Minimally Invasive Approach for Diagnosing TMJ Osteoarthritis

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.

Does salivary cortisol serve as a potential biomarker for temporomandibular disorders in adults?

BACKGROUND

The etiology of temporomandibular disorders (TMD) is multifactorial, involving a complex interplay of psychological and physiological factors. While salivary biomarkers, particularly cortisol, play an important role in TMD pathophysiology, evidence in the literature is still scarce and inconsistent. Hence, this study aims to evaluate the applicability of salivary cortisol as a potential biomarker for TMD in adults.

METHODS

The Diagnostic Criteria for Temporomandibular Disorders (DC/TMD) were used to accurately diagnose TMD patients. The study included adults, both male and female, aged 18-40 in the TMD group (n = 66) and non TMD participants (n = 66) matched for age and gender. Salivary samples were collected from participants at two time points: early and late morning. Cortisol levels in the samples were quantified using enzyme-linked immunosorbent assay (ELISA). Statistical analysis was performed using a one-way ANOVA to evaluate the correlations between cortisol levels and the study variables. Tukey's post-hoc tests were applied to adjust for multiple comparisons.

RESULTS

Salivary cortisol levels were significantly higher in the TMD group than in the corresponding controls (p = 0.034). In the TMD group, the mean cortisol levels early in the morning were 29.95 ± 75.05 (0-398.64), while the late morning levels were recorded as 4.87 ± 3.96 (0-17.13). In the control group, the mean cortisol levels early in the morning were 10.98 ± 16.83 (2.16-92.90), and late morning mean cortisol levels were 6.15 ± 6.13 (0-20.42). This indicates that the early morning levels of cortisol are higher in TMD patients (p = 0.046). In the subgroup analysis of the TMD, the mean salivary cortisol levels recorded were highest at 82.49 ± 124.34 (8.32-398.64) in patients having disc displacement without reduction with limited mouth opening. Furthermore, the mean salivary cortisol levels in the early morning were statistically higher (84.83 ± 132.80) in males compared to females (9.36 ± 9.01) (p = 0.008) with TMD.

CONCLUSION

The result of this study suggests that salivary cortisol could be a potential biomarker for a specific TMD subtype (disc displacement without reduction with limited mouth opening). However, further studies are needed to better understand the role of cortisol biomarker in the underlying pathogenesis of TMD.

Automatic detection of temporomandibular joint osteoarthritis radiographic features using deep learning artificial intelligence. A Diagnostic accuracy study

OBJECTIVE

The purpose of this study was to investigate the diagnostic performance of a neural network Artificial Intelligence model for the radiographic confirmation of Temporomandibular Joint Osteoarthritis in reference to an experienced radiologist.

MATERIALS AND METHODS

The diagnostic performance of an AI model in identifying radiographic features in patients with TMJ-OA was evaluated in a diagnostic accuracy cohort study. Adult patients elected for radiographic examination by the Diagnostic Criteria for Temporomandibular Disorders decision tree were included. Cone-beam computed Tomography images were evaluated by object detection YOLO deep learning model. The diagnostic performance was verified against examiner radiographic evaluation.

RESULTS

The differences between the AI model and examiner were non-significant statistically, except in the subcortical cyst (P=0.049*). AI model showed substantial to near-perfect levels of agreement when compared to those of the examiner data. Regarding each radiographic phenotype, the AI model reported favorable sensitivity, specificity, accuracy, and highly statistically significant Receiver Operating Characteristic (ROC) analysis (p<0.001). Area Under Curve ranged from 0.872, for surface erosion, to 0.911 for subcortical cyst.

CONCLUSION

AI object detection model could open the horizon for a valid, automated, and convenient modality for TMJ-OA radiographic confirmation and radiomic features identification with a significant diagnostic power.

Deep learning for temporomandibular joint arthropathies: A systematic review and meta-analysis

BACKGROUND AND OBJECTIVE

The accurate diagnosis of temporomandibular disorders continues to be a challenge, despite the existence of internationally agreed-upon diagnostic criteria. The purpose of this study is to review applications of deep learning models in the diagnosis of temporomandibular joint arthropathies.

MATERIALS AND METHODS

An electronic search was conducted on PubMed, Scopus, Embase, Google Scholar, IEEE, arXiv, and medRxiv up to June 2023. Studies that reported the efficacy (outcome) of prediction, object detection or classification of TMJ arthropathies by deep learning models (intervention) of human joint-based or arthrogenous TMDs (population) in comparison to reference standard (comparison) were included. To evaluate the risk of bias, included studies were critically analysed using the quality assessment of diagnostic accuracy studies (QUADAS-2). Diagnostic odds ratios (DOR) were calculated. Forrest plot and funnel plot were created using STATA 17 and MetaDiSc.

RESULTS

Full text review was performed on 46 out of the 1056 identified studies and 21 studies met the eligibility criteria and were included in the systematic review. Four studies were graded as having a low risk of bias for all domains of QUADAS-2. The accuracy of all included studies ranged from 74% to 100%. Sensitivity ranged from 54% to 100%, specificity: 85%-100%, Dice coefficient: 85%-98%, and AUC: 77%-99%. The datasets were then pooled based on the sensitivity, specificity, and dataset size of seven studies that qualified for meta-analysis. The pooled sensitivity was 95% (85%-99%), specificity: 92% (86%-96%), and AUC: 97% (96%-98%). DORs were 232 (74-729). According to Deek's funnel plot and statistical evaluation (p =.49), publication bias was not present.

CONCLUSION

Deep learning models can detect TMJ arthropathies high sensitivity and specificity. Clinicians, and especially those not specialized in orofacial pain, may benefit from this methodology for assessing TMD as it facilitates a rigorous and evidence-based framework, objective measurements, and advanced analysis techniques, ultimately enhancing diagnostic accuracy.

Ginsenoside Rg1/ADSCs supplemented with hyaluronic acid as the matrix improves rabbit temporomandibular joint osteoarthrosis

OBJECTIVE

To investigate whether and how ginsenoside Rg1/ADSCs supplemented with hyaluronic acid as the matrix can improve rabbit temporomandibular joint osteoarthrosis.

METHOD

Isolate and culture adipose stem cells, measure the activity of differentiated chondrocytes by MTT assay and expression of type II collagen in these cells by immunohistochemistry, in order to evaluate the effect of ginsenoside Rg1 on adipose stem cell proliferation and differentiation into chondrocytes.32 New Zealand white rabbits were randomly divided into four groups: blank group, model group, control group and experimental group, 8 in each group. Osteoarthritis model was established by intra-articular injection of papain. Two weeks after successful model building, medication was given for the rabbits in control group and experimental group. 0.6 mL ginsenoside Rg1/ ADSCs suspension was injected into superior joint space for the rabbits in control group, once a week; 0.6 mL ginsenoside Rg1/ ADSCs complex was injected for the rabbits in experimental group, once a week.

