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

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.

Kartogenin potentially protects temporomandibular joints from collagenase-induced osteoarthritis via core binding factor β and runt-related transcription factor 1 binding - A rat model study

Background/purpose

Temporomandibular joint (TMJ) osteoarthritis (TMJOA) is a chronic disease with progressive destruction of articular cartilage. This study aimed to explore the therapeutic effects of kartogenin on TMJOA via promoting the binding of core binding factor β (CBFβ) and runt-related transcription factor 1 (RUNX1).

Materials and methods

Type II collagenase was injected into 35 8-week-old male Sprague Dawley rat TMJs to establish the TMJOA model. Kartogenin, or the CBFβ-RUNX1 complex inhibitor (Ro5-3335), was also delivered via intra-articular injection. Subchondral bone was analyzed by MicroCT. The hematoxylin-eosin, Safranin O, and toluidine blue O staining were used to observe histopathology. Immunohistochemical staining of proliferating cell nuclear antigen (PCNA), caspase-3 (CASP3), interleukin-1β (IL-1β), and collagen II (COL2) was performed.

Results

TMJOA was established in rats by intra-articular injection of type II collagenase. The condylar cartilage and subchondral bone were damaged, with decreased PCNA and COL2 and increased CASP3 and IL-1 (P < .001). Compared with the OA group, kartogenin alleviated the destruction of cartilage and subchondral bone, rescued the expression of PCNA and COL2, and decreased the expression of CASP3 and IL-1β (P < .01). Ro5-3335 did not aggravate the pathology of TMJOA but neutralized the therapeutic effects of kartogenin on TMJOA.

Conclusion

Kartogenin has a potential therapeutic effect on TMJOA via promoting the CBFβ-RUNX1 binding.

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.

A systematic review of screening diagnostic tools for trigeminal neuralgia

Background and objective

Trigeminal neuralgia (TN) is a rare chronic neuropathic pain condition of sudden and severe pain, often described as an electric shock. Diagnosis is challenging for non-expert clinicians, particularly in primary care settings. We wanted to identify and assess the diagnostic accuracy of existing screening tools for TN and orofacial pain that could be used to support the diagnosis of TN in primary care.

Databases and data treatment

We searched key databases (MEDLINE, ASSIA, Embase, and Web of Knowledge and PsycINFO) supplemented by citation tracking from January 1988 to 2021. We used an adapted version of the Quality of Diagnostic Accuracy Studies (QUADAS-2) to assess the methodological quality of each study.

Results

Searches identified five studies, from the UK, USA and Canada; three validated self-report questionnaires; and two artificial neural networks. All screened for multiple orofacial pain diagnoses, including dentoalveolar pain, musculoskeletal pain (temporomandibular disorders) and neurological pain (trigeminal neuralgia, headache, atypical facial pain and postherpetic neuralgia). The overall quality assessment was low for one study.

Conclusions

Diagnosis of TN can be challenging for non-expert clinicians. Our review found few existing screening tools to diagnose TN, and none is currently suitable to be used in primary care settings. This evidence supports the need to adapt an existing tools or to create a new tool for this purpose. The development of an appropriate screening questionnaire could assist non-expert dental and medical clinicians to identify TN more effectively and empower them to manage or refer patients for treatment more effectively.

Design and Implement Strategy of Wireless Bite Force Device

Abnormal bite force is an important risk factor for oral and maxillofacial disorders, which is a critical dilemma that dentists face every day without effective solutions. Therefore, it is of great clinical significance to develop a wireless bite force measurement device and explore quantitative measurement methods to help find effective strategies for improving occlusal diseases. This study designed the open window carrier of a bite force detection device through 3D printing technology, and then the stress sensors were integrated and embedded into a hollow structure. The sensor system mainly consisted of a pressure signal acquisition module, a main control module, and a server terminal. A machine learning algorithm will be leveraged for bite force data processing and parameter configuration in the future. This study implemented a sensor prototype system from scratch to fully evaluate each component of the intelligent device. The experimental results showed reasonable parameter metrics for the device carrier and demonstrated the feasibility of the proposed scheme for bite force measurement. An intelligent and wireless bite force device with a stress sensor system is a promising approach to occlusal disease diagnosis and treatment.

