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

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.

Assessing and treating primary headaches and cranio-facial pain in patients undergoing rehabilitation for neurological diseases.

BACKGROUND: Pain is a very common condition in patient undergoing rehabilitation for neurological disease; however the presence of primary headaches and other cranio-facial pains, particularly when they are actually or apparently independent from the disability for which patient is undergoing rehabilitation, is often neglected. Diagnostic and therapeutic international and national guidelines, as well as tools for the subjective measure of head pain are available and should also be applied in the neurorehabilitation setting. This calls for searching the presence of head pain, independently from the rehabilitation needs, since pain, either episodic or chronic, interferes with patient performance by affecting physical and emotional status. Pain may also interfere with sleep and therefore hamper recovery. METHODS: In our role of task force of the Italian Consensus Conference on Pain in Neurorehabilitation (ICCPN), we have elaborated specific recommendations for diagnosing and treating head pains in patients undergoing rehabilitation for neurological diseases. RESULTS AND CONCLUSION: In this narrative review, we describe the available literature that has been evaluated in order to define the recommendations and outline the needs of epidemiological studies concerning headache and other cranio-facial pain in neurorehabilitation.