RESULTS

Ginsenoside Rg1 can promote ADSCs-derived chondrocytes' activity and expression of type II collagen. Scanning electron microscopy histology images showed cartilage lesions of the experimental group was significantly improved in comparison with control group.

CONCLUSION

Ginsenoside Rg1 can promote ADSCs differentiate into chondrocytes, and Ginsenoside Rg1/ADSCs supplemented with hyaluronic acid as the matrix can significantly improve rabbit temporomandibular joint osteoarthrosis.

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.

Salivary Biomarkers and Temporomandibular Disorders: A Systematic Review conducted according to PRISMA guidelines and the Cochrane Handbook for Systematic Reviews of Interventions

BACKGROUND

The present review aimed to investigate the association between salivary biomarkers and temporomandibular disorders (TMD). TMD is a multifactorial condition characterised by pain and dysfunction in the temporomandibular joint (TMJ) and surrounding structures. Salivary biomarkers have emerged as potential diagnostic tools due to their non-invasiveness and easy accessibility. However, the literature on salivary biomarkers in relation to TMD is limited and inconsistent.

METHODS

Electronic databases of Pubmed, Embase, Web of Science, Scopus, Cochrane Library, PsychINFO, CINAHL and Medline were searched using specific search terms and Boolean operators. The search was limited to articles published in English that assessed salivary biomarkers in individuals diagnosed with TMD. Two reviewers independently screened the articles and extracted data. ROB-2 was used to assess the risk of bias.

RESULTS

Eleven clinical papers met the inclusion criteria and were included in the review. The findings provided consistent evidence of a clear association between salivary biomarkers and TMD. Various biomarkers, including cortisol, IL-1, glutamate and several others, were assessed. Some studies reported higher levels of cortisol and IL-1 in TMD patients, indicating potential involvement in stress and inflammation. Glutamate levels were found to be elevated, suggesting a role in pain modulation. Other biomarkers also showed alterations in TMD patients compared to controls: CONCLUSION: The findings from the included studies suggest that salivary biomarkers may play a role in TMD pathophysiology. Though a definitive conclusion can be drawn regarding the specific salivary biomarkers and their association with TMD, the results must be interpreted with caution considering the heterogeneity of the biomarkers assessed. Further research with larger sample sizes, standardised methodology and rigorous study designs is needed to elucidate the role of salivary biomarkers in TMD.

AI in Orthodontics: Revolutionizing Diagnostics and Treatment Planning-A Comprehensive Review

The advent of artificial intelligence (AI) in medicine has transformed various medical specialties, including orthodontics. AI has shown promising results in enhancing the accuracy of diagnoses, treatment planning, and predicting treatment outcomes. Its usage in orthodontic practices worldwide has increased with the availability of various AI applications and tools. This review explores the principles of AI, its applications in orthodontics, and its implementation in clinical practice. A comprehensive literature review was conducted, focusing on AI applications in dental diagnostics, cephalometric evaluation, skeletal age determination, temporomandibular joint (TMJ) evaluation, decision making, and patient telemonitoring. Due to study heterogeneity, no meta-analysis was possible. AI has demonstrated high efficacy in all these areas, but variations in performance and the need for manual supervision suggest caution in clinical settings. The complexity and unpredictability of AI algorithms call for cautious implementation and regular manual validation. Continuous AI learning, proper governance, and addressing privacy and ethical concerns are crucial for successful integration into orthodontic practice.

Artificial intelligence in salivary biomarker discovery and validation for oral diseases

Salivary biomarkers can improve the efficacy, efficiency, and timeliness of oral and maxillofacial disease diagnosis and monitoring. Oral and maxillofacial conditions in which salivary biomarkers have been utilized for disease-related outcomes include periodontal diseases, dental caries, oral cancer, temporomandibular joint dysfunction, and salivary gland diseases. However, given the equivocal accuracy of salivary biomarkers during validation, incorporating contemporary analytical techniques for biomarker selection and operationalization from the abundant multi-omics data available may help improve biomarker performance. Artificial intelligence represents one such advanced approach that may optimize the potential of salivary biomarkers to diagnose and manage oral and maxillofacial diseases. Therefore, this review summarized the role and current application of techniques based on artificial intelligence for salivary biomarker discovery and validation in oral and maxillofacial diseases.

Neuroimaging and artificial intelligence for assessment of chronic painful temporomandibular disorders-a comprehensive review

Chronic Painful Temporomandibular Disorders (TMD) are challenging to diagnose and manage due to their complexity and lack of understanding of brain mechanism. In the past few decades' neural mechanisms of pain regulation and perception have been clarified by neuroimaging research. Advances in the neuroimaging have bridged the gap between brain activity and the subjective experience of pain. Neuroimaging has also made strides toward separating the neural mechanisms underlying the chronic painful TMD. Recently, Artificial Intelligence (AI) is transforming various sectors by automating tasks that previously required humans' intelligence to complete. AI has started to contribute to the recognition, assessment, and understanding of painful TMD. The application of AI and neuroimaging in understanding the pathophysiology and diagnosis of chronic painful TMD are still in its early stages. The objective of the present review is to identify the contemporary neuroimaging approaches such as structural, functional, and molecular techniques that have been used to investigate the brain of chronic painful TMD individuals. Furthermore, this review guides practitioners on relevant aspects of AI and how AI and neuroimaging methods can revolutionize our understanding on the mechanisms of painful TMD and aid in both diagnosis and management to enhance patient outcomes.

Orthodontic Implementation of Machine Learning Algorithms for Predicting Some Linear Dental Arch Measurements and Preventing Anterior Segment Malocclusion: A Prospective Study

Background and Objectives: Orthodontics is a field that has seen significant advancements in recent years, with technology playing a crucial role in improving diagnosis and treatment planning. The study aimed to implement artificial intelligence to predict the arch width as a preventive measure to avoid future crowding in growing patients or even in adult patients seeking orthodontic treatment as a tool for orthodontic diagnosis. Materials and Methods: Four hundred and fifty intraoral scan (IOS) images were selected from orthodontic patients seeking treatment in private orthodontic centers. Real inter-canine, inter-premolar, and inter-molar widths were measured digitally. Two of the main machine learning models were used: the Python programming language and machine learning algorithms, implementing the data on k-nearest neighbor and linear regression. Results: After the dataset had been implemented on the two ML algorithms, linear regression and k-nearest neighbor, the evaluation metric shows that KNN gives better prediction accuracy than LR does. The resulting accuracy was around 99%. Conclusions: it is possible to leverage machine learning to enhance orthodontic diagnosis and treatment planning by predicting linear dental arch measurements and preventing anterior segment malocclusion.

