Comparison of Machine Learning Algorithms Fed with Mobility-Related and Baropodometric Measurements to Identify Temporomandibular Disorders

Abstract

Temporomandibular disorders (TMDs) refer to a group of conditions that affect the temporomandibular joint, causing pain and dysfunction in the jaw joint and related muscles. The diagnosis of TMDs typically involves clinical assessment through operator-based physical examination, a self-reported questionnaire and imaging studies. To objectivize the measurement of TMD, this study aims at investigating the feasibility of using machine-learning algorithms fed with data gathered from low-cost and portable instruments to identify the presence of TMD in adult subjects. Through this aim, the experimental protocol involved fifty participants, equally distributed between TMD and healthy subjects, acting as a control group. The diagnosis of TMD was performed by a skilled operator through the typical clinical scale. Participants underwent a baropodometric analysis by using a pressure matrix and the evaluation of the cervical mobility through inertial sensors. Nine machine-learning algorithms belonging to support vector machine, k-nearest neighbours and decision tree algorithms were compared. The k-nearest neighbours algorithm based on cosine distance was found to be the best performing, achieving performances of 0.94, 0.94 and 0.08 for the accuracy, F1-score and G-index, respectively. These findings open the possibility of using such methodology to support the diagnosis of TMDs in clinical environments.

Keywords: clinical assessment; inertial sensors; machine learning; pressure platform; temporomandibular disorder.

Figure 1

(a) Straight opening; (b) lateral deviation; (c) corrected deviation and (d) other opening. Arrows indicate the type of movement.

Figure 7

(a) Representation of static test and the division of the forefoot and rearfoot. (b) Calculation of the podalic angle. Color map indicates the level of pressure with the lowest related to the blue and the highest to the red.

Figure 2

Measuring the width of the opening.

Figure 3

Muscle palpation: the upper images demonstrate the palpation of the temporal muscle and the lower images demonstrate palpation of the masseter muscle.

Figure 4

Joint palpation.

Figure 5

Baropodometric setup.

Figure 6

Cervical mobility with Moover sensor. (a) Flexion–extension of the head; (b) head rotation and (c) head inclination. Arrows indicate the relative rotation.

Figure 8

Example of left cervical rotation, expressed in degree, of a single repetition for CG (red line) and TMD group (blue line).

Figure 9

Example of baropodometric result for CG (a) and TMD group (b). Color scale represents the amount of load, with greater load associated with red color.