Variables influencing the device-dependent approaches in digitally analysing jaw movement-a systematic review

BACKGROUND

To explore the digitisation of jaw movement trajectories through devices and discuss the physiological factors and device-dependent variables with their subsequent effects on the jaw movement analyses.

METHODS

Based on predefined eligibility criteria, the search was conducted following PRISMA-P 2015 guidelines on MEDLINE, EBSCO Host, Scopus, PubMed, and Web of Science databases in 2022 by 2 reviewers. Articles then underwent Cochrane GRADE approach and JBI critical appraisal for certainty of evidence and bias evaluation.

RESULTS

Thirty articles were included following eligibility screening. Both in vitro experiments (20%) and in vivo (80%) devices ranging from electronic axiography, electromyography, optoelectronic and ultrasonic, oral or extra-oral tracking, photogrammetry, sirognathography, digital pressure sensors, electrognathography, and computerised medical-image tracing were documented. 53.53% of the studies were rated below "moderate" certainty of evidence. Critical appraisal showed 80% case-control investigations failed to address confounding variables while 90% of the included non-randomised experimental studies failed to establish control reference.

CONCLUSION

Mandibular and condylar growth, kinematic dysfunction of the neuromuscular system, shortened dental arches, previous orthodontic treatment, variations in habitual head posture, temporomandibular joint disorders, fricative phonetics, and to a limited extent parafunctional habits and unbalanced occlusal contact were identified confounding variables that shaped jaw movement trajectories but were not highly dependent on age, gender, or diet. Realistic variations in device accuracy were found between 50 and 330 µm across the digital systems with very low interrater reliability for motion tracing from photographs. Forensic and in vitro simulation devices could not accurately recreate variations in jaw motion and muscle contractions.

Temporomandibular Joint Osteoarthritis Diagnosis Employing Artificial Intelligence: Systematic Review and Meta-Analysis

The aim was to systematically synthesize the current research and influence of artificial intelligence (AI) models on temporomandibular joint (TMJ) osteoarthritis (OA) diagnosis using cone-beam computed tomography (CBCT) or panoramic radiography. Seven databases (PubMed, Embase, Scopus, Web of Science, LILACS, ProQuest, and SpringerLink) were searched for TMJ OA and AI articles. We used QUADAS-2 to assess the risk of bias, while with MI-CLAIM we checked the minimum information about clinical artificial intelligence modeling. Two hundred and three records were identified, out of which seven were included, amounting to 10,077 TMJ images. Three studies focused on the diagnosis of TMJ OA using panoramic radiography with various transfer learning models (ResNet model) on which the meta-analysis was performed. The pooled sensitivity was 0.76 (95% CI 0.35-0.95) and the specificity was 0.79 (95% CI 0.75-0.83). The other studies investigated the 3D shape of the condyle and disease classification observed on CBCT images, as well as the numerous radiomics features that can be combined with clinical and proteomic data to investigate the most effective models and promising features for the diagnosis of TMJ OA. The accuracy of the methods was nearly equivalent; it was higher when the indeterminate diagnosis was excluded or when fine-tuning was used.

An artificially intelligent (or algorithm-enhanced) electronic medical record in orofacial pain

This review examines how a highly structured data collection system could be used to create data-driven diagnostic classification algorithms. Some preliminary data using this process is provided. The data collection system described is applicable to any clinical domain where the diagnoses being explored are based predominately on clinical history (subjective) and physical examination (objective) information. The system has been piloted and refined using patient encounters collected in a clinic specializing in Orofacial Pain treatment. In summary, whether you believe a branching hybrid check-box based data collection system with built-in algorithms is needed, depends on your individual agenda. If you have no plans for data analysis or publishing about the various phenotypes discovered and you do not need pop-up suggestions for best diagnosis and treatment options, it is easier to use a semi-structured narrative note for your patient encounters. If, however, you want data-driven diagnostic and disease risk algorithms and pop-up best-treatment options, then you need a highly structured data collection system that is compatible with machine learning analysis. Automating the journey from data collection to diagnoses has the potential to improve standards of care by providing faster and reliable predictions.