Osteoarthritis Diagnosis Integrating Whole Joint Radiomics and Clinical Features for Robust Learning Models Using Biological Privileged Information

This paper proposes a machine learning model using privileged information (LUPI) and normalized mutual information feature selection method (NMIFS) to build a robust and accurate framework to diagnose patients with Temporomandibular Joint Osteoarthritis (TMJ OA). To build such a model, we employ clinical, quantitative imaging and additional biological markers as privileged information. We show that clinical features play a leading role in the TMJ OA diagnosis and quantitative imaging features, extracted from cone-beam computerized tomography (CBCT) scans, improve the model performance. As the proposed LUPI model employs biological data in the training phase (which boosted the model performance), this data is unnecessary for the testing stage, indicating the model can be widely used even when only clinical and imaging data are collected. The model was validated using 5-fold stratified cross-validation with hyperparameter tuning to avoid the bias of data splitting. Our method achieved an AUC, specificity and precision of 0.81, 0.79 and 0.77, respectively.

An artificial intelligence model for the radiographic diagnosis of osteoarthritis of the temporomandibular joint

The interpretation of the signs of Temporomandibular joint (TMJ) osteoarthritis on cone-beam computed tomography (CBCT) is highly subjective that hinders the diagnostic process. The objectives of this study were to develop and test the performance of an artificial intelligence (AI) model for the diagnosis of TMJ osteoarthritis from CBCT. A total of 2737 CBCT images from 943 patients were used for the training and validation of the AI model. The model was based on a single convolutional network while object detection was achieved using a single regression model. Two experienced evaluators performed a Diagnostic Criteria for Temporomandibular Disorders (DC/TMD)-based assessment to generate a separate model-testing set of 350 images in which the concluded diagnosis was considered the golden reference. The diagnostic performance of the model was then compared to an experienced oral radiologist. The AI diagnosis showed statistically higher agreement with the golden reference compared to the radiologist. Cohen's kappa showed statistically significant differences in the agreement between the AI and the radiologist with the golden reference for the diagnosis of all signs collectively (P = 0.0079) and for subcortical cysts (P = 0.0214). AI is expected to eliminate the subjectivity associated with the human interpretation and expedite the diagnostic process of TMJ osteoarthritis.

Early Predicting Tribocorrosion Rate of Dental Implant Titanium Materials Using Random Forest Machine Learning Models

Early detection and prediction of bio-tribocorrosion can avert unexpected damage that may lead to secondary revision surgery and associated risks of implantable devices. Therefore, this study sought to develop a state-of-the-art prediction technique leveraging machine learning(ML) models to classify and predict the possibility of mechanical degradation in dental implant materials. Key features considered in the study involving pure titanium and titanium-zirconium (zirconium = 5, 10, and 15 in wt%) alloys include corrosion potential, acoustic emission(AE) absolute energy, hardness, and weight-loss estimates. ML prototype models deployed confirms its suitability in tribocorrosion prediction with an accuracy above 90%. Proposed system can evolve as a continuous structural-health monitoring as well as a reliable predictive modeling technique for dental implant monitoring.

Salivary biomarkers and temporomandibular disorders: A systematic review

Temporomandibular disorders (TMD) are a common condition affecting the musculoskeletal group evoking clinical signs such as pain, restricted mouth opening, and disability in the temporomandibular joint (TMJ), masticatory musculature, and the osseous structures in the surroundings. Saliva is a strong proponent of a diagnostic and prognostic tool for TMDs. Hence, a systematic review was undertaken to answer the research question "What is the role of salivary biomarkers in the identification of TMD?" A thorough literature search was performed in databases of PubMed, Embase, and Google Scholar till February 2022. Every included study was characterized by Study ID, location, sample size, demographic information, biomarker analysis, assessment method, and results. Newcastle-Ottawa scale was used to assess the methodological quality of all qualifying research. A total of eight articles were included for the review after screening the titles, abstracts, and full-text articles. The review included articles of observational design with a control group. TMD disorders were confirmed both clinically and radiographically in the study of Shoukri et al. TMDs are commonly prevalent in maxillofacial conditions. Despite the availability of various diagnostic techniques, certain limitations are remarkable. The researchers are yet to ascertain a gold standard biomarker to identify TMD.

Comparative evaluation of the trabecular structure of the mandibular condyle, the levels of salivary cortisol, MMP-3, TNF-α, IL-1β in individuals with and without temporomandibular joint disorder

OBJECTIVE

Temporomandibular disorder (TMD) is still a challenge in terms of diagnosis and management. The aim of this study is to explore if the evaluation of salivary biomarkers and fractal dimension (FD) of mandibular condyle could be useful for interpreting early degenerative changes and the effectiveness of salivary cortisol in determining the stress levels of TMD patients.

MATERIALS AND METHODS

34 patients with TMD, and 34 healthy controls were included in this study. Saliva samples were obtained from all participants between 09:00-12:00 am. Salivary cortisol, IL-1β, TNF-α, and MMP-3 levels were evaluated with ELISA method. FD of the mandibular condyle was determined by means of box-counting method. Depression and anxiety were determined with PHQ-9 and GAD-7 questionnaires.

RESULTS

The salivary cortisol and depression/anxiety were higher in study group; however, not significant (p>0.05). FD of the study group was found significantly lower than the control group (p<0.01). Salivary TNF-α, IL-1β and MMP-3 levels were showed no significant difference between the two groups (p>0.05). There were no significant correlations between the evaluated parameters.

CONCLUSION

Salivary cortisol seems to be a non-invasive way of measuring physiological stress of TMD patients. Fractal analysis may be a useful tool in detecting early structural changes in mandibular condyle. Salivary TNF-α. IL-1β and MMP-3 have not a diagnostic value in terms of interpreting early degenerative changes.

A Comparison of the Condyle and Articular Eminence in Asian Juvenile Idiopathic Osteoarthritis Patients with Unilateral and Bilateral TMJ Involvement: A Retrospective Case-Control Study

This study compared the condylar volume, length, and articular eminence (AE) characteristics of normal individuals to those with unilateral and bilateral juvenile idiopathic osteoarthritis (JOA). The 116 patients were divided into four groups: Control (n = 16), affected condyle of unilateral JOA (Aff-Uni) (n = 36), non-affected condyle of JOA (NonAff-uni) (n = 36), and bilateral JOA (Bilateral) (n = 28). The differences in condyle volume and length and AE were analyzed using ANOVA and Bonferroni post-hoc tests. The results showed that Bilateral had a significantly different condylar volume, especially in the condylar head (p < 0.01), specifically the middle, anterior, and medial parts (p < 0.05). Condylar length also differed among the groups, with differences observed between the control group and the other three groups, as well as between the bilateral group and the other three groups (p < 0.01). AE total volume differed between the control group and Aff-Uni. In the detailed comparison, Aff-Uni and NonAff-Uni were smaller than the control group in the posterior, lateral, and medial sections (p < 0.05). In conclusion, depending on the involvement of unilateral or bilateral JOA, there were differences in condylar volume and AE when compared to the normal control group. Therefore, a prognosis should be evaluated by distinguishing between patients with unilateral and bilateral JOA.

