Sonographic Evaluation of Masseter Muscle Thickness in Bruxist and Non - Bruxist Subjects

Najm, Areej A.

doi:10.12816/0015224

Cited by 7 publications

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“…TNC indices in healthy individuals were 3.4 ± 0.95; in patients with SB (n = 100) -50. The analysis of the masseter muscle thickness was conducted based on the gender of the patients, because it was established that the differences of these indices are statistically significant between men and women [18,19]. During the state of relaxation, the masseter muscle thickness was 9.75 ± 0.27 mm for women under 35 and of 9.82 ± 0.26 mm for older women (p > 0.05).…”

Section: Resultsmentioning

confidence: 99%

“…It is used for five consecutive nights in order to assess the average values of the motor indices: total clench time (TCT) during sleep and their number (TNC -total number of clenches). We have determined the thickness of the masseter muscle following the recommendations in the scientific literature [18,19] by applying the Envisor C (Philips, Japan) equipment and a 7.5 MHz linear transducer. The masseter muscle thickness gradient (MTG) was calculated according to the formula:…”

Section: Methodsmentioning

confidence: 99%

“…With aging, the microstructure of the masticatory muscles essentially changes; signs of osteoporosis that are associated with the changes in the masticatory Original Article muscles occur [17]. The thickness and hypertrophy of the masticatory muscles also depend on the age of the patients [18,19,20]. Based on the age factor, the degree of severity is also various for the manifestations of sleep bruxism.…”

Section: Introductionmentioning

confidence: 99%

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Manifestation of Sleep Bruxism According to the Age of Patients

Romaniuc¹,

Fala²,

Lacusta³

et al. 2018

EDUJ

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PhD, Univ. Prof., Academician d Neurology residentIntroduction: Bruxism is a widespread condition, affecting up to 85-90% of the general population, and in 5% of these individuals, the grinding evolves into a clinical condition. In many cases, sleep bruxism is latent, the disorder may be identified by its consequences (dental wear, etc.). This ascertains the need to develop diagnostic methods that may be able to identify bruxism at the initial stages of the disorder. Aim of the study: to determine the peculiarities of the clinical manifestation of primary sleep bruxism (SB) based on the age of the patients. Methodology: One hundred patients with primary SB (70 patients aged between 18-35 and 30 patients aged between 35-50) were investigated. The clinical features of SB (algic syndrome, myogenic disorders, temporomandibular disorders, dental wear, psychoemotional disorders, sleep quality alterations) were also investigated. Results: The expression of emotional stress in patients of various ages was almost identical. The highest clenching frequency and duration, as well as a higher clinical expression of sleep bruxism was observed in patients under 35. The components of the algic syndrome show a varied expression for different age groups -patients under 35 had more severe disorders of the nocturnal episodes and myogenic-spastic events; older patients (35-50) were characterized by a prevalence of local and diffuse myogenic structural disorders, arthrogenic pain and severe sleep disturbances. Conclusion: Electromyography and ultrasonography can reveal important morphofunctional peculiarities of the masticatory muscles that are associated with the severity of SB in patients from different age groups.

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Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Manifestation of Sleep Bruxism According to the Age of Patients

Romaniuc¹,

Fala²,

Lacusta³

et al. 2018

EDUJ

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“…When the measurements of ultrasonic thickness of masseter muscles between patients with bruxism and healthy people are compared, its thickness in people with bruxism was found to be higher than the healthy subjects. 19,20 In recent years, elastography has gradually been used for examining the mechanical properties of musculoskeletal tissues. This technique takes advantage of changing soft tissue elasticity in different pathologies to provide qualitative and quantitative information that can be used for diagnostic purposes and follow-up evaluations during rehabilitation.…”

Section: Discussionmentioning

confidence: 99%

An Artificial Intelligence Hypothetical Approach for Masseter Muscle Segmentation on Ultrasonography in Patients With Bruxism

