A comparison of a handheld minicomputer and an external graphics processing unit in performing 3D intraoral scans

Farook, Taseef Hasan; Dudley, James

doi:10.1016/j.prosdent.2024.03.028

Cited by 2 publications

(1 citation statement)

References 18 publications

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“…A pilot evaluation assessed the hardware used and operator-induced biases, revealing that neither the choice of hardware nor the clinical experience of the operator influenced machine predictions 20 . It was also found that mobile devices were capable of capturing data comparable to standard workstations 21 meaning medical devices and graphics processing units connected through highspeed terminals such as USB-4 and Oculink are capable of generating accurate sampling data without signal loss. CAD software (Meshmixer; Autodesk Inc.) measured inter-canine and intermolar distances, arch perimeters, overjet, and overbite.…”

Section: Methodsmentioning

confidence: 99%

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

Farook,

Haq,

Ramees

et al. 2024

Sci Rep

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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. Six models were trained on synthetic data ranging from 500 to 25,000 observations, with 65 original observations reserved for testing: Linear Regression, Random Forest, CatBoost Regressor, XGBoost Regressor, Multilayer Perceptron Neural Network (MPNN), and TabNet. Explainable AI indicated that key predictors of freeway space included phonetics, resting temporalis muscle activity, mandibular muscle activity during clenching, body weight, mandibular hard tissue lateral displacements, and dental arch parameters. CatBoost excelled with a test error of 0.65 mm using 5000 synthetic data points, while a refined MPNN achieved the best performance with 25,000 synthetic data points and 121 unique predictors, yielding an absolute error of 0.43 mm on the 65 original observations.

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Section: Methodsmentioning

confidence: 99%

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

Farook,

Haq,

Ramees

et al. 2024

Sci Rep

View full text Add to dashboard Cite

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Understanding Occlusion and Temporomandibular Joint Function Using Deep Learning and Predictive Modeling

Farook,

Dudley

2024

Clinical & Exp Dental Res

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ObjectivesAdvancements in artificial intelligence (AI)‐driven predictive modeling in dentistry are outpacing the clinical translation of research findings. Predictive modeling uses statistical methods to anticipate norms related to TMJ dynamics, complementing imaging modalities like cone beam computed tomography (CBCT) and magnetic resonance imaging (MRI). Deep learning, a subset of AI, helps quantify and analyze complex hierarchical relationships in occlusion and TMJ function. This narrative review explores the application of predictive modeling and deep learning to identify clinical trends and associations related to occlusion and TMJ function.ResultsDebates persist regarding best practices for managing occlusal factors in temporomandibular joint (TMJ) function analysis while interpreting and quantifying findings related to the TMJ and occlusion and mitigating biases remain challenging. Data generated from noninvasive chairside tools such as jaw trackers, video tracking, and 3D scanners with virtual articulators offer unique insights by predicting variations in dynamic jaw movement, TMJ, and occlusion. The predictions help us understand the highly individualized norms surrounding TMJ function that are often required to address temporomandibular disorders (TMDs) in general practice.ConclusionsNormal TMJ function, occlusion, and the appropriate management of TMDs are complex and continue to attract ongoing debate. This review examines how predictive modeling and artificial intelligence aid in understanding occlusion and TMJ function and provides insights into complex dental conditions such as TMDs that may improve diagnosis and treatment outcomes with noninvasive techniques.

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A comparison of a handheld minicomputer and an external graphics processing unit in performing 3D intraoral scans

Cited by 2 publications

References 18 publications

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

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

Understanding Occlusion and Temporomandibular Joint Function Using Deep Learning and Predictive Modeling

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