A Comprehensive Review of Artificial Intelligence Based Algorithms Regarding Temporomandibular Joint Related Diseases

Today, with rapid advances in technology, computer-based studies and Artificial Intelligence (AI) approaches are finding their place in every field, especially in the medical sector, where they attract great attention. The Temporomandibular Joint (TMJ) stands as the most intricate joint within the human body, and diseases related to this joint are quite common. In this paper, we reviewed studies that utilize AI-based algorithms and computer-aided programs for investigating TMJ and TMJ-related diseases. We conducted a literature search on Google Scholar, Web of Science, and PubMed without any time constraints and exclusively selected English articles. Moreover, we examined the references to papers directly related to the topic matter. As a consequence of the survey, a total of 66 articles within the defined scope were assessed. These selected papers were distributed across various areas, with 11 focusing on segmentation, 3 on Juvenile Idiopathic Arthritis (JIA), 10 on TMJ Osteoarthritis (OA), 21 on Temporomandibular Joint Disorders (TMD), 6 on decision support systems, 10 reviews, and 5 on sound studies. The observed trend indicates a growing interest in artificial intelligence algorithms, suggesting that the number of studies in this field will likely continue to expand in the future.

Metformin attenuates symptoms of osteoarthritis: role of genetic diversity of Bcl2 and CXCL16 in OA

OBJECTIVE

This study aimed to evaluate the effectiveness of metformin versus placebo in overweight patients with knee osteoarthritis (OA). In addition, to assess the effects of inflammatory mediators and apoptotic proteins in the pathogenesis of OA, the genetic polymorphisms of two genes, one related to apoptosis (rs2279115 of Bcl-2) and the other related to inflammation (rs2277680 of CXCL-16), were investigated.

METHODS

In this double-blind placebo-controlled clinical trial, patients were randomly divided to two groups, one group receiving metformin (n = 44) and the other one receiving an identical inert placebo (n = 44) for 4 consecutive months (starting dose 0.5 g/day for the first week, increase to 1 g/day for the second week, and further increase to 1.5 g/day for the remaining period). Another group of healthy individuals (n = 92) with no history and diagnosis of OA were included in this study in order to evaluate the role of genetics in OA. The outcome of treatment regimen was evaluated using the Knee Injury and Osteoarthritis Outcome Score (KOOS) questionnaire. The frequency of variants of rs2277680 (A181V) and rs2279115 (938C>A) were determined in extracted DNAs using PCR-RFLP method.

RESULTS

Our results indicated an increase in scores of pain (P ≤ 0.0001), activity of daily living (ADL) (P ≤ 0.0001), sport and recreation (Sport/Rec) (P ≤ 0.0001), and quality of life (QOL) (P = 0.003) and total scores of the KOOS questionnaire in the metformin group compared to the placebo group. Susceptibility to OA was associated with age, gender, family history, CC genotype of 938C>A (Pa = 0.001; OR = 5.2; 95% CI = 2.0-13.7), and GG+GA genotypes of A181V (Pa = 0.04; OR = 2.1; 95% CI = 1.1-10.5). The C allele of 938C>A (Pa = 0.04; OR = 2.2; 95% CI = 1.1-9.8) and G allele of A181V (Pa = 0.02; OR = 2.2; 95% CI = 1.1-4.8) were also associated with OA.

CONCLUSION

Our findings support the possible beneficial effects of metformin on improving pain, ADL, Sport/Rec, and QOL in OA patients. Our findings support the association between the CC genotype of Bcl-2 and GG+GA genotypes of CXCL-16 and OA.

Effects of food hardness on temporomandibular joint osteoarthritis: Qualitative and quantitative micro-CT analysis of rats in vivo

BACKGROUND

Temporomandibular joint osteoarthritis (TMJ-OA) is a degenerative joint disease in which quantitative analysis based on magnetic resonance image (MRI) or cone-beam computed tomography (CBCT) remains limited. Moreover, the long-term effects of soft food on the adaptive condylar remodeling process in TMJ-OA remain unclear. This study aimed to assess the effects of food hardness on adaptive condylar remodeling in a healthy TMJ, TMJ-OA, and controlled TMJ-OA.

METHODS

Complete Freund's adjuvant (CFA) was used for TMJ-OA induction and Link-N (LN) for TMJ repair. Eighteen mature rats were randomly divided into six groups: (1) control/normal diet (Ctrl-N); (2) control/soft diet (Ctrl-S); (3) TMJ-OA/normal diet (CFA-N); (4) TMJ-OA/soft diet (CFA-S); (5) Link-N-controlled TMJ-OA/normal diet (LN-N); and (6) Link-N-controlled TMJ-OA/soft diet (LN-S). Micro-CT was performed 14, 21, and 28 days after CFA injection to analyze the bone volume, bone volume fraction (BVF), bone mineral density (BMD), and trabecular bone number and thickness (Tb.N, Tb.Th). MRI and histological imaging were performed to support the analysis.

RESULTS

Under CFA treatment, the BVF and BMD decreased significantly (p < 0.01) and later recovered to normal. However, more significant improvements occurred in normal-diet groups than soft-diet groups. Additionally, bone volume changes were more predictable in the normal-diet groups than in the soft-diet groups. The normal-diet groups presented a significant decrease and increase in the Tb.N and Tb.Th, respectively (p < 0.05), while the Tb.N and Tb.Th in the soft-diet groups remained largely unchanged. Furthermore, a significantly higher frequency of irregularities on the condylar articular surface was found in the soft-diet groups.

CONCLUSIONS

Compared with a soft diet, a normal diet may be beneficial for preserving condyle articular surface and directing bone remodeling in TMJ-OA rats.

