Identification of arthropathy and myopathy of the temporomandibular syndrome by biomechanical facial features

Assessment of Mandibular Kinematic Variables using a Motion Analysis System and a Regular Mobile Phone

Background

The development of a standard motion capture system is needed since the measurement of temporomandibular disorders is time-consuming and costly using laboratory tools.

Objective

The current study aimed to investigate the mandibular kinematic variables using a regular mobile phone and the motion analysis system.

Material and Methods

In this quasi-laboratory and comparative study, ten healthy individuals participated, and three mobile cameras, nine red markers, and Kinovea software were also used to investigate the mandibular kinematic variables. Nine light reflective markers were used to check the sensitivity, accuracy, and reliability of the proposed system. The motion was analyzed using seven motion analysis infrared cameras and Qualisys Track Manager (QTM) software. Two other raters analyzed the kinematic variables obtained from the mobile to measure intra- and inter-rater reliability.

Results

Pearson's correlation coefficient was obtained at 0.98, 0.75, 0.98, and 0.96, showing a high correlation. The accuracy and reliability validation tests showed an average error and an accuracy of 0.156 mm and 95%, respectively, with a mobile phone. The Intra Class Correlation coefficient showed a high internal correlation in the mentioned confidence interval (0.98 and 0.81, and 0.96 and 0.97). The intraclass correlation coefficient method also showed 97% inter-raster reliability.

Conclusion

Mobile phones as a new system can evaluate the kinematic variables of mandibular disorders with appropriate accuracy and reliability.

Kinematic classification of mandibular movements in patients with temporomandibular disorders based on PCA

This retrospective study aimed to kinematically classify mandibular movements collected during Temporomandibular Disorders (TMD) treatment, employing Fourier transformation (FT), Principal Component Analysis (PCA), and K-means clustering (k-means), and to investigate their correlation with symptoms of pain-related TMD. The study included five TMD participants diagnosed with myalgia (age: 39-86 years, with an SD of 18.96) and three healthy participants (age: 32-42 years, with an SD of 5.13) with no stomatognathic problems. TMD participants underwent tailored treatment for their symptoms, and their maximum unassisted mouth opening (MMO) was recorded randomly with a motion capture system (ARCUS digma II, Kavo, Biberach, Germany) at multiple time points. MMO for healthy participants served as a control. The dataset comprising 28 trials, was transferred to the AnyBody Modeling System (AnyBody Technology, Aalborg, Denmark) to extract joint angle time series, which were then transformed into Fourier series. Subsequently, PCA and k-means clustering were conducted. Two clusters were identified: Cluster 1, predominantly composed of symptomatic trials, and Cluster 2, mainly consisting of asymptomatic trials. Distinct transition pathways between the clusters were observed among participants, corresponding to the alleviation of pain-related symptoms during TMD treatment. These findings suggest that this approach has potential as an effective tool for diagnosing and assessing TMD by identifying symptomatic kinematic patterns and tracking temporal changes in mandibular movement. Despite the small dataset, these results suggest promise for a novel functional assessment method for TMD based on kinematic features.

Clinical machine learning in parafunctional and altered functional occlusion: A systematic review

STATEMENT OF PROBLEM

The advent of machine learning in the complex subject of occlusal rehabilitation warrants a thorough investigation into the techniques applied for successful clinical translation of computer automation. A systematic evaluation on the topic with subsequent discussion of the clinical variables involved is lacking.

PURPOSE

The purpose of this study was to systematically critique the digital methods and techniques used to deploy automated diagnostic tools in the clinical evaluation of altered functional and parafunctional occlusion.

MATERIAL AND METHODS

Articles were screened by 2 reviewers in mid-2022 according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Eligible articles were critically appraised by using the Joanna Briggs Institute's Diagnostic Test Accuracy (JBI-DTA) protocol and Minimum Information for Clinical Artificial Intelligence Modeling (MI-CLAIM) checklist.

RESULTS

Sixteen articles were extracted. Variations in mandibular anatomic landmarks obtained via radiographs and photographs produced notable errors in prediction accuracy. While half of the studies adhered to robust methods of computer science, the lack of blinding to a reference standard and convenient exclusion of data in favor of accurate machine learning suggested that conventional diagnostic test methods were ineffective in regulating machine learning research in clinical occlusion. As preestablished baselines or criterion standards were lacking for model evaluation, a heavy reliance was placed on the validation provided by clinicians, often dental specialists, which was prone to subjective biases and largely governed by professional experience.

