Diagnosis Of Temporomandibular Joint Disorders Research Articles

Research and implementation of intelligent diagnostic system for temporomandibular joint disorder

Temporomandibular joint disorder (TMD) is a common oral and maxillofacial disease, which is difficult to detect due to its subtle early symptoms. In this study, a TMD intelligent diagnostic system implemented on edge computing devices was proposed, which can achieve rapid detection of TMD in clinical diagnosis and facilitate its early-stage clinical intervention. The proposed system first automatically segments the important components of the temporomandibular joint, followed by quantitative measurement of the joint gap area, and finally predicts the existence of TMD according to the measurements. In terms of segmentation, this study employs semi-supervised learning to achieve the accurate segmentation of temporomandibular joint, with an average Dice coefficient (DC) of 0.846. A 3D region extraction algorithm for the temporomandibular joint gap area is also developed, based on which an automatic TMD diagnosis model is proposed, with an accuracy of 83.87%. In summary, the intelligent TMD diagnosis system developed in this paper can be deployed at edge computing devices within a local area network, which is able to achieve rapid detecting and intelligent diagnosis of TMD with privacy guarantee.

Biomechanical behavior of temporomandibular joint movements driven by mastication muscles.

Surgery of jawbones has a high potential risk of causing complications associated with temporomandibular joint disorder (TMD). The objective of this study was to investigate the effects of two drive modeling methods on the biomechanical behavior of the temporomandibular joint (TMJ) including articular disc during mandibular movements. A finite element (FE) model from a healthy human computed tomography was used to evaluate TMJ dynamic using two methods, namely, a conventional spatial-oriented method (displacement-driven) and a compliant muscle-initiated method (masticatory muscle-driven). The same virtual FE model was 3D printed and a custom designed experimental platform was established to validate the accuracy of experimental and theoretical results of the TMJ biomechanics during mandibular movements. The results show that stress distributed to TMJ and articular disc from mandibular movements provided better representation from the muscle-driving approach than those of the displacement-driven modeling. The simulation and experimental data exhibited significant strong correlations during opening, protrusion, and laterotrusion (with canonical correlation coefficients of 0.994, 0.993, and 0.932, respectively). The use of muscle-driven modeling holds promise for more accurate forecasting of stress analysis of TMJ and articular disc during mandibular movements. The compliant approach to analyze TMJ dynamics would potentially contribute to clinic diagnosis and prediction of TMD resulting from occlusal disease and jawbone surgery such as orthognathic surgery or tumor resection.

Effectiveness of ultrasonography in the diagnosis of temporomandibular joint disorders: A systematic review and meta-analysis.

Temporomandibular disorders (TMDs) pose diagnostic challenges, and selecting appropriate imaging modalities is crucial for accurate assessment. This study aimed to compare the diagnostic accuracy and efficacy of ultrasonography (US) and magnetic resonance imaging (MRI) in identifying TMDs. A comprehensive meta-analysis was conducted, including studies that compared US and MRI for TMJ disorder assessments. Fixed-effects models were utilized to calculate pooled odds ratios (ORs) and relative risks (RRs) with 95% confidence intervals (CIs). Heterogeneity was assessed using the chi-squared test and I2 statistic. Newcastle-Ottawa scale was used to assess the methodological quality of the studies included. Six studies were included, involving a total of 281 participants. The meta-analysis demonstrated that MRI was statistically somewhat better than US in identifying TMJ disorders. The summary OR was 0.64 (95% CI: 0.46-0.90), and the summary RR was 0.80 (95% CI: 0.68-0.95). Heterogeneity among the studies was low (χ2 = 2.73, df = 5, p = .74; I2 = 0%). Demographic variables revealed variations in sample size, gender ratio and mean age across the studies. This meta-analysis provides evidence that MRI may be more effective than US in diagnosing TMDs. However, the study is limited by the small number of included studies and variations in demographic variables and study designs. Future research with larger samples and standardised protocols is essential to confirm and strengthen these findings. Understanding the diagnostic accuracy of MRI and US for TMJ disorders will aid clinicians in making informed decisions for effective TMJ disorder assessments and patient management.