Efficacy analysis of splint combined with platelet-rich plasma in the treatment of temporomandibular joint osteoarthritis

Objective: To evaluate the efficacy of splints combined with PRP for the treatment of temporomandibular joint osteoarthritis. Methods: Thirty-one patients with temporomandibular joint osteoarthritis who were treated with splints combined with platelet-rich plasma (PRP) from January 2021 to June 2021 at the Department of Oral and Maxillofacial Surgery, School of Stomatology, China Medical University (Shenyang, China) were retrospectively reviewed. The VAS scores of all the patients were recorded before and 6 months after treatment, and the maximum comfortable mouth opening was recorded. All data were analyzed by the paired t-test using SPSS software, and a p-value < 0.05 indicated statistically significant differences. Results: Splint + PRP treatment was successful in 31 patients. The mean pretreatment VAS score was 6.1, and the mean VAS score 6 months posttreatment was 4.1. The posttreatment VAS score was significantly lower than the preoperative VAS score (p < 0.05). The mean pretreatment maximum comfortable mouth opening (MCMO) was 27.6 mm, and the mean MCMO 6 months posttreatment was 34.8 mm. The MCMO was significantly increased (p < 0.05). Conclusion: Splint + PRP is an effective treatment for temporomandibular joint osteoarthritis.

Quantitative bone imaging biomarkers and joint space analysis of the articular Fossa in temporomandibular joint osteoarthritis using artificial intelligence models

Temporomandibular joint osteoarthritis (TMJ OA) is a disease with a multifactorial etiology, involving many pathophysiological processes, and requiring comprehensive assessments to characterize progressive cartilage degradation, subchondral bone remodeling, and chronic pain. This study aimed to integrate quantitative biomarkers of bone texture and morphometry of the articular fossa and joint space to advance the role of imaging phenotypes for diagnosis of Temporomandibular Joint Osteoarthritis (TMJ OA) in early to moderate stages by improving the performance of machine-learning algorithms to detect TMJ OA status. Ninety-two patients were prospectively enrolled (184 h-CBCT scans of the right and left mandibular condyles), divided into two groups: 46 control and 46 TMJ OA subjects. No significant difference in the articular fossa radiomic biomarkers was found between TMJ OA and control patients. The superior condyle-to-fossa distance (p < 0.05) was significantly smaller in diseased patients. The interaction effects of the articular fossa radiomic biomarkers enhanced the performance of machine-learning algorithms to detect TMJ OA status. The LightGBM model achieved an AUC 0.842 to diagnose the TMJ OA status with Headaches and Range of Mouth Opening Without Pain ranked as top features, and top interactions of VE-cadherin in Serum and Angiogenin in Saliva, TGF-β1 in Saliva and Headaches, Gender and Muscle Soreness, PA1 in Saliva and Range of Mouth Opening Without Pain, Lateral Condyle Grey Level Non-Uniformity and Lateral Fossa Short Run Emphasis, TGF-β1 in Serum and Lateral Fossa Trabeculae number, MMP3 in Serum and VEGF in Serum, Headaches and Lateral Fossa Trabecular spacing, Headaches and PA1 in Saliva, and Headaches and BDNF in Saliva. Our preliminary results indicate that condyle imaging features may be more important in regards to main effects, but the fossa imaging features may have a larger contribution in terms of interaction effects. More studies are needed to optimize and further enhance machine-learning algorithms to detect early markers of disease, improve prediction of disease progression and severity to ultimately better serve clinical decision support systems in the treatment of patients with TMJ OA.

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.

Withdrawal

Raveendran, R, Perumbure, S, Nath, SG. Artificial intelligence: A newer vista in dentistry. Artif. Organs. 2021; 00:1-11. https://doi.org/10.1111/aor.14128. The above article, published online on December 6, 2021 in Wiley Online Library (wileyonlinelibrary.com), has been withdrawn by agreement between the journal Editor in Chief, Vakhtang Tchantchaleishvili, and John Wiley and Sons, Inc. The withdrawal has been agreed due to an editorial office error that led to the publication of the article without peer review. The authors were unaware of this error until notified by the editorial team and did not engage in any inappropriate or suspicious publishing conduct.

Piezo1 Affects Temporomandibular Joint Osteoarthritis by Influencing pSmad3

Objective: The aim of this research was to study the expression of Piezo1 in a rat temporomandibular joint osteoarthritis animal model and to explore its mechanism for inducing inflammatory changes. Methods: A total of 24male SD rats aged approximately 8 weeks were randomly divided into three groups: the blank control group, complete Freund's adjuvant group (CFA), and CFA + inhibitor (GsMTx4) group. After 3 weeks, the condylar heads of the rats were evaluated by micro-CT, HE, immunohistochemistry, safranin O staining, and other experimental techniques. Protein was extracted from the subchondral bone, and the changes in Piezo1, Smad3, and pSmad3 levels in each group were detected by Western blotting. p < 0.05 was considered to indicate statistical significance. Results: The degree of damage to the cartilage and subchondral bone in the Piezo1 inhibitor group was smaller than that in the CFA group. The expression level of Piezo1 in the CFA group was higher than that in the other groups, and the difference was statistically significant. The expression of pSmad3 in the CFA group was also higher than that in the other groups (p < 0.05). Conclusion: Piezo1 is expressed in the condylar cartilage and subchondral bone of rats, and the degree of condylar destruction can be improved by influencing the pSmad3 expression.

Artificial Intelligence in Dentistry-Narrative Review

Nowadays, artificial intelligence (AI) is becoming more important in medicine and in dentistry. It can be helpful in many fields where the human may be assisted and helped by new technologies. Neural networks are a part of artificial intelligence, and are similar to the human brain in their work and can solve given problems and make fast decisions. This review shows that artificial intelligence and the use of neural networks has developed very rapidly in recent years, and it may be an ordinary tool in modern dentistry in the near future. The advantages of this process are better efficiency, accuracy, and time saving during the diagnosis and treatment planning. More research and improvements are needed in the use of neural networks in dentistry to put them into daily practice and to facilitate the work of the dentist.

Introduction to artificial intelligence and machine learning into orthodontics: A review

Over the past few years, artificial intelligence (AI) and machine learning (ML) have revolutionized different healthcare branches, including dentistry. AI in a wider aspect means computers that mimic or behave like human intelligence whereas ML forms a part of AI and enables machines to increase their capabilities by the process of self-adapting algorithms. AI models’ basic principles or fundamentals are purely based on artificial neural networks or convolutional neural networks. This review focuses on giving a comprehensive and detailed explanation about AI and ML technology and their wide range of applications in various sections of orthodontic practice.

Similarities and differences between rat and mouse chondrocyte gene expression induced by IL-1β

Background

Osteoarthritis (OA) is the most prevalent degenerative joint disease. In vitro experiments are an intuitive method used to investigate its early pathogenesis. Chondrocyte inflammation models in rats and mice are often used as in vitro models of OA. However, similarities and differences between them in the early stages of inflammation have not been reported.

Objective

This paper seeks to compare the chondrocyte phenotype of rats and mice in the early inflammatory state and identify chondrocytes suitable for the study of early OA.