CONCLUSIONS

Based on the findings and because of the numerous clinical variables and inconsistencies, the current literature on dental machine learning presented nondefinitive but promising results in diagnosing functional and parafunctional occlusal parameters.

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

OBJECTIVES

Advancements 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.

RESULTS

Debates 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.

CONCLUSIONS

Normal 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.

Using machine learning to classify temporomandibular disorders: a proof of concept

BACKGROUND

the escalating influx of patients with temporomandibular disorders and the challenges associated with accurate diagnosis by non-specialized dental practitioners underscore the integration of artificial intelligence into the diagnostic process of temporomandibular disorders (TMD) as a potential solution to mitigate diagnostic disparities associated with this condition.

OBJECTIVES

In this study, we evaluated a machine-learning model for classifying TMDs based on the International Classification of Orofacial Pain, using structured data.

METHODOLOGY

Model construction was performed by the exploration of a dataset comprising patient records from the repository of the Multidisciplinary Orofacial Pain Center (CEMDOR) affiliated with the Federal University of Santa Catarina. Diagnoses of TMD were categorized following the principles established by the International Classification of Orofacial Pain (ICOP-1). Two independent experiments were conducted using the decision tree technique to classify muscular or articular conditions. Both experiments uniformly adopted identical metrics to assess the developed model's performance and efficacy.

RESULTS

The classification model for joint pain showed a relevant potential for general practitioners, presenting 84% accuracy and f1-score of 0.85. Thus, myofascial pain was classified with 78% accuracy and an f1-score of 0.76. Both models used from 2 to 5 clinical variables to classify orofacial pain.

CONCLUSION

The use of decision tree-based machine learning holds significant support potential for TMD classification.

Artificial intelligence applications in dentistry: A bibliometric review with an emphasis on computational research trends within the field

BACKGROUND

The aim of this study was to understand the trends regarding the use of artificial intelligence in dentistry through a bibliometric review.

TYPES OF STUDIES REVIEWED

The authors performed a literature search on Web of Science. They collected the following data: articles-number and density of citations, year, key words, language, document type, study design, and theme (main objective, diagnostic method, and specialties); journals-impact factor; authors-country, continent, and institution. The authors used Visualization of Similarities Viewer software (Leiden University) to analyze the data and Spearman test for correlation analysis.

RESULTS

After selection, 1,478 articles were included. The number of citations ranged from 0 through 327. The articles were published from 1984 through 2024. Most articles were characterized as proof of concept (979). Definition and classification of structures and diseases was the most common theme (550 articles). There was an emphasis on radiology (333 articles) and radiographic-based diagnostic methods (715 articles). China was the country with the most articles (251), and Asia was the continent with the most articles (871). The Charité-University of Medicine Berlin was the institution with the most articles (42), and the author with the most articles was Schwendicke (53).

PRACTICAL IMPLICATIONS

Artificial intelligence is an important clinical tool to facilitate diagnosis and provide automation in various processes.

Machine learning-based medical imaging diagnosis in patients with temporomandibular disorders: a diagnostic test accuracy systematic review and meta-analysis

OBJECTIVES

Temporomandibular disorders (TMDs) are the second most common musculoskeletal condition which are challenging tasks for most clinicians. Recent research used machine learning (ML) algorithms to diagnose TMDs intelligently. This study aimed to systematically evaluate the quality of these studies and assess the diagnostic accuracy of existing models.

MATERIALS AND METHODS

Twelve databases (Europe PMC, Embase, etc.) and two registers were searched for published and unpublished studies using ML algorithms on medical images. Two reviewers extracted the characteristics of studies and assessed the methodological quality using the QUADAS-2 tool independently.

RESULTS

A total of 28 studies (29 reports) were included: one was at unclear risk of bias and the others were at high risk. Thus the certainty of evidence was quite low. These studies used many types of algorithms including 8 machine learning models (logistic regression, support vector machine, random forest, etc.) and 15 deep learning models (Resnet152, Yolo v5, Inception V3, etc.). The diagnostic accuracy of a few models was relatively satisfactory. The pooled sensitivity and specificity were 0.745 (0.660-0.814) and 0.770 (0.700-0.828) in random forest, 0.765 (0.686-0.829) and 0.766 (0.688-0.830) in XGBoost, and 0.781 (0.704-0.843) and 0.781 (0.704-0.843) in LightGBM.

CONCLUSIONS

Most studies had high risks of bias in Patient Selection and Index Test. Some algorithms are relatively satisfactory and might be promising in intelligent diagnosis. Overall, more high-quality studies and more types of algorithms should be conducted in the future.