Analysis of Temporomandibular Joint Disorders in Children and Adolescents: Diagnosis and Treatment Pattern by Age

Temporomandibular joint disorders (TMDs) can occur at any age, including childhood and adolescence, and pain-related TMDs can affect growth and quality of life. In the present study, recent trends in the diagnosis and treatment of TMDs in children and adolescents were analyzed over a 10-year period. Using 10 years of data from the Health Insurance Review and Assessment Service (HIRA) and Jeonbuk National University (JBNU) Dental Hospital, patients between 0 and 19 years of age diagnosed with K07.6 (temporomandibular joint disorders) were analyzed by 5-year bins. Both datasets indicated a higher prevalence in females (1.2-fold in HIRA, 1.5-fold in JBNU) and in ages 15 to 19 years (72.1% in HIRA, 74.7% in JBNU). HIRA reported a 42.3% increase in prevalence per 100,000 people, from 651.4 in 2011 to 927.0 in 2020. JBNU reported K07.66 (masticatory muscle disorders) as the most common diagnosis in subjects under 10 years of age and K07.60 (internal derangement of temporomandibular joint) in those over 10 years of age. In addition, both were treated mainly by a combination of physical therapy and medication, and the treatment rate increased in accordance with age. Because TMDs can affect various structures in the orofacial region and cause pain that tends to differ with age, an early and specific diagnosis appropriate for age is important for treatment. Therefore, pediatric dentists need to promptly recognize TMDs in children and adolescents and consult with specialists as the prevalence increases.

Imaging in temporomandibular joint disorders

The temporomandibular joint is a unique and complex joint. Various imaging techniques have been developed to properly visualize this complex joint, such as conventional radiology, orthopantomography, CBCT and MRI. Imaging can contribute to the differential diagnosis of temporomandibular joint disorders. Common joint disorders are arthritis and internal derangement. Osseous changes of the temporomandibular joint can be clearly visualized with CBCT. MRI is superior for imaging the internal anatomy of the temporomandibular joint and is preferred in the context of internal derangement.

Diagnosis and treatment of Eagle's syndrome and possible complications.

Eagle's syndrome (ES) is a term describing a group of symptoms associated with the elongation of the styloid process (SP) of the temporal bone or with ossification of the stylohyoid ligament. Clinically, it manifests through pain in the orofacial and pharyngeal regions, similar to that experienced by patients with temporomandibular joint disease. The presented paper aims to assess the evaluation of the success of surgical treatment and the complications associated with such treatment. Our retrospective study includes nine patients with ES treated in our Department Oral and Maxillofacial Surgery in years 2019-2023 (median age of 47 years) - all patients affected with this disease out of 2,716 patients examined at our department due to a temporomandibular joint disease. Eight of these patients underwent a surgical resection of the SP. The success and complications in these patients are evaluated in this study. In seven patients, the problems disappeared or at least diminished, which is consistent with data from the literature. Postoperative complications such as deficit of the marginal branch of the facial nerve (n. VII), limited sticking the tongue out (n. XII), and velopharyngeal insufficiency (n. IX) occurred in three patients. The wide spectrum of clinical manifestations makes ES a condition that should be considered in the differential diagnosis of temporomandibular joint disorders, post-traumatic conditions, or complicated eruption of wisdom teeth or their extraction. ES can be treated conservatively or (mostly) surgically. Surgical treatment can be relatively successful and effective, but it is necessary to respect the anatomical placement of the SP, which can be associated with the development of complications, especially in cases with extremely long SP.

Noise-Optimized CBCT Imaging of Temporomandibular Joints-The Impact of AI on Image Quality.

