Using deep learning to predict temporomandibular joint disc perforation based on magnetic resonance imaging

Relationship between temporomandibular joint space and articular disc displacement

OBJECTIVE

Analyse the correlation between the changes in joint space of TMJ and the displacement and degree of articular disc for clinical diagnosis.

METHODS

Two hundred sixteen TMJs of 108 temporomandibular disorders (TMD) patients with clinical symptoms and MRI examination were included in the study. 30 of these patients had undergone CBCT before MRI. According to the degree of articular disc displacement, the 216 joints are divided into five groups. Group A: no disc displacement (40 cases); group B: mild anterior disc displacement (44 cases); group C: moderate anterior disc displacement (36 cases); group D: severe anterior disc displacement (52 cases); group E: posterior displacement (44 cases). The 132 sides of these anteriorly displaced discs (ADD) were further divided into two groups, anterior disc displacement with reduction (ADDwR) and anterior disc displacement without reduction (ADDwoR). We analysed the concordance of the joint space measured by MRI and CBCT, and explored the relationship between joint space, ln(P/A) values and joint disc displacement.

RESULTS

There was no statistically significant difference between the joint spaces measured by CBCT and MRI (P > 0.05). The anterior joint space in group B (2.7 ± 0.72 mm) and C (2.82 ± 0.88 mm) was larger than group A (1.82 ± 0.50 mm) (P < 0.05), and ln(P/A) value in group B (-0.52 ± 0.34) and C (-0.62 ± 0.43) was smaller than group A (0.04 ± 0.15) (P < 0.05). The posterior joint space (3.33 ± 1.28 mm) and ln(P/A) value (0.74 ± 0.33) in group E was larger than group A (P < 0.05). There was no significant difference in the anterior, superior and posterior joint space and ln(P/A) value between group D and A (P > 0.05). The ADDwR group had a larger anterior joint space (2.72 ± 0.83 mm) than group A (P < 0.05), while having a smaller posterior joint space (1.61 ± 0.49 mm) and ln(P/A) value (-0.52 ± 0.39 mm) (P < 0.05). Compared with group A, there was no significant difference in the anterior joint space and ln(P/A) value in the ADDwoR group(P > 0.05).

CONCLUSION

There is no significant change in anterior, supra, and posterior joint space in severe anterior disc displacement. The anterior joint space increases in mild to moderate anterior disc displacement, but does not change in severe anterior disc displacement-the posterior joint space increases when the joint disc is displaced posteriorly. The position of the joint disc cannot be accurately inferred by observing the joint space through CBCT, and a combination of MRI and clinical examination is required to make a definitive judgement.

Automated pediatric TMJ articular disk identification and displacement classification in MRI with machine learning

OBJECTIVE

To evaluate the performance of an automated two-step model interpreting pediatric temporomandibular joint (TMJ) magnetic resonance imaging (MRI) using artificial intelligence (AI). Using deep learning techniques, the model first automatically identifies the disk and the TMJ osseous structures, and then an automated algorithm classifies disk displacement.

MATERIALS AND METHODS

MRI images of the TMJ from 235 pediatric patients (470 joints) were reviewed. TMJ structures were segmented, and the disk position was classified as dislocated or not dislocated. The UNet++ model was trained on MRI images from 135 and tested on images from 100 patients. Disk displacement was then classified by an automated algorithm assessing the location of disk centroid and surfaces for bone landmarks.

RESULTS

The mean age was 14.6 ± 0.1 years (Female: 138/235, 58 %), with 104 of 470 disks (22 %) anteriorly dislocated. UNet++ performed well in segmenting the TMJ anatomical structures, with a Dice coefficient of 0.67 for the disk, 0.91 for the condyle, and a Hausdorff distance of 2.8 mm for the articular eminence. The classification algorithm showed disk displacement classification comparable to human experts, with an AUC of 0.89-0.92 for the distance between the disk center and the eminence-condyle line.

CONCLUSION

A two-step automated model can accurately identify TMJ osseous structures and classify disk dislocation in pediatric TMJ MRI. This tool could assist clinicians who are not MRI experts when assessing pediatric TMJ disorders.

CLINICAL SIGNIFICANCE

Automated software that assists in locating the articular disk and surrounding structures and classifies disk displacement would improve the TMJ-MRI interpretation and the assessment of TMJ disorders in children.