CLINICAL RELEVANCE

We evaluated the diagnostic accuracy of the existing models and provided clinicians with much advice about the selection of algorithms. This study stated the promising orientation of future research, and we believe it will promote the intelligent diagnosis of TMDs.

Diagnosis of temporomandibular disorders using artificial intelligence technologies: A systematic review and meta-analysis

Background

Artificial intelligence (AI) algorithms have been applied to diagnose temporomandibular disorders (TMDs). However, studies have used different patient selection criteria, disease subtypes, input data, and outcome measures. Resultantly, the performance of the AI models varies.

Objective

This study aimed to systematically summarize the current literature on the application of AI technologies for diagnosis of different TMD subtypes, evaluate the quality of these studies, and assess the diagnostic accuracy of existing AI models.

Materials and methods

The study protocol was carried out based on the preferred reporting items for systematic review and meta-analysis protocols (PRISMA). The PubMed, Embase, and Web of Science databases were searched to find relevant articles from database inception to June 2022. Studies that used AI algorithms to diagnose at least one subtype of TMD and those that assessed the performance of AI algorithms were included. We excluded studies on orofacial pain that were not directly related to the TMD, such as studies on atypical facial pain and neuropathic pain, editorials, book chapters, and excerpts without detailed empirical data. The risk of bias was assessed using the QUADAS-2 tool. We used Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) to provide certainty of evidence.

Results

A total of 17 articles for automated diagnosis of masticatory muscle disorders, TMJ osteoarthrosis, internal derangement, and disc perforation were included; they were retrospective studies, case-control studies, cohort studies, and a pilot study. Seven studies were subjected to a meta-analysis for diagnostic accuracy. According to the GRADE, the certainty of evidence was very low. The performance of the AI models had accuracy and specificity ranging from 84% to 99.9% and 73% to 100%, respectively. The pooled accuracy was 0.91 (95% CI 0.76–0.99), I² = 97% (95% CI 0.96–0.98), p < 0.001.

Conclusions

Various AI algorithms developed for diagnosing TMDs may provide additional clinical expertise to increase diagnostic accuracy. However, it should be noted that a high risk of bias was present in the included studies. Also, certainty of evidence was very low. Future research of higher quality is strongly recommended.

Discrimination between healthy and patients with Parkinson's disease from hand resting activity using inertial measurement unit

BACKGROUND

Parkinson's disease (PD) is a neurological disease that affects the motor system. The associated motor symptoms are muscle rigidity or stiffness, bradykinesia, tremors, and gait disturbances. The correct diagnosis, especially in the initial stages, is fundamental to the life quality of the individual with PD. However, the methods used for diagnosis of PD are still based on subjective criteria. As a result, the objective of this study is the proposal of a method for the discrimination of individuals with PD (in the initial stages of the disease) from healthy groups, based on the inertial sensor recordings.

METHODS

A total of 27 participants were selected, 15 individuals previously diagnosed with PD and 12 healthy individuals. The data collection was performed using inertial sensors (positioned on the back of the hand and on the back of the forearm). Different numbers of features were used to compare the values of sensitivity, specificity, precision, and accuracy of the classifiers. For group classification, 4 classifiers were used and compared, those being [Random Forest (RF), Support Vector Machine (SVM), K-Nearest Neighbor (KNN), and Naive Bayes (NB)].

RESULTS

When all individuals with PD were analyzed, the best performance for sensitivity and accuracy (0.875 and 0.800, respectively) was found in the SVM classifier, fed with 20% and 10% of the features, respectively, while the best performance for specificity and precision (0.933 and 0.917, respectively) was associated with the RF classifier fed with 20% of all the features. When only individuals with PD and score 1 on the Hoehn and Yahr scale (HY) were analyzed, the best performances for sensitivity, precision and accuracy (0.933, 0.778 and 0.848, respectively) were from the SVM classifier, fed with 40% of all features, and the best result for precision (0.800) was connected to the NB classifier, fed with 20% of all features.

CONCLUSION

Through an analysis of all individuals in this study with PD, the best classifier for the detection of PD (sensitivity) was the SVM fed with 20% of the features and the best classifier for ruling out PD (specificity) was the RF classifier fed with 20% of the features. When analyzing individuals with PD and score HY = 1, the SVM classifier was superior across the sensitivity, precision, and accuracy, and the NB classifier was superior in the specificity. The obtained result indicates that objective methods can be applied to help in the evaluation of PD.