Background: Temporomandibular joint disorder (TMD) is a common medical condition. Cone beam computed tomography (CBCT) is effective in assessing TMD-related bone changes, but image noise may impair diagnosis. Emerging deep learning reconstruction algorithms (DLRs) could minimize noise and improve CBCT image clarity. This study compares standard and deep learning-enhanced CBCT images for image quality in detecting osteoarthritis-related degeneration in TMJs (temporomandibular joints). This study analyzed CBCT images of patients with suspected temporomandibular joint degenerative joint disease (TMJ DJD). Methods: The DLM reconstructions were performed with ClariCT.AI software. Image quality was evaluated objectively via CNR in target areas and subjectively by two experts using a five-point scale. Both readers also assessed TMJ DJD lesions. The study involved 50 patients with a mean age of 28.29 years. Results: Objective analysis revealed a significantly better image quality in DLM reconstructions (CNR levels; p < 0.001). Subjective assessment showed high inter-reader agreement (κ = 0.805) but no significant difference in image quality between the reconstruction types (p = 0.055). Lesion counts were not significantly correlated with the reconstruction type (p > 0.05). Conclusions: The analyzed DLM reconstruction notably enhanced the objective image quality in TMJ CBCT images but did not significantly alter the subjective quality or DJD lesion diagnosis. However, the readers favored DLM images, indicating the potential for better TMD diagnosis with CBCT, meriting more study.

Evaluation of IL-1β and CRP mRNA expression levels by RT-PCR in postorthodontic treatment patients with temporomandibular joint disorders: a cross-sectional Study.

Temporomandibular joint disorder (TMD), which affects the masticatory muscles, temporomandibular joint, and surrounding tissues, can manifest as inflammation. This study aims to explore the expression levels of the inflammatory biomarkers, interleukin (IL)-1β and C-reactive protein (CRP), in TMD patients who have undergone orthodontic treatment. Buccal swabs from 105 postorthodontic treatment patients were analyzed using real-time polymerase chain reaction to assess the expression levels of IL-1β and CRP in each group after messenger ribonucleic acid extraction. Patients were also examined using the Diagnostic Criteria for TMD (DC/TMD) to determine if they met the criteria for a TMD diagnosis. The TMD group was subdivided into three categories based on the DC/TMD. The study included 37 patients who did not develop TMD (group 0) and 68 participants who developed TMD after orthodontic treatment, including 17 with pain-related TMDs (group 1), 29 with intra-articular TMDs (Group 2), and 22 with combined pain-related and intra-articular TMDs (group 3). CRP expression was higher than IL-1β in groups 1 and 2, and IL-1β expression was higher than CRP in group 3. The Kruskal-Wallis test showed that IL-1β and CRP expression levels in groups 1, 2, and 3 were not statistically different. Sex and adult age had considerable effects on the occurrence of TMD in patients after orthodontic treatment. Higher IL-1β expression was found in postorthodontic treatment patients with more complex TMD. This study strengthens the evidence of inflammation through IL-1β and CRP expression in individuals with TMD, especially after orthodontic treatment.

Correlation between mental well-being and severity of class II temporomandibular joint disorders, compared to non-temporomandibular joint disorders

Temporomandibular joint disorders (TMDs) are one of the most common ailments encountered in the craniofacial region, with various clinical, radiological and psychological manifestations but often go undiagnosed due to lack of a diagnostic protocol to assess and correlate all these factors in order to create an effective management plan based on early diagnosis. The objectives of this research are to assess, compare and co-relate the severity of clinical and radiological findings with mental well-being in class II (vertical growth pattern) TMD cases as compared to class I non-TMD cases. Grading of skeletal class II cases on the severity of TMD will be done using Diagnostic Criteria for TMDs (DC/TMD). Bilateral sagittal as well as coronal magnetic resonance imaging (MRI) images will be obtained. Evaluation and grading of mental well-being and anxiety will be done using the Warwick-Edinburgh and Hamilton scales, respectively. The expected results are a probable positive correlation between the severity of clinical and radiological symptoms of TMDs and deteriorating mental well-being in the form of stress, anxiety and depression. A cause-effect relationship may be evaluated with the aim of early diagnosis and management of TMDs as improving the quality of life of these cases. This study will aid in identifying the clinical symptoms and articular disc position on MRI as they correlate with the mental well-being status and anxiety levels in TMD cases. Further management and early intervention can be undertaken in such cases to prevent or stall the progression of TMD into severe forms while improving the quality of life (QoL) in such cases.