Automated diagnosis and classification of temporomandibular joint degenerative disease via artificial intelligence using CBCT imaging

OBJECTIVES

In this study, artificial intelligence (AI) techniques were used to achieve automated diagnosis and classification of temporomandibular joint (TMJ) degenerative joint disease (DJD) on cone beam computed tomography (CBCT) images.

METHODS

An AI model utilizing the YOLOv10 algorithm was trained, validated and tested on 7357 annotated and corrected oblique sagittal TMJ images (3010 images of normal condyles and 4347 images of condyles with DJD) from 1018 patients who visited Peking University School and Hospital of Stomatology for temporomandibular disorders and underwent TMJ CBCT examinations. This model could identify DJD as well as the radiographic signs of DJD, namely, erosion, osteophytes, sclerosis and subchondral cysts. The diagnosis and classification performances of the model were evaluated on the test set. The accuracy of the model for evaluating images with one to four DJD signs was also evaluated.

RESULTS

The accuracy, precision, sensitivity, specificity, F1 score and mean average precision (mAP) at an intersection over union (IoU) threshold of 0.5 of the model for DJD detection all exceeded 0.95. The accuracies for identifying erosion, osteophytes, sclerosis and subchondral cysts were 0.91, 0.96, 0.91 and 0.96, respectively. The precisions, specificities and F1 scores for the DJD signs were all >0.90. The sensitivity ranged from 0.88 to 0.95, and the mAP (IoU=0.5) ranged from 0.87 to 0.97. The accuracies of the model for detecting one to four DJD signs in one image were 94 %, 84 %, 66 % and 63 %, respectively.

CONCLUSIONS

A deep learning model based on the YOLOv10 algorithm can not only detect the presence of TMJ DJD on CBCT images but also differentiate the typical radiographic signs of DJD, including erosion, osteophytes, sclerosis and subchondral cysts, with acceptable accuracy.

CLINICAL SIGNIFICANCE

TMJ DJD is a very common disease that causes joint pain and mandibular dysfunction and affects patients' quality of life; therefore, early diagnosis and intervention are particularly important. However, identifying radiographic signs of early-stage TMJ DJD is difficult. AI can quickly review CBCT images and assist in the accurate and rapid diagnosis and classification of TMJ DJD.

The current landscape of artificial intelligence in oral and maxillofacial surgery- a narrative review

OBJECTIVE

This narrative review aims to explore the current applications and future prospects of AI within the subfields of oral and maxillofacial surgery (OMS), emphasizing its potential benefits and anticipated challenges.

METHODS

A detailed review of the literature was conducted to evaluate the role of AI in oral and maxillofacial surgery. All domains within OMS were reviewed with a focus on diagnostic, therapeutic and prognostic interventions.

RESULTS

AI has been successfully integrated into surgical specialties to enhance clinical outcomes. In OMS, AI demonstrates potential to improve clinical and administrative workflows in both ambulatory and hospital-based settings. Notable applications include more accurate risk prediction, minimally invasive surgical techniques, and optimized postoperative management.

CONCLUSION

OMS stands to benefit enormously from the integration of AI. However, significant roadblocks, such as ethical concerns, data security, and integration challenges, must be addressed to ensure effective adoption. Further research and innovation are needed to fully realize the potential of AI in this specialty.

Temporomandibular joint assessment in MRI images using artificial intelligence tools: where are we now? A systematic review

OBJECTIVES

To summarize the current evidence on the performance of artificial intelligence (AI) algorithms for the temporomandibular joint (TMJ) disc assessment and TMJ internal derangement diagnosis in magnetic resonance imaging (MRI) images.

METHODS

Studies were gathered by searching 5 electronic databases and partial grey literature up to May 27, 2024. Studies in humans using AI algorithms to detect or diagnose internal derangements in MRI images were included. The methodological quality of the studies was evaluated using the Quality Assessment Tool for Diagnostic of Accuracy Studies-2 (QUADAS-2) and a proposed checklist for dental AI studies.

RESULTS

Thirteen studies were included in this systematic review. Most of the studies assessed disc position. One study assessed disc perforation. A high heterogeneity related to the patient selection domain was found between the studies. The studies used a variety of AI approaches and performance metrics with CNN-based models being the most used. A high performance of AI models compared to humans was reported with accuracy ranging from 70% to 99%.

CONCLUSIONS

The integration of AI, particularly deep learning, in TMJ MRI, shows promising results as a diagnostic-assistance tool to segment TMJ structures and classify disc position. Further studies exploring more diverse and multicentre data will improve the validity and generalizability of the models before being implemented in clinical practice.

