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Background: Referred pain often complicates and delays the diagnosis of temporomandibular disorders (TMD). Elaborating the prevalence and characteristics of TMD-associated referred pain as well as the distribution of referred pain in different TMD classes will significantly improve the diagnostic process. The objectives of the present study were to...
Context in source publication
Context 1
... pain was recorded in 153 patients (60.7%). The highest prevalence of referred pain was seen in the temporal area (45.2%), followed by the ear (42.1%), neck (19.0%) and forehead (17.5%) (Figure 1). The prevalence of referred pain felt at more than one location in the same patient was 46.43% (117 patients). ...
Citations
... The device is attached to the upper and lower teeth and is designed to advance the mandible forward, thereby correcting the malocclusion. However, there have been reports of temporomandibular disorders (TMD) associated with using the Herbst appliance [1,2]. ...
Background
The Herbst appliance is an excellent therapy for treating class II malocclusions with increased overjet. Its mechanics involve propelling the mandibular bone using two pistons the patient cannot remove. The so-called bite-jumping keeps the mandible in a more anterior position for a variable period, usually at least 6 months. This appliance does not inhibit joint functions and movements, although there are scientific papers in the literature investigating whether this appliance can lead to temporomandibular disorders. This systematic review aims to evaluate whether Herbst’s device can cause temporomandibular diseases by assessing the presence of TMD in patients before and after treatment.
Methods
A literature search up to 3 May 2023 was carried out on three online databases: PubMed, Scopus and Web of Science. Only studies that evaluated patients with Helkimo scores and Manual functional analysis were considered, as studies that assessed the difference in TMD before and after Herbst therapy. Review Manager version 5.2.8 (Cochrane Collaboration) was used for the pooled analysis. We measured the odds ratio (OR) between the two groups (pre and post-Herbst).
Results
The included papers in this review were 60. Fifty-seven were excluded. In addition, a manual search was performed. After the search phase, four articles were considered in the study, one of which was found through a manual search. The overall effect showed that there was no difference in TMD prevalence between pre-Herbst and post-Herbst therapy (OR 0.74; 95% CI: 0.33–1.68).
Conclusion
Herbst appliance seems not to lead to an increase in the incidence of TMD in treated patients; on the contrary, it appears to decrease it. Further studies are needed to assess the possible influence of Herbst on TMDs.
... It intensifies during mastication and might be accompanied by swelling (35) . In contrast, referred pain often originates remotely, such as in the temporomandibular joint or sinuses, and migrates to adjacent regions (36) . Referred pain might result from jaw movement or sinus congestion, and its intensity can vary. ...
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.