The effect of myotonic dystrophy type 1 on temporomandibular joint and dentofacial morphology: A CBCT analysis.

Myotonic dystrophy type 1 (DM1) is a neuromuscular multisystem disease. Early involvement of facial muscles may produce an extra load on the temporomandibular joint (TMJ) in DM1. This study aimed to investigate the morphological analyses of the bone components of temporomandibular joint (TMJ), and dentofacial morphology in myotonic dystrophy type 1 (DM1) patients by cone-beam computed tomography (CBCT). Sixty-six individuals (33 DM1, and 33 healthy subjects) age ranging from 20 to 69 were included in the study. Clinical examinations of the patients' TMJ regions and evaluation of dentofacial morphology (maxillary deficiency, open-bite, deep palate and cross-bite) were performed. Dental occlusion was determined based on Angle's classification. CBCT images were evaluated regarding mandibular condyle morphology (convex, angled, flat and round) and osseous changes observed in the condyle (normal, osteophyte, erosion, flattening, sclerosis). DM1-specific morphological and bony TMJ alterations were determined. DM1 patients showed a high prevalence of morphological and osseous TMJ changes, and statistically significant skeletal alterations. The analysis of CBCT scans indicated the prevalent condylar shape among patients with DM1 was flat, the main osseous abnormality was flattening, there was a tendency towards skeletal Class II and a posterior cross-bite was frequently detected in DM1 patients. There was no statistically significant difference between the genders on the parameters evaluated in both groups. Adult patients with DM1 presented a high frequency of crossbite, tendency to skeletal Class II and morphological osseous alterations of TMJ. The analysis of the morphological condylar alterations in patients with DM1 may be beneficial in the diagnosis of TMJ disorders. This study reveals DM1-specific morphological and osseous TMJ alterations to provide an appropriate orthodontic/orthognathic treatment planning to patients.

Diagnosis Gangguan Sendi Temporomandibular pada Kasus Kehilangan Gigi dengan Metode DC/TMD

Background: Temporomandibular joint disorder (TMD) is characterized by craniofacial pain involving the jaw joint, masticatory muscles, or muscles that innervate the head and neck. The causes of TMD are multifactorial, including tooth loss and bad habits. The new DC/TMD protocol is a TMD examination protocol that is highly recommended for use as a clinical and research protocol for dentists. Objective: This study aims to determine the prevalence of TMD diagnosis results using the DC/TMD method, to determine the types of TMD diseases experienced and their relationship to the status of tooth loss in students. Method: The study used a cross-sectional analytical design with samples that met the inclusion criteria of 150 students aged 19-25 years who were taken by total sampling. The research subjects were asked to agree and sign an informed consent in advance by the researcher. Data were obtained from the results of anamnesis and physical examination with a diagnosis of TMD using the DC/TMD protocol based on the International RDC/TMD Consortium Network which has been translated into Indonesian. The examination data were processed using SPSS statistical software with univariate and bivariate tests. Results: 48% of students had tooth loss, 62% of students were diagnosed with TMD, and the results of the relationship analysis using the chi-square test showed that there was a significant relationship between tooth loss and TMD (p-value = 0.024; p

Combination of temporomandibular disorder and trigeminal neuralgia in adolescent: a case report