Deep learning-based automated diagnosis of temporomandibular joint anterior disc displacement and its clinical application

Introduction

This study aimed to develop a deep learning-based method for interpreting magnetic resonance imaging (MRI) scans of temporomandibular joint (TMJ) anterior disc displacement (ADD) and to formulate an automated diagnostic system for clinical practice.

Methods

The deep learning models were utilized to identify regions of interest (ROI), segment TMJ structures including the articular disc, condyle, glenoid fossa, and articular tubercle, and classify TMJ ADD. The models employed Grad-CAM heatmaps and segmentation annotation diagrams for visual diagnostic predictions and were deployed for clinical application. We constructed four deep-learning models based on the ResNet101_vd framework utilizing an MRI dataset of 618 TMJ cases collected from two hospitals (Hospitals SS and SG) and a dataset of 840 TMJ MRI scans from October 2022 to July 2023. The training and validation datasets included 700 images from Hospital SS, which were used to develop the models. Model performance was assessed using 140 images from Hospital SS (internal validity test) and 140 images from Hospital SG (external validity test). The first model identified the ROI, the second automated the segmentation of anatomical components, and the third and fourth models performed classification tasks based on segmentation and non-segmentation approaches. MRI images were classified into four categories: normal (closed mouth), ADD (closed mouth), normal (open mouth), and ADD (open mouth). Combined findings from open and closed-mouth positions provided conclusive diagnoses. Data augmentation techniques were used to prevent overfitting and enhance model robustness. The models were assessed using performance metrics such as precision, recall, mean average precision (mAP), F1-score, Matthews Correlation Coefficient (MCC), and confusion matrix analysis.

Results

Despite lower performance with Hospital SG's data than Hospital SS's, both achieved satisfactory results. Classification models demonstrated high precision rates above 92%, with the segmentation-based model outperforming the non-segmentation model in overall and category-specific metrics.

Discussion

In summary, our deep learning models exhibited high accuracy in detecting TMJ ADD and provided interpretable, visualized predictive results. These models can be integrated with clinical examinations to enhance diagnostic precision.

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.

Correlation Between Temporomandibular Joint Disc Perforation and Degenerative Joint Changes: A CBCT and Clinical Analysis

OBJECTIVES

To analyse the correlation between temporomandibular joint (TMJ) disc perforation and degenerative joint changes (DJC) on cone-beam computed tomography (CBCT) and related factors.

STUDY DESIGN

A total of 238 female patients with anterior disc displacement without reduction (ADDwoR), accounting for 348 affected joints, requiring TMJ disc open anchorage surgery were included in the study conducted from June 2021 to August 2022. Following TMJ disc open anchorage surgery, patients were divided into two groups: disc perforation (DP) and disc non-perforation (DNP). CBCT was utilised to assess different grades of condyle and articular eminence degenerative changes, and comparisons were made between the two groups regarding DJC and clinically relevant factors.

RESULTS

In comparison with the DNP group, the DP group exhibited statistically significant differences in mid- and late-stage condyle bone degenerative changes and bone alterations of the articular eminence (odds ratio [OR] = 7.822; 95% CI [4.438-13.785]; p < 0.001 and OR = 5.575; 95% CI [3.128-9.936]; p < 0.001). Additionally, persistent joint sounds (OR = 1.932; 95% CI [1.011-3.691]; p = 0.046) and longer disease duration (OR = 4.901; 95% CI [2.395-10.028]; p < 0.001) demonstrated statistically significant differences. However, no significant differences were observed between the two groups in terms of age, joint pain and limited mouth opening.

CONCLUSIONS

Bone degeneration changes in TMJ CBCT images are a high possible risk factor for DP. With an escalation in the degree of condyle degeneration, the risk of DP may increased correspondingly. Persistent joint sounds and extended duration of the disease were also confirmed to be noteworthy clinical risks of DP.

Applications of artificial intelligence in dentomaxillofacial imaging: a systematic review

BACKGROUND

Artificial intelligence (AI) technology has been increasingly developed in oral and maxillofacial imaging. The aim of this systematic review was to assess the applications and performance of the developed algorithms in different dentomaxillofacial imaging modalities.

STUDY DESIGN

A systematic search of PubMed and Scopus databases was performed. The search strategy was set as a combination of the following keywords: "Artificial Intelligence," "Machine Learning," "Deep Learning," "Neural Networks," "Head and Neck Imaging," and "Maxillofacial Imaging." Full-text screening and data extraction were independently conducted by two independent reviewers; any mismatch was resolved by discussion. The risk of bias was assessed by one reviewer and validated by another.

