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

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.

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.

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.