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

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.

Automatic segmentation and visualization of cortical and marrow bone in mandibular condyle on CBCT: a preliminary exploration of clinical application

OBJECTIVES

To develop a deep learning-based automatic segmentation method for cortex and marrow in mandibular condyle on cone-beam computed tomography (CBCT) images and explore its clinical application.

METHODS

825 condyles of 490 CBCT images from 3 centers of Stomatology hospital affliated to Zhejiang University School of Medicine were collected. A deep learning model was developed for simultaneous segmentation of cortex and marrow in mandibular condyle. It included a region of interest extraction network and a segmentation network based on 3D U-net, with modifications made to improve the segmentation boundaries. To evaluate its clinical potential, the model's segmentation efficiency and accuracy were compared with those of both junior and senior oral and maxillofacial radiologists. Additionally, the model's ability to assist junior radiologists in diagnosis through visualization and quantitative analysis of the generated 3D model was also assessed.

RESULTS

The Dice similarity coefficient of the deep learning model was 0.901 (cortex), 0.969 (marrow), and 0.982 (entire condyle). Hausdorff distance was 0.755 mm (cortex), 0.826 mm (marrow), and 0.760 mm (entire condyle). The model outperformed radiologists across all segmentation metrics, completing the task in merely 15.06 s. With the assistance of visualization and quantitative analysis generated from the model's segmentation, the diagnostic accuracy of junior radiologists significantly improved.

CONCLUSIONS

The proposed deep learning-based model achieved accurate and efficient segmentation for mandibular condylar cortex and marrow. It possessed capability to generate precise 3D models, facilitating visual quantitative measurement and aiding in the diagnosis of condylar bony changes. This model holds potential for clinical applications in orthognathic surgery, orthodontic treatment, and other TMJ-related interventions.

Empowering Modern Dentistry: The Impact of Artificial Intelligence on Patient Care and Clinical Decision Making

Advancements in artificial intelligence (AI) are poised to catalyze a transformative shift across diverse dental disciplines including endodontics, oral radiology, orthodontics, pediatric dentistry, periodontology, prosthodontics, and restorative dentistry. This narrative review delineates the burgeoning role of AI in enhancing diagnostic precision, streamlining treatment planning, and potentially unveiling innovative therapeutic modalities, thereby elevating patient care standards. Recent analyses corroborate the superiority of AI-assisted methodologies over conventional techniques, affirming their capacity for personalization, accuracy, and efficiency in dental care. Central to these AI applications are convolutional neural networks and deep learning models, which have demonstrated efficacy in diagnosis, prognosis, and therapeutic decision making, in some instances surpassing traditional methods in complex cases. Despite these advancements, the integration of AI into clinical practice is accompanied by challenges, such as data security concerns, the demand for transparency in AI-generated outcomes, and the imperative for ongoing validation to establish the reliability and applicability of AI tools. This review underscores the prospective benefits of AI in dental practice, envisioning AI not as a replacement for dental professionals but as an adjunctive tool that fortifies the dental profession. While AI heralds improvements in diagnostics, treatment planning, and personalized care, ethical and practical considerations must be meticulously navigated to ensure responsible development of AI in dentistry.

Perspective: Advancing the science regarding temporomandibular disorders

This Special Issue was initiated in response to the call for improved research by the National Academies of Sciences, Engineering, and Medicine (NASEM) (United States) Consensus Study Report on Temporomandibular Disorders (TMDs), a set of putatively localized musculoskeletal conditions. In this Special Issue, the importance of systems biology for TMDs emerges from each of three separate publications. The importance of systems biology to patients is anchored in two domains-laboratory research and clinical observation. The three publications fully speak to the underlying goals in the NASEM recommendations for initiatives: that research on TMDs needs to broaden, that integration between basic and clinical science needs to improve, and that while better evidence is needed, clinicians need to utilize the evidence that already exists. All three of these initiatives, taken together, would lead to better understanding of these complex diseases and to better care of patients with these diseases.

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.

A comprehensive patient-specific prediction model for temporomandibular joint osteoarthritis progression

Temporomandibular joint osteoarthritis (TMJ OA) is a prevalent degenerative disease characterized by chronic pain and impaired jaw function. The complexity of TMJ OA has hindered the development of prognostic tools, posing a significant challenge in timely, patient-specific management. Addressing this gap, our research employs a comprehensive, multidimensional approach to advance TMJ OA prognostication. We conducted a prospective study with 106 subjects, 74 of whom were followed up after 2 to 3 y of conservative treatment. Central to our methodology is the development of an innovative, open-source predictive modeling framework, the Ensemble via Hierarchical Predictions through Nested cross-validation tool (EHPN). This framework synergistically integrates 18 feature selection, statistical, and machine learning methods to yield an accuracy of 0.87, with an area under the ROC curve of 0.72 and an F1 score of 0.82. Our study, beyond technical advancements, emphasizes the global impact of TMJ OA, recognizing its unique demographic occurrence. We highlight key factors influencing TMJ OA progression. Using SHAP analysis, we identified personalized prognostic predictors: lower values of headache, lower back pain, restless sleep, condyle high gray level-GL-run emphasis, articular fossa GL nonuniformity, and long-run low GL emphasis; and higher values of superior joint space, mouth opening, saliva Vascular-endothelium-growth-factor, Matrix-metalloproteinase-7, serum Epithelial-neutrophil-activating-peptide, and age indicate recovery likelihood. Our multidimensional and multimodal EHPN tool enhances clinicians' decision-making, offering a transformative translational infrastructure. The EHPN model stands as a significant contribution to precision medicine, offering a paradigm shift in the management of temporomandibular disorders and potentially influencing broader applications in personalized healthcare.

Call for algorithmic fairness to mitigate amplification of racial biases in artificial intelligence models used in orthodontics and craniofacial health

Machine Learning (ML), a subfield of Artificial Intelligence (AI), is being increasingly used in Orthodontics and craniofacial health for predicting clinical outcomes. Current ML/AI models are prone to accentuate racial disparities. The objective of this narrative review is to provide an overview of how AI/ML models perpetuate racial biases and how we can mitigate this situation. A narrative review of articles published in the medical literature on racial biases and the use of AI/ML models was undertaken. Current AI/ML models are built on homogenous clinical datasets that have a gross underrepresentation of historically disadvantages demographic groups, especially the ethno-racial minorities. The consequence of such AI/ML models is that they perform poorly when deployed on ethno-racial minorities thus further amplifying racial biases. Healthcare providers, policymakers, AI developers and all stakeholders should pay close attention to various steps in the pipeline of building AI/ML models and every effort must be made to establish algorithmic fairness to redress inequities.