Methods

Under similar conditions, chondrocytes from rats and mice were stimulated using the same IL-1β concentration for a short period of time. The phenotypic changes of chondrocytes were observed under a microscope. The treated chondrocytes were subjected to RNA-seq to identify similarities and differences in gene expression. Chondrocytes were labelled with EdU for proliferation analysis. Cell proliferation-associated proteins, including minichromosome maintenance 2 (MCM2), minichromosome maintenance 5 (MCM5), Lamin B1, proliferating cell nuclear antigen (PCNA), and Cyclin D1, were analysed by immunocytochemical staining, cell immunofluorescence, and Western blots to verify the RNA-seq results.

Results

RNA-seq revealed that the expression patterns of cytokines, chemokines, matrix metalloproteinases, and collagen were similar between the rat and mouse chondrocyte inflammation models. Nonetheless, the expression of proliferation-related genes showed the opposite pattern. The RNA-seq results were further verified by subsequent experiments. The expression levels of MCM2, MCM5, Lamin B1, PCNA, and Cyclin D1 were significantly upregulated in rat chondrocytes (P < 0.05) and mouse chondrocytes (P < 0.05).

Conclusions

Based on the findings, the rat chondrocyte inflammation model may help in the study of the early pathological mechanism of OA.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13018-021-02889-2.

Automated segmentation of articular disc of the temporomandibular joint on magnetic resonance images using deep learning

Temporomandibular disorders are typically accompanied by a number of clinical manifestations that involve pain and dysfunction of the masticatory muscles and temporomandibular joint. The most important subgroup of articular abnormalities in patients with temporomandibular disorders includes patients with different forms of articular disc displacement and deformation. Here, we propose a fully automated articular disc detection and segmentation system to support the diagnosis of temporomandibular disorder on magnetic resonance imaging. This system uses deep learning-based semantic segmentation approaches. The study included a total of 217 magnetic resonance images from 10 patients with anterior displacement of the articular disc and 10 healthy control subjects with normal articular discs. These images were used to evaluate three deep learning-based semantic segmentation approaches: our proposed convolutional neural network encoder-decoder named 3DiscNet (Detection for Displaced articular DISC using convolutional neural NETwork), U-Net, and SegNet-Basic. Of the three algorithms, 3DiscNet and SegNet-Basic showed comparably good metrics (Dice coefficient, sensitivity, and positive predictive value). This study provides a proof-of-concept for a fully automated deep learning-based segmentation methodology for articular discs on magnetic resonance images, and obtained promising initial results, indicating that the method could potentially be used in clinical practice for the assessment of temporomandibular disorders.

VALUE OF ULTRASONOGRAPHY METHOD IN THE DIAGNOSIS OF TEMPOROMANDIBULAR DISORDERS AND PATIENT MANAGEMENT MONITORING. CASE REPORT

Value of ultrasonography method in the diagnosis of temporomandibular disorders and patient management monitoring is underestimated. Application of ultrasonography in the diagnosis of patients with temporomandibular disorders in many countries is limited. The main advantage and feature of the method is the ability to conduct examination in dynamics (during the function). Its safety and availability allow applying the method often and at different stages of diagnosis and treatment. So, ultrasonography fully satisfies the needs of primary diagnosing. MRI remains the recognized «gold standard» for the diagnosis of temporomandibular disorders. A case report which is presented, demonstrates the relevance and scope of diagnostic information obtained by ultrasonography, and their verification and clarification with the use of magnetic resonance imaging. Upon comparing the results of USG and MRI described in the clinical case report, we can conclude that ultrasound is quite sensitive and specific in diagnosing anterior disc displacement and blocked movement of the head of the mandible.

Radiographic interpretation using high-resolution Cbct to diagnose degenerative temporomandibular joint disease

The objective of this study was to use high-resolution cone-beam computed images (hr- CBCT) to diagnose degenerative joint disease in asymptomatic and symptomatic subjects using the Diagnostic Criteria for Temporomandibular Disorders DC/TMD imaging criteria. This observational study comprised of 92 subjects age-sex matched and divided into two groups: clinical degenerative joint disease (c-DJD, n = 46) and asymptomatic control group (n = 46). Clinical assessment of the DJD and high-resolution CBCT images (isotropic voxel size of 0.08mm) of the temporomandibular joints were performed for each participant. An American Board of Oral and Maxillofacial Radiology certified radiologist and a maxillofacial radiologist used the DC/TMD imaging criteria to evaluate the radiographic findings, followed by a consensus of the radiographic evaluation. The two radiologists presented a high agreement (Cohen's Kappa ranging from 0.80 to 0.87) for all radiographic findings (osteophyte, erosion, cysts, flattening, and sclerosis). Five patients from the c- DJD group did not present radiographic findings, being then classified as arthralgia. In the asymptomatic control group, 82.6% of the patients presented radiographic findings determinant of DJD and were then classified as osteoarthrosis or overdiagnosis. In conclusion, our results showed a high number of radiographic findings in the asymptomatic control group, and for this reason, we suggest that there is a need for additional imaging criteria to classify DJD properly in hr-CBCT images.

Automated cortical thickness measurement of the mandibular condyle head on CBCT images using a deep learning method

This study proposes a deep learning model for cortical bone segmentation in the mandibular condyle head using cone-beam computed tomography (CBCT) and an automated method for measuring cortical thickness with a color display based on the segmentation results. In total, 12,800 CBCT images from 25 normal subjects, manually labeled by an oral radiologist, served as the gold-standard. The segmentation model combined a modified U-Net and a convolutional neural network for target region classification. Model performance was evaluated using intersection over union (IoU) and the Hausdorff distance in comparison with the gold standard. The second automated model measured the cortical thickness based on a three-dimensional (3D) model rendered from the segmentation results and presented a color visualization of the measurements. The IoU and Hausdorff distance showed high accuracy (0.870 and 0.928 for marrow bone and 0.734 and 1.247 for cortical bone, respectively). A visual comparison of the 3D color maps showed a similar trend to the gold standard. This algorithm for automatic segmentation of the mandibular condyle head and visualization of the measured cortical thickness as a 3D-rendered model with a color map may contribute to the automated quantification of bone thickness changes of the temporomandibular joint complex on CBCT.

Applications of artificial intelligence and machine learning in orthodontics: a scoping review

Introduction

This scoping review aims to provide an overview of the existing evidence on the use of artificial intelligence (AI) and machine learning (ML) in orthodontics, its translation into clinical practice, and what limitations do exist that have precluded their envisioned application.

Methods

A scoping review of the literature was carried out following the PRISMA-ScR guidelines. PubMed was searched until July 2020.