RESULTS

The search returned a total of 3,392 articles. After careful evaluation of the titles, abstracts, and full texts, a total number of 194 articles were included. Most studies focused on AI applications for tooth and implant classification and identification, 3-dimensional cephalometric landmark detection, lesion detection (periapical, jaws, and bone), and osteoporosis detection.

CONCLUSION

Despite the AI models' limitations, they showed promising results. Further studies are needed to explore specific applications and real-world scenarios before confidently integrating these models into dental practice.

An Examination of Temporomandibular Joint Disc Displacement through Magnetic Resonance Imaging by Integrating Artificial Intelligence: Preliminary Findings

Background and Objectives: This research was aimed at constructing a complete automated temporomandibular joint disc position identification system that could assist with magnetic resonance imaging disc displacement diagnosis on oblique sagittal and oblique coronal images. Materials and Methods: The study included fifty subjects with magnetic resonance imaging scans of the temporomandibular joint. Oblique sagittal and coronal sections of the magnetic resonance imaging scans were analyzed. Investigations were performed on the right and left coronal images with a closed mouth, as well as right and left sagittal images with closed and open mouths. Three hundred sagittal and coronal images were employed to train the artificial intelligence algorithm. Results: The accuracy ratio of the completely computerized articular disc identification method was 81%. Conclusions: An automated and accurate evaluation of temporomandibular joint disc position was developed by using both oblique sagittal and oblique coronal magnetic resonance imaging images.

Multi-class segmentation of temporomandibular joint using ensemble deep learning

Temporomandibular joint disorders are prevalent causes of orofacial discomfort. Diagnosis predominantly relies on assessing the configuration and positions of temporomandibular joint components in magnetic resonance images. The complex anatomy of the temporomandibular joint, coupled with the variability in magnetic resonance image quality, often hinders an accurate diagnosis. To surmount this challenge, we developed deep learning models tailored to the automatic segmentation of temporomandibular joint components, including the temporal bone, disc, and condyle. These models underwent rigorous training and validation utilizing a dataset of 3693 magnetic resonance images from 542 patients. Upon evaluation, our ensemble model, which combines five individual models, yielded average Dice similarity coefficients of 0.867, 0.733, 0.904, and 0.952 for the temporal bone, disc, condyle, and background class during internal testing. In the external validation, the average Dice similarity coefficients values for the temporal bone, disc, condyle, and background were 0.720, 0.604, 0.800, and 0.869, respectively. When applied in a clinical setting, these artificial intelligence-augmented tools enhanced the diagnostic accuracy of physicians, especially when discerning between temporomandibular joint anterior disc displacement and osteoarthritis. In essence, automated temporomandibular joint segmentation by our deep learning approach, stands as a promising aid in refining temporomandibular joint disorders diagnosis and treatment strategies.

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.

Explainable deep learning and biomechanical modeling for TMJ disorder morphological risk factors

Clarifying multifactorial musculoskeletal disorder etiologies supports risk analysis, development of targeted prevention, and treatment modalities. Deep learning enables comprehensive risk factor identification through systematic analyses of disease data sets but does not provide sufficient context for mechanistic understanding, limiting clinical applicability for etiological investigations. Conversely, multiscale biomechanical modeling can evaluate mechanistic etiology within the relevant biomechanical and physiological context. We propose a hybrid approach combining 3D explainable deep learning and multiscale biomechanical modeling; we applied this approach to investigate temporomandibular joint (TMJ) disorder etiology by systematically identifying risk factors and elucidating mechanistic relationships between risk factors and TMJ biomechanics and mechanobiology. Our 3D convolutional neural network recognized TMJ disorder patients through participant-specific morphological features in condylar, ramus, and chin. Driven by deep learning model outputs, biomechanical modeling revealed that small mandibular size and flat condylar shape were associated with increased TMJ disorder risk through increased joint force, decreased tissue nutrient availability and cell ATP production, and increased TMJ disc strain energy density. Combining explainable deep learning and multiscale biomechanical modeling addresses the "mechanism unknown" limitation undermining translational confidence in clinical applications of deep learning and increases methodological accessibility for smaller clinical data sets by providing the crucial biomechanical context.

