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

ABSTRACT: BACKGROUND:Temporomandibular disorders (TMDs) are pathological conditions affecting the temporomandibular joint and/or masticatory muscles. The current diagnosis of TMDs is complex and multi-factorial, including questionnaires, medical testing and the use of diagnostic methods, such as computed tomography and magnetic resonance imaging. The evaluation, like the mandibular range of motion, needs the experience of the professional in the field and as such, there is a probability of human error when diagnosing TMD. The aim of this study is therefore to develop a method with infrared cameras, using the maximum range of motion of the jaw and four types of classifiers to help professionals to classify the pathologies of the temporomandibular joint (TMJ) and related muscles in a quantitative way, thus helping to diagnose and follow up on TMD. METHODS:Forty individuals were evaluated and diagnosed using the diagnostic criteria for temporomandibular disorders (DC/TMD) scale, and divided into three groups: 20 healthy individuals (control group CG), 10 individuals with myopathies (MG), 10 individuals with arthropathies (AG). A quantitative assessment was carried out by motion capture. The TMJ movement was captured with camera tracking markers mounted on the face and jaw of each individual. Data was exported and analyzed using a custom-made software. The data was used to identify and place each participant into one of three classes using the K-nearest neighbor (KNN), Random Forest, Naïve Bayes and Support Vector Machine algorithms. RESULTS:Significant precision and accuracy (over 90%) was reached by KNN when classifying the three groups. The other methods tested presented lower values of sensitivity and specificity. CONCLUSION:The quantitative TMD classification method proposed herein has significant precision and accuracy over the DC/TMD standards. However, this should not be used as a standalone tool but as an auxiliary method for diagnostic TMDs.

SUBMITTER: Calil BC

PROVIDER: S-EPMC7161015 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature

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

Calil Bruno Coelho BC da Cunha Danilo Vieira DV Vieira Marcus Fraga MF de Oliveira Andrade Adriano A Furtado Daniel Antônio DA Bellomo Junior Douglas Peres DP Pereira Adriano Alves AA

Biomedical engineering online 20200415 1

<h4>Background</h4>Temporomandibular disorders (TMDs) are pathological conditions affecting the temporomandibular joint and/or masticatory muscles. The current diagnosis of TMDs is complex and multi-factorial, including questionnaires, medical testing and the use of diagnostic methods, such as computed tomography and magnetic resonance imaging. The evaluation, like the mandibular range of motion, needs the experience of the professional in the field and as such, there is a probability of human e ...[more]

PMID: 32295597

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Project description:ObjectiveWilliams syndrome is caused by a microdeletion at 7q11.23 and is characterized by a distinctive face, cardiovascular disease, and intellectual disability with a specific cognitive and behavioral profile. This study aims to evaluate the clinical features and obtain important information that can guide early diagnoses and correct follow-up.Materials and methodsThe study included 78 patients whose diagnoses were confirmed by fluorescent in situ hybridization. Facial features, anthropometric measurements, and neurocognitive, endocrine, and urinary system evaluations were obtained from the medical records, and photographs of the patients were evaluated retrospectively.ResultsThe most common complaints at admission were cardiovascular disease and atypical face. The mean age at admission was 39 ± 4.8 months. The mean age of patients presenting with atypical face was 41 ± 5.6 months, while it was 11 ± 3.1 months in patients presenting with cardiovascular disease. Short nose/bulbous nasal type with anteverted nares and periorbital fullness, which are diagnostic facial features, were present in all patients in the infantile/ early childhood period. 80% of the patients had cardiovascular disease; supravalvular aortic stenosis (53.8%) and peripheral pulmonary artery stenosis (41%) were the most common cardiac anomalies.Intel lectu al/de velop menta l disability was present in 75.6% of the patients. Behavioral disorders including autism spectrum disorder and attention deficit hyperactivity disorder were detected in 50% of our patients. Hypersensitivity to loud and/or sudden sounds was present in all patients.ConclusionWe highlighted that recognition of facial findings is important for early diagnosis, especially in patients without cardiovascular disease. The frequency of cardiovascular, endocrinological, renal anomalies, and intellectual disab ility /deve lopme ntal delay was described that provide valuable information in the follow-up of patients.

Dataset Information

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

Publications

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

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