Results

Sixty-two articles fulfilled the inclusion criteria. A total of 43 out of the 62 studies (69.35%) were published this last decade. The majority of these studies were from the USA (11), followed by South Korea (9) and China (7). The number of studies published in non-orthodontic journals (36) was more extensive than in orthodontic journals (26). Artificial Neural Networks (ANNs) were found to be the most commonly utilized AI/ML algorithm (13 studies), followed by Convolutional Neural Networks (CNNs), Support Vector Machine (SVM) (9 studies each), and regression (8 studies). The most commonly studied domains were diagnosis and treatment planning—either broad-based or specific (33), automated anatomic landmark detection and/or analyses (19), assessment of growth and development (4), and evaluation of treatment outcomes (2). The different characteristics and distribution of these studies have been displayed and elucidated upon therein.

Conclusion

This scoping review suggests that there has been an exponential increase in the number of studies involving various orthodontic applications of AI and ML. The most commonly studied domains were diagnosis and treatment planning, automated anatomic landmark detection and/or analyses, and growth and development assessment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40510-021-00361-9.

Strontium ranelate promotes chondrogenesis through inhibition of the Wnt/β-catenin pathway

BACKGROUND

Cartilage regeneration is a key step in functional reconstruction for temporomandibular joint osteoarthritis (TMJ-OA) but is a difficult issue to address. Strontium ranelate (SrR) is an antiosteoporosis drug that has been proven to affect OA in recent years, but its effect on chondrogenesis and the underlying mechanism are still unclear.

METHODS

Bone mesenchymal stem cells (BMSCs) from Sprague-Dawley (SD) rats were induced in chondrogenic differentiation medium with or without SrR, XAV-939, and LiCl. CCK-8 assays were used to examine cell proliferation, and alcian blue staining, toluidine blue staining, immunofluorescence, and PCR analysis were performed. Western blot (WB) analyses were used to assess chondrogenic differentiation of the cells. For an in vivo study, 30 male SD rats with cartilage defects on both femoral condyles were used. The defect sites were not filled, filled with silica nanosphere plus gelatine-methacryloyl (GelMA), or filled with SrR-loaded silica nanosphere plus GelMA. After 3 months of healing, paraffin sections were made, and toluidine blue staining, safranin O/fast green staining, and immunofluorescent or immunohistochemical staining were performed for histological evaluation. The data were analyzed by SPSS 26.0 software.

RESULTS

Low concentrations of SrR did not inhibit cell proliferation, and the cells treated with SrR (0.25 mmol/L) showed stronger chondrogenesis than the control. XAV-939, an inhibitor of β-catenin, significantly promoted chondrogenesis, and SrR did not suppress this effect, while LiCl, an agonist of β-catenin, strongly suppressed chondrogenesis, and SrR reversed this inhibitory effect. In vivo study showed a significantly better cartilage regeneration and a lower activation level of β-catenin by SrR-loaded GelMA than the other treatments.

CONCLUSION

SrR could promote BMSCs chondrogenic differentiation by inhibiting the Wnt/β-catenin signaling pathway and accelerate cartilage regeneration in rat femoral condyle defects.

Decision Support Systems in Temporomandibular Joint Osteoarthritis: A review of Data Science and Artificial Intelligence Applications

With the exponential growth of computational systems and increased patient data acquisition, dental research faces new challenges to manage a large quantity of information. For this reason, data science approaches are needed for the integrative diagnosis of multifactorial diseases, such as Temporomandibular joint (TMJ) Osteoarthritis (OA). The Data science spectrum includes data capture/acquisition, data processing with optimized web-based storage and management, data analytics involving in-depth statistical analysis, machine learning (ML) approaches, and data communication. Artificial intelligence (AI) plays a crucial role in this process. It consists of developing computational systems that can perform human intelligence tasks, such as disease diagnosis, using many features to help in the decision-making support. Patient's clinical parameters, imaging exams, and molecular data are used as the input in cross-validation tasks, and human annotation/diagnosis is also used as the gold standard to train computational learning models and automatic disease classifiers. This paper aims to review and describe AI and ML techniques to diagnose TMJ OA and data science approaches for imaging processing. We used a web-based system for multi-center data communication, algorithms integration, statistics deployment, and process the computational machine learning models. We successfully show AI and data-science applications using patients' data to improve the TMJ OA diagnosis decision-making towards personalized medicine.

The Modern and Digital Transformation of Oral Health Care: A Mini Review

Dentistry is a part of the field of medicine which is advocated in this digital revolution. The increasing trend in dentistry digitalization has led to the advancement in computer-derived data processing and manufacturing. This progress has been exponentially supported by the Internet of medical things (IoMT), big data and analytical algorithm, internet and communication technologies (ICT) including digital social media, augmented and virtual reality (AR and VR), and artificial intelligence (AI). The interplay between these sophisticated digital aspects has dramatically changed the healthcare and biomedical sectors, especially for dentistry. This myriad of applications of technologies will not only be able to streamline oral health care, facilitate workflow, increase oral health at a fraction of the current conventional cost, relieve dentist and dental auxiliary staff from routine and laborious tasks, but also ignite participatory in personalized oral health care. This narrative article review highlights recent dentistry digitalization encompassing technological advancement, limitations, challenges, and conceptual theoretical modern approaches in oral health prevention and care, particularly in ensuring the quality, efficiency, and strategic dental care in the modern era of dentistry.

Application of Artificial Intelligence in Dentistry

Artificial intelligence (AI) is a technology that utilizes machines to mimic intelligent human behavior. To appreciate human-technology interaction in the clinical setting, augmented intelligence has been proposed as a cognitive extension of AI in health care, emphasizing its assistive and supplementary role to medical professionals. While truly autonomous medical robotic systems are still beyond reach, the virtual component of AI, known as software-type algorithms, is the main component used in dentistry. Because of their powerful capabilities in data analysis, these virtual algorithms are expected to improve the accuracy and efficacy of dental diagnosis, provide visualized anatomic guidance for treatment, simulate and evaluate prospective results, and project the occurrence and prognosis of oral diseases. Potential obstacles in contemporary algorithms that prevent routine implementation of AI include the lack of data curation, sharing, and readability; the inability to illustrate the inner decision-making process; the insufficient power of classical computing; and the neglect of ethical principles in the design of AI frameworks. It is necessary to maintain a proactive attitude toward AI to ensure its affirmative development and promote human-technology rapport to revolutionize dental practice. The present review outlines the progress and potential dental applications of AI in medical-aided diagnosis, treatment, and disease prediction and discusses their data limitations, interpretability, computing power, and ethical considerations, as well as their impact on dentists, with the objective of creating a backdrop for future research in this rapidly expanding arena.

