When a dysfunction occurs in any component of the stomatognathic system, temporomandibular disorders (TMD) may originate. The aim of this study was to compare the deviations, displacement and the execution speed of mandibular movements among asymptomatic participants and those with TMD. Convenience sampling was used; forty participants diagnosed by clinical evaluation following the Research Diagnostic Criteria for Temporomandibular Disorders were divided into three groups: arthropathy (GART, 10 participants, 40% men), myopathy (GMYO, 10 participants, 30% men), and the control group (CG, 20 asymptomatic participants, 25% men). Participants were asked to perform the movements of free maximal mouth opening and closing, right and left lateral excursions, and protrusion with sliding teeth contacts. The mandibular trajectory was recorded using opto-electronic devices tracking reflective markers placed in front of the 'soft tissue pogonion point'. The movements were analysed on the following axis: x - medial-lateral, y - vertical, z - antero-posterior. Significative differences were found in CGxGART - unassisted maximal mouth opening and closing projection on y-axis (OCY), CGxGMYO - unassisted maximal mouth opening and closing projection on x-axis (OCX), and in the measures Opening lateral deviation on x-axis (OLDX), closing lateral deviation on x-axis (CLDX) and in the measures of speed for both. In regard to GARTxGMYO, a significative difference was found in Protrusion lateral deviation on x-axis (PLDX) 'Conover-Iman Test of Multiple Comparisons Using Rank Sums' using Bonferroni correction (P < 0·05). In conclusion, the total opening movements in individuals with TMD tended to have higher deviation than in those asymptomatic individuals and a reduction in the speed of movements.

Background Temporomandibular disorders (TMDs) are pathological conditions affecting the temporomandibular joint and/or masticatory muscles. The current diagnosis of TMDs is complex and multi-factorial, including questionnaires, medical testing and the use of diagnostic methods, such as computed tomography and magnetic resonance imaging. The evaluation, like the mandibular range of motion, needs the experience of the professional in the field and as such, there is a probability of human error when diagnosing TMD. The aim of this study is therefore to develop a method with infrared cameras, using the maximum range of motion of the jaw and four types of classifiers to help professionals to classify the pathologies of the temporomandibular joint (TMJ) and related muscles in a quantitative way, thus helping to diagnose and follow up on TMD. Methods Forty individuals were evaluated and diagnosed using the diagnostic criteria for temporomandibular disorders (DC/TMD) scale, and divided into three groups: 20 healthy individuals (control group CG), 10 individuals with myopathies (MG), 10 individuals with arthropathies (AG). A quantitative assessment was carried out by motion capture. The TMJ movement was captured with camera tracking markers mounted on the face and jaw of each individual. Data was exported and analyzed using a custom-made software. The data was used to identify and place each participant into one of three classes using the K-nearest neighbor (KNN), Random Forest, Naïve Bayes and Support Vector Machine algorithms. Results Significant precision and accuracy (over 90%) was reached by KNN when classifying the three groups. The other methods tested presented lower values of sensitivity and specificity. Conclusion The quantitative TMD classification method proposed herein has significant precision and accuracy over the DC/TMD standards. However, this should not be used as a standalone tool but as an auxiliary method for diagnostic TMDs.

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