Orhan

Yazıcı

Kolsuz

et al. 2021

Journal of Advanced Oral Research

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Aim: The present study is aimed to assess the segmentation success of an artificial intelligence (AI) system based on the deep convolutional neural network (D-CNN) method for the segmentation of masseter muscles on ultrasonography (USG) images. Materials and Methods: This retrospective study was carried out by using the radiology archive of the Department of Oral and Maxillofacial Radiology of the Faculty of Dentistry in Ankara University. A total of 195 anonymized USG images were used in this retrospective study. The deep learning process was performed using U-net, Pyramid Scene Parsing Network (PSPNet), and Fuzzy Petri Net (FPN) architectures. Muscle thickness was assessed using USG by manual segmentation and measurements using USG’s software. The neural network model (CranioCatch, Eskisehir-Turkey) was then used to determine the muscles, following automatic measurements of the muscles. Accuracy, ROC area under the curve (AUC), and Precision-Recall Curves (PRC) AUC were calculated in the test dataset and compare a human observer and the AI model. Manual segmentation and measurements were compared statistically with AI ( P < .05). The Mann–Whitney U test was used to analyze whether there is a statistically significant difference between the predicted values and the actual values. Results: The AI models detected and segmented all test muscle data for FPN and U-net, while only two cases of muscles were not detected by PSPNet (false negatives). Accuracies of FPN, PSPNet, and U-net were estimated as 0.985, 0.947, and 0.969, respectively. Receiver operating characteristic scores of FPN, PSPNet, and U-net were estimated as 0.977, 0.934, and 0.969, respectively. The D-CNN measurements of the muscles were similar to manual measurements. There was no significant difference between the two measurement methods in three groups ( P > .05). Conclusion: The proposed AI system approach for the analysis of USG images seems to be promising for automatic masseter muscle segmentation and measurement of thickness. This method can help surgeons, radiologists, and other professionals such as physical therapists in evaluating the time correctly and saving time for diagnosis.

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Ultrasonographic Assessment of Masseter and Anterior Temporal Muscle Thickness and Internal Structure in Young Adult Patients With Bruxism

Aslan,

Artaş

2024

J of Clinical Ultrasound

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ObjectiveThe current study is a comparative cross‐sectional analysis study to determine the thickness and internal structure of the masseter muscle (MM) and anterior temporal muscle (ATM) in patients with bruxism by ultrasonography (USG).Materials and MethodsA total of 100 patients (36 males, 64 females), 50 with bruxism and 50 without bruxism, aged 20–30 years were included in the study. All patients were investigated with MM and ATM USG. The thickness of the muscles was measured at rest and during clenching and the internal structure at rest was classified as Type I, II, and III. Differences in the internal structure between bruxism and non‐bruxism groups were determined using the Chi‐square test (p < 0.05).ResultsThe most common internal structure in bruxism was Type 2 in MM (74%) and Type 1 in ATM (46%), whereas in non‐bruxism, Type 1 in MM (58%) and Type 1 in ATM (80%) were found most frequently. While there was a statistically significant difference in the internal echogenic pattern in the right MM and ATM in bruxism (p < 0.05), no significant difference was observed on the left side (p > 0.05). Bruxism patients had higher rest and clench thicknesses than non‐bruxism patients on both sides in MM and ATM, however, this did not result in a statistically significant difference (p > 0.05).ConclusionsThe results obtained showed that the evaluation of internal structure and thickness differences via USG is crucial in understanding the nature of the bruxism process affecting the masseter and ATM, facilitating its clinical diagnosis and leading to its treatment.

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Sonographic Evaluation of Masseter Muscle Thickness in Bruxist and Non - Bruxist Subjects

Cited by 7 publications

References 0 publications

Manifestation of Sleep Bruxism According to the Age of Patients

Manifestation of Sleep Bruxism According to the Age of Patients

An Artificial Intelligence Hypothetical Approach for Masseter Muscle Segmentation on Ultrasonography in Patients With Bruxism

Ultrasonographic Assessment of Masseter and Anterior Temporal Muscle Thickness and Internal Structure in Young Adult Patients With Bruxism

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