Trigeminal neuralgia was uncommon in people younger than 30 years of age. Only 1% of cases reportedly occurring in those younger than 20 years of age, so that this case is quite rare. The diagnosis of temporomandibular joint disorder (TMD) was also present along with the trigeminal neuralgia and confirmed by head and TMJ MRI. This article describes a case of a 17-years-old females who sought help because of pain in the right side of her face, from her temple area down to her cheek and TMJ, continuing to her lower jaw and sometimes at the back of the ear. She also reported increased pain during chewing or otherfunctional activity. The pain already lasted for almost two years and debilitating. The patient was treated with the combi-nation of pharmacotherapy for treating the trigeminal neuralgia, physical therapy (jaw exercise) and stabilization appliance, in order to reduce the pain and restore the normal function of the lower jaw.It was concluded that trigeminal neuralgia and TMD can be treated simultaneously and obtained a good result.

Temporomandibular joint segmentation in MRI images using deep learning

ObjectivesTemporomandibular joint (TMJ) internal derangements (ID) represent the most prevalent temporomandibular joint disorder (TMD) in the population and its diagnosis typically relies on magnetic resonance imaging (MRI). TMJ articular discs in MRIs usually suffer from low resolution and contrast, and it is difficult to identify them. In this study, we applied two convolutional neural networks (CNN) to delineate mandibular condyle, articular eminence, and TMJ disc in MRI images. MethodsThe models were trained on MRI images from 100 patients and validated on images from 40 patients using 2D slices and 3D volume as input, respectively. Data augmentation and five-fold cross-validation scheme were applied to further regularize the models. The accuracy of the models was then compared with four raters having different expertise in reading TMJ-MRI images to evaluate the performance of the models. ResultsBoth models performed well in segmenting the three anatomical structures. A Dice coefficient of about 0.7 for the articular disc, more than 0.9 for the mandibular condyle, and Hausdorff distance of about 2mm for the articular eminence were achieved in both models. The models reached near-expert performance for the segmentation of TMJ articular disc and performed close to the expert in the segmentation of mandibular condyle and articular eminence. They also surpassed non-experts in segmenting the three anatomical structures. ConclusionThis study demonstrated that CNN-based segmentation models can be a reliable tool to assist clinicians identifying key anatomy on TMJ-MRIs. The approach also paves the way for automatic diagnosis of TMD. Clinical significanceAccurately locating the articular disc is the hardest and most crucial step in the interpretation of TMJ-MRIs and consequently in the diagnosis of TMJ-ID. Automated software that assists in locating the articular disc and its surrounding structures would improve the reliability of TMJ-MRI interpretation, save time and assist in reader training. It will also serve as a foundation for additional automated analysis of pathology in TMJ structures to aid in TMD diagnosis.

New Methods for the Acoustic-Signal Segmentation of the Temporomandibular Joint.

(1) Background: The stethoscope is one of the main accessory tools in the diagnosis of temporomandibular joint disorders (TMD). However, the clinical auscultation of the masticatory system still lacks computer-aided support, which would decrease the time needed for each diagnosis. This can be achieved with digital signal processing and classification algorithms. The segmentation of acoustic signals is usually the first step in many sound processing methodologies. We postulate that it is possible to implement the automatic segmentation of the acoustic signals of the temporomandibular joint (TMJ), which can contribute to the development of advanced TMD classification algorithms. (2) Methods: In this paper, we compare two different methods for the segmentation of TMJ sounds which are used in diagnosis of the masticatory system. The first method is based solely on digital signal processing (DSP) and includes filtering and envelope calculation. The second method takes advantage of a deep learning approach established on a U-Net neural network, combined with long short-term memory (LSTM) architecture. (3) Results: Both developed methods were validated against our own TMJ sound database created from the signals recorded with an electronic stethoscope during a clinical diagnostic trail of TMJ. The Dice score of the DSP method was 0.86 and the sensitivity was 0.91; for the deep learning approach, Dice score was 0.85 and there was a sensitivity of 0.98. (4) Conclusions: The presented results indicate that with the use of signal processing and deep learning, it is possible to automatically segment the TMJ sounds into sections of diagnostic value. Such methods can provide representative data for the development of TMD classification algorithms.