Overexpression of MMPs, cytokines, and RANKL/OPG in temporomandibular joint osteoarthritis and their association with joint pain, mouth opening, and bone degeneration: A preliminary report

OBJECTIVE

This study aimed to determine the expression of distinct matrix metalloproteinases, cytokines, and bone resorptive factors in temporomandibular joint osteoarthritis (TMJ-OA) patients and their association with joint pain, mouth opening, and subchondral bone degeneration.

MATERIALS AND METHODS

Twelve patients affected with TMJ-OA (n = 5), disk displacement without reduction (DDWoR) (n = 3), or disk displacement with reduction (DDWR) (n = 4) were selected. Joint pain was quantified by using visual analog scale, mouth opening was quantified at the maximum pain-free aperture, and bone degeneration was quantified using joint imaging. Synovial fluid samples were collected and immediately processed for cell and synovial fluid recovering. From cells, the MMP-1, MMP-2, MMP-8, MMP-13, IL-6, IL-23, and TNF-α expression was quantified by qPCR. From synovial fluid, the RANKL and OPG levels were quantified by ELISA.

RESULTS

Higher levels of MMP-1, MMP-8, MMP-13, IL-6, IL-23, TNF-α, and RANKL/OPG ratio were detected in TMJ-OA compared with DDWoR and DDWR patients (p < .05). Joint pain significantly correlated with TNF-α levels (r = .975, p = .029). Besides, imaging signs of bone degeneration significantly correlated with RANKL/OPG ratio (r = .949, p = .042). Conversely, mouth opening did not correlate with any of the analyzed mediators.

CONCLUSION

During TMJ-OA, a pathological response characterized by the overexpression of TNF-α and RANKL/OPG could be involved in joint pain and subchondral bone degeneration.

2D-3D comparison of the temporomandibular joint in skeletal Class II versus Class I adults: A retrospective study

OBJECTIVE

To compare the temporomandibular joint (TMJ) morphological characteristics in people with Class II versus Class I sagittal skeletal relationship and to identify other factors that influence the TMJ dimensions.

MATERIAL AND METHODS

This cross-sectional and retrospective study evaluated 188 people divided into two groups, 92 cone-beam computed tomographies (CBCTs) and lateral radiographs (LR) of people with Skeletal class II relationship with Class II division 1 malocclusion versus 96 CBCTs and LR of people with Class I skeletal relationship and Class I malocclusion (controls). The CBCTs included people of both sexes, aged between 15 and 65 years old. The 3D Imaging Carestream Software was used to evaluate the condyle height and neck width, mediolateral and anteroposterior condyle dimensions, the shape of the glenoid fossa and condyle in the CBCTs. Likewise, the ANB angle, the Wits appraisal and other measurements were evaluated on LR. Besides, Mann-Whitney U, Chi² and multiple linear regression tests were performed. The significance level was set at P˂0.05.

RESULTS

The mediolateral and anteroposterior condyle dimensions were smaller in class II people (1.82mm and 0.29mm, respectively) than class I people (P<0.05). Likewise, height and neck width of condyle were smaller in class II people (0.73mm and 0.40mm, respectively) than class I people (P<0.05). Multiple linear regression identified mainly the ANB angle as a factor (P<0.05) that influenced the dimensions, decreasing the condyle dimensions in skeletal class II relationship.

CONCLUSIONS

People with skeletal class II relationship showed smaller condyle dimension values than class I people. A decrease in the dimensions of the eminence and the condyle could be expected when the ANB angle increases.

Osteoarthritis of the Temporomandibular Joint can be diagnosed earlier using biomarkers and machine learning

After chronic low back pain, Temporomandibular Joint (TMJ) disorders are the second most common musculoskeletal condition affecting 5 to 12% of the population, with an annual health cost estimated at $4 billion. Chronic disability in TMJ osteoarthritis (OA) increases with aging, and the main goal is to diagnosis before morphological degeneration occurs. Here, we address this challenge using advanced data science to capture, process and analyze 52 clinical, biological and high-resolution CBCT (radiomics) markers from TMJ OA patients and controls. We tested the diagnostic performance of four machine learning models: Logistic Regression, Random Forest, LightGBM, XGBoost. Headaches, Range of mouth opening without pain, Energy, Haralick Correlation, Entropy and interactions of TGF-β1 in Saliva and Headaches, VE-cadherin in Serum and Angiogenin in Saliva, VE-cadherin in Saliva and Headaches, PA1 in Saliva and Headaches, PA1 in Saliva and Range of mouth opening without pain; Gender and Muscle Soreness; Short Run Low Grey Level Emphasis and Headaches, Inverse Difference Moment and Trabecular Separation accurately diagnose early stages of this clinical condition. Our results show the XGBoost + LightGBM model with these features and interactions achieves the accuracy of 0.823, AUC 0.870, and F1-score 0.823 to diagnose the TMJ OA status. Thus, we expect to boost future studies into osteoarthritis patient-specific therapeutic interventions, and thereby improve the health of articular joints.

Correlation of molecular biomarker concentrations between synovial fluid and saliva of the patients with temporomandibular disorders

OBJECTIVES

The synovial membrane and fluid are involved in the pathogenesis of temporomandibular joint (TMJ) disorders. This study aims to assess the relationship between matrix metalloproteinase-2 (MMP-2), chemerin and prostaglandin (PGE₂) levels in the synovial fluid (SF) and saliva of patients with TMJ disorder regarding their role in inflammation and the value of being a candidate for predictive biomarkers in the disease. Also, it is aimed to find out whether chemerin's main function triggers the formation inflammatory cytokine markers in the associated area.

MATERIALS AND METHODS

Thirty-two samples of SF and saliva were obtained from patients with disc displacement without reduction with limited opening (DDWORwLO). Mann-Whitney-U test was used for the comparisons of the biomarker levels in SF and saliva. The correlation between chemerin and BMI (Body Mass Index) is analyzed by non-parametric Spearman's rho correlation coefficient.

RESULTS

For all of the three biomarkers, statistically significant differences were found between SF and saliva. An unexpectedly high level expression of chemerin was observed in SF. A statistically significant, positive correlation was observed between PGE₂ -MMP-2, and chemerin-PGE₂ in saliva, chemerin and MMP-2 in SF, respectively (p = 0.031, r = 0.382 / p = 0.039, r = 0.366 / p = 0.032, r = 0.379). A positive correlation was determined between saliva and SF levels of PGE₂ (p = 0.016, r = 0.421).

CONCLUSIONS

Chemerin, MMP-2, and PGE₂ can play a role as an inflammatory factor for the development of TMJ disorder.

CLINICAL RELEVANCE

The search for molecular markers in TMJ and the inhibition of the associated molecular signaling mechanism is important to reduce joint inflammation and cartilage degradation.