Joint Vibration Analysis A Functonal Tool in Diagnosis of Temporomandibular Disorders – Case Reports

Abstract

Temporomandibular disorder (TMD) was a group of clinical symptoms involving masticatory muscles,or joint in oro facial region, or both. Evaluation of joint sounds was considered a key diagnostic criterion in determination of TMD. Temporomandibularjoint [TMJ] sounds have especially been studied because of their possible relation with an interfering mechanical condition (internal derangement) or a focal degenerative disorder (degenerative joint disease (DJD/ osteo arthrosis) of the TMJ. Joint Vibration Analysis [JVA] measures the joint vibrations through electrovibratography (EVG) is more reliable than physical examination alone and can serve as a non-invasive method for screening patients with TMDs. Case report: The aim of this report was to report cases of TMDS by comparing the diagnostic accuracy of JVA and MRI in patients with articular Disc Displacement with and without reduction. Conclusion: Analyzing joint noise using electrovibratography suggests the type of joint dysfunction and may help to establish a diagnosis, as well as a treatment plan where as MRI is a gold standard imaging for disc displacements and its usage is limited because its cost.

Repeatability of the Vibroarthrogram in the Temporomandibular Joints

“…For this classification setup, accuracy was 0.596 and 0.564 for the first and the second measurements, respectively. Low accuracy is to be expected, however, since the JVA decision tree [ 33 ] was defined for a completely different signal acquisition setup.…”

“…Designations can be found in the literature on the use of JVA features of vibroacoustic signals that best characterize individual TMJ pathologies. The research results indicate that the characteristics of vibroacoustic signals that best describe disk displacement with reduction (IIa according to the (R)DC/TMD) are: (1) high peak amplitude values [ 33 ], (2) total integral values above 80 Pa (even reaching 1000 Pa in sharp displacements) [ 33 ], (3) and high integral parameter below 300 Hz values [ 33 , 51 ].…”

“…To test the classification capabilities of the studied features, we constructed a simple k-nearest neighbors (KNN) classifier and compared it to the JVA decision tree classifier [ 33 ].…”

“…The built-in software determines features characterizing vibroacoustic signals on the basis of the frequency spectrum (features described in the following section). On the basis of these features, the classification of the TMJ pathology in the form of a JVA decision tree was developed [ 33 ].…”