Imaging methods used in the diagnosis of temporomandibular joint disorders

Temporomandibular eklem düzensizliği, çiğneme kasları ile eklemin sert ve yumuşak dokularını etkileyen ve toplumda yaygın gözlenen bir hastalıktır. Teşhise karar verme sürecinde anamnez ve klinik muayene yanında radyolojik değerlendirme de yapılır. Temporomandibular eklem görüntülenmesinde, sert ve yumuşak dokuların anatomik bütünlüğünün değerlendirildiği ya da metabolik değişimlerin incelendiği çeşitli görüntüleme yöntemleri kullanılmaktadır. Kullanılacak yöntem patolojiye uygunluk, radyasyon dozu, maliyet, yararlılık, ulaşılabilirlik gibi faktörlere göre seçilmelidir. Bu derlemede temporomandibular eklem yapılarının değerlendirilmesinde kullanılan direkt radyografi yöntemlerinden panoramik, transkraniyel, transfaringeal, transmaksiller, submentoverteks, transorbital, reverse towne, posterior-anterior radyografileri, lateral ve posteroanterior sefalometri, dijital substraksiyon radyografisi ile dijital floroskopi, konvansiyonel, bilgisayarlı ve konik ışınlı bilgisayarlı tomografi, manyetik rezonans görüntüleme, artrografi, ultrasonografi ve radyonüklid görüntüleme yöntemleri incelenmiştir. Yöntemlerin endikasyonları, avantaj ve dezavantajları özetlenmiş ve birbiri ile karşılaştırıldığı çalışmalardan bahsedilmiştir.

Role of Radiology in Diagnosis of Temporomandibular Joint Disorders

The diagnosis and management of temporomandibular disorders (TMD) require both clinical andimaging examinations of the temporomandibular joint (TMJ). A variety of modalities can be used toimage the TMJ, including magnetic resonance imaging (MRI), computed tomography (CT), cone beamCT, ultrasonography, conventional radiography. The present review outlines the indications of the mostfrequently used imaging techniques in TMD diagnosis.Because of the anatomic complexity of the TMJ, imaging can be difficult. Choosing the properimaging technique is essential. Conventional radiography, nowadays, is of limited interest. The useof flat plane films for TMJ pathology is not sufficient, because this joint requires three dimensionalimaging views. Osseous changes are better visualized with CT and cone beam CT. Cone beam CTprovides high-resolution multiplanar reconstruction of the TMJ, with a low radiation dose, withoutsuperimposition of the bony structures. MRI is a noninvasive technique, considered to be the goldstandard in imaging the soft tissue components of the TMJ. MRI is used to evaluate the articular disc interms of location and morphology. Moreover, the early signs of TMD and the presence of joint effusioncan be determined. High-resolution ultrasonography is a noninvasive, dynamic, inexpensive imagingtechnique, which can be useful in diagnosing TMJ disc displacements. The diagnostic value of highresolutionultrasonography is strictly dependent on the examiner’s skills and on the equipment used.

Comparison of diagnosis of temporomandibular joint disorders based on RDC/TMD Axis I and DC/TMD Axis I

Introduction: The Research Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD) has become the most widely used diagnostic protocol for research in temporomandibular disorders (TMD). The invalidity of RDC/ TMD in clinical application causes the revision of RDC/TMD to be the Diagnostic Criteria for Temporomandibular Disorder (DC/TMD). The purpose of this study was to determine the differences in diagnosis of TMD between RDC/ TMD examination and DC/TMD Axis I on students of Faculty of Dentistry, Padjadjaran University. Method: The type of this research was comparative survey approach using clinical examinations and questionnaires. The sample was collected from 48 people using random sampling techniques. The diagnosis of TMD was obtained by filling in the symptom questionnaire and clinical examination based on RDC/TMD Axis I and DC/TMD Axis I, which is then entered into the RDC/TMD diagnosis algorithm and DC/TMD decision tree. Results: The results showed that from 48 samples there were 36 (75%) people with the same diagnosis of RDC/TMD and DC/TMD, and 12 (25%) people with different diagnoses between RDC/TMD and DC/TMD. Conclusions: Based on the results of the study, the diagnosis of TMD based on RDC/TMD were still categorized the same as the diagnosis based on DC/TMD.