Image preprocessing with contrast-limited adaptive histogram equalization improves the segmentation performance of deep learning for the articular disk of the temporomandibular joint on magnetic resonance images

OBJECTIVES

The objective was to evaluate the robustness of deep learning (DL)-based encoder-decoder convolutional neural networks (ED-CNNs) for segmenting temporomandibular joint (TMJ) articular disks using data sets acquired from 2 different 3.0-T magnetic resonance imaging (MRI) scanners using original images and images subjected to contrast-limited adaptive histogram equalization (CLAHE).

STUDY DESIGN

In total, 536 MR images from 49 individuals were examined. An expert orthodontist identified and manually segmented the disks in all images, which were then reviewed by another expert orthodontist and 2 expert oral and maxillofacial radiologists. These images were used to evaluate a DL-based semantic segmentation approach using an ED-CNN. Original and preprocessed CLAHE images were used to train and validate the models whose performances were compared.

RESULTS

Original and CLAHE images acquired on 1 scanner had pixel values that were significantly darker and with lower contrast. The values of 3 metrics-the Dice similarity coefficient, sensitivity, and positive predictive value-were low when the original MR images were used for model training and validation. However, these metrics significantly improved when images were preprocessed with CLAHE.

CONCLUSIONS

The robustness of the ED-CNN model trained on a dataset obtained from a single device is low but can be improved with CLAHE preprocessing. The proposed system provides promising results for a DL-based, fully automated segmentation method for TMJ articular disks on MRI.

Perforation of the temporomandibular joint disc: cadaveric anatomical study

The aim of this human cadaveric study was to investigate the relationship between temporomandibular joint disc perforation and bony changes of the mandibular condyle. Overall, 135 cadaveric mandibles were used (69 male, 66 female; all White). Mean age at death was 78.7 years. Perforation of the disc was investigated. Differences in the area of the perforation according to the different types of bony change (erosion, flattening, osteophyte) were evaluated. Perforation of the disc was observed in 34.8% of all mandibles, occurring unilaterally in 53.2% of cases and bilaterally in 46.8%. The prevalence of perforation was 16.4% in cadavers <80 years old (67 heads) and 52.9% in those ≥80 years old (68 heads) (P < 0.001). Osteophyte formation was always identified along with other bony changes (21.7%) and never in isolation. The area of the perforation was significantly larger in the groups with bony changes (one, two, or three changes) than in the 'no bony change' group. The group with osteophyte formation showed a significantly larger perforated area than the group without osteophyte formation; likewise, the group with flattening showed a significantly larger perforated area than the group without flattening. Osteophytes and flattening are probably secondary bony changes that occur following disc perforation. Based on this study, disc perforation should be suspected when these findings are identified on imaging.

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.

Specific tissue engineering for temporomandibular joint disc perforation

BACKGROUND

The temporomandibular joint (TMJ) disc is a critical fibrocartilaginous structure with limited regenerative capacity in the oral system. Perforation of the TMJ disc can lead to osteoarthritis and ankylosis of the TMJ because of the lack of disc protection. Clinical treatments for TMJ disc perforation, such as discectomy, hyaluronic acid injection, endoscopic surgery and high position arthroplasty of TMJ, are questionable with regard to long-term outcomes, and only three fourths of TMJ disc perforations are repairable by surgery, even in the short-term. Tissue engineering offers the potential for cure of repairable TMJ disc perforations and regeneration of unrepairable ones.

OBJECTIVES

This review discusses the classification of TMJ disc perforation and defines typical TMJ disc perforation. Advancements in the engineering-based repair of TMJ disc perforation by stem cell therapy, construction of a disc-like scaffold and functionalization by offering bioactive stimuli are also summarized in the review, and the barriers developing engineering technologies need to overcome to be popularized are discussed.

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.

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.

Intelligent systems for precision dental diagnosis and treatment planning – A review

Machines have changed the course of mankind. Simple machines were the basis of human civilization. Today with humongous technological development, machines are intelligent enough to carry out very complex nerve-racking tasks. The ability of a machine to learn from algorithms changed eventually into, the machine learning by itself, which constitutes artificial intelligence. Literature has plausible evidence for the use of intelligent systems in medical field. Artificial intelligence has been used in the multiple denominations of dentistry. These machines are used in the precision diagnosis, interpretation of medical images, accumulation of data, classification and compilation of records, determination of treatment and construction of a personalized treatment plan. Artificial intelligence can help in timely diagnosis of complex dental diseases which would ultimately aid in rapid commencement of treatment. Research helps us understand the effectiveness and challenges in the use of this technology. The apt use of intelligent systems could transform the entire medical system for the better.