (ST) and hard tissue (HT) displacement.

Predictive modelling of freeway space utilising clinical history, normalised muscle activity, dental occlusion, and mandibular movement analysis.

This study aimed to predict dental freeway space by examining the clinical history, habits, occlusal parameters, mandibular hard tissue movement, soft tissue motion, muscle activity, and temporomandibular joint function of 66 participants. Data collection involved video-based facial landmark tracking, mandibular electrognathography, surface electromyography of mandibular range of motion, freeway space, chewing tasks, phonetic expressions, joint vibration analysis, and 3D jaw scans of occlusion. This resulted in a dataset of 121 predictor features, with freeway space as the target variable.

Predicting masticatory muscle activity and deviations in mouth opening from non-invasive temporomandibular joint complex functional analyses.

Background: A quantitative approach to predict expected muscle activity and mandibular movement from non-invasive hard tissue assessments remains unexplored.

Objectives: This study investigated the predictive potential of normalised muscle activity during various jaw movements combined with temporomandibular joint (TMJ) vibration analyses to predict expected maximum lateral deviation during mouth opening.

Method: Sixty-six participants underwent electrognathography (EGN), surface electromyography (EMG) and joint vibration analyses (JVA).

Deep learning and predictive modelling for generating normalised muscle function parameters from signal images of mandibular electromyography.

Challenges arise in accessing archived signal outputs due to proprietary software limitations. There is a notable lack of exploration in open-source mandibular EMG signal conversion for continuous access and analysis, hindering tasks such as pattern recognition and predictive modelling for temporomandibular joint complex function. To Develop a workflow to extract normalised signal parameters from images of mandibular muscle EMG and identify optimal clustering methods for quantifying signal intensity and activity durations.