Towards classification of patients based on surface EMG data of temporomandibular joint muscles using self-organising maps

The study considers the need for an effective method of classification of patients with a temporomandibular joint disorder (TMD). The self-organising map method (SOM) was applied to group patients and used together with the cross-correlation approach to interpret the processed (rectified and smoothed by using root mean square (RMS) algorithm) surface electromyography signal (sEMG) obtained from testing the muscles (two temporal muscles and two masseters) of the temporomandibular joint (TMJ) during selected jaw movements. SOM’s Unified distance matrix (U-matrix) maps consist of formed clusters that correspond to similarities in input datasets. The results showed that SOM was able to encode muscular responses and create clusters. Information about the level of similarity between the activity of right, left, ipsilateral, and contralateral pairs of muscles was provided by intra cross-correlation coefficient (CC). A low intra CC value may indicate instability of the TMJ function. Information about the level of similarity between the sEMG signals of the same muscles tested in two different patients was provided by inter CC. SOM analysis can be used to interpret the activation of muscular systems, and by comparing the results of different individuals also to identify their TMJ health. Using the cross-correlation approach, one can find similarities in the sEMG data of different patients that can be used to provide clinically useful information. Such findings could be used to improve the clinical diagnosis of TMD and assess muscle activity during treatment.

A deep learning based decision support system for diagnosis of Temporomandibular joint disorder

Temporomandibular Joint sounds are a very common disorder in the general population. Temporomandibular Disorder (TMD) is any discomfort related to Temporomandibular Joint (TMJ). In this paper, a novel decision support system based on deep learning and neural network algorithms for diagnosis of Temporomandibular Joint disorder is introduced. A non-invasive device is designed for the recording of TMJ sounds. An interface is developed that will facilitate the dentist to operate on the recorded audio data. The collected data consist of the patient's left and right Temporomandibular Joint sound, ambient noise sound, the patient's clinical data, the dentist's notes about the patient, diagnosis, and treatment. Then signal processing, artificial neural network and deep learning algorithms are used to classify these measurements, and thus, the method that decides about the patient's condition is developed. Frequency, statistical and deep learning-based methods are compared in terms of classification success. The results show that the classification success of the classification method based on deep learning is consistently over 94.5% and it is more successful than the previous two methods. The proposed system can give the physician an idea about the effectiveness of the treatment methods applied to the patient in order to treat joint sounds which are among the important symptoms of TMD.

Investigation of osteopontin and CD44 levels in patients with temporomandibular joint disorders

BackgroundThe information about the cellular and molecular mechanisms underlying the formation and progression of temporomandibular joint disorders (TMDs) is limited and the etiology of these disorders is still controversial. Biochemical analyzes are an important tool in understanding the pathogenesis of these diseases. ObjectiveThe main aim of the present study was to investigate the role of serum osteopontin (OPN) and CD44 levels in the etiopathology and to determine whether they could be used as biochemical markers in the diagnosis of TMDs. MethodsTotal number of 54 patients who diagnosed with TMD and total of 34 healthy controls with no joint problems were included in this study. Blood samples were obtained from all patients and then serum samples were analyzed by OPN and CD44 enzyme-linked immunosorbent assay (ELISA). ResultsAccording to the control group, there was a significant decrease in OPN levels in patients with TMDs. However, no statistically significant difference was found in CD44 levels among the groups. ConclusionsFindings obtained in the present study suggest that OPN may play a key role in the pathogenesis of TMDs as a novel biomarker in the diagnosis of these diseases.