Artificial neural network learns to differentiate normal TMJs and nonreducing displaced disks after training on incisor-point chewing movements.

Previous authors have described four frontal gum-chewing patterns associated with normal and abnormal TMJ disk-condyle relationships. The objective of this study was to create an automatic detection capability (expert system) by training an artificial neural network to recognize nonreducing displaced disks from frontal chewing data. Sixty-eight (68) subjects, 29 with normal joints, 18 with unilateral nonreducing displaced disks and 21 with bilateral nonreducing displaced disks were selected from a continuous series of patients seeking treatment for TMD. Right-sided gum chewing was recorded from all patients. Left-sided chewing was also recorded from the right unilateral patients. 50% of the vertical, lateral and timing values at 10%, 65% and 100% of opening and at 30%, 70% and 90% of closing were used to train an artificial neural network. The remaining 50% were used for testing. All normal subjects were detected as normal (specificity = 100%). Two bilateral and two unilateral patients were not detected (sensitivity = 91.8%). Four (4) patients received the wrong classification (unilateral vs. bilateral) and one patient received both (undecided) for an overall accuracy = 86.8%. The artificial neural network detected, at an acceptable level of error, the presence and type of nonreducing disk displacement from frontal plane jaw recordings of gum chewing in a group of real patients seeking treatment for TMD. Since it is very inexpensive to conduct, mastication analysis appears to have the potential of an excellent cost/benefit ratio.