Martina Ferrillo1, Mario Migliario2,3, Nicola Marotta4, Francesco Fortunato5, Marino Bindi3, Federica Pezzotti3, Antonio Ammendolia4, Amerigo Giudice1, Pier Luigi Foglio Bonda2,3, Alessandro de Sire4. 1. Dentistry Unit, Department of Health Sciences, University of Catanzaro "Magna Graecia", Catanzaro, Italy. 2. Dentistry Unit, Department of Translational Medicine, University of Eastern Piedmont, Novara, Italy. 3. Dentistry Unit, University Hospital "Maggiore della Carità", Novara, Italy. 4. Physical Medicine and Rehabilitation Unit, Department of Medical and Surgical Sciences, University of Catanzaro "Magna Graecia", Catanzaro, Italy. 5. Institute of Neurology, Department of Medical and Surgical Sciences, University of Catanzaro "Magna Graecia", Catanzaro, Italy.
Abstract
OBJECTIVES: To evaluate the linkage underpinning different clinical conditions as painful TMD and neck pain in patients affected by primary headaches. MATERIALS AND METHODS: In this machine learning study, data from medical records of patients with headaches as migraine, tension-type headache (TTH) and other primary ones, referring to a University Hospital over a 10-year period were analysed. VAS was used to evaluate the intensity of the TMD and neck pain. Moreover, the magnetic resonance imaging was used to supplement the clinical data. RESULTS: A total of 300 patients (72 male, 228 female), mean aged 37.78 ± 5.11 years, were included. Higher TMD and neck pain VAS in migraine patients were reported. The machine learning analysis focussed on type of primary headache demonstrated that a higher TMD VAS was correlated to migraine, whereas a higher neck pain VAS was correlated to TTH or migraine. Concerning the TMD type, arthrogenous and mixed TMD were correlated to mild-moderate TMD pain (depending on neck pain intensity), whereas myogenic TMD was correlated to moderate-severe TMD pain. CONCLUSIONS: Machine-learning approach highlighted the complexity of diagnosis process and demonstrated that neck pain might be an influential variable on the belonging to different group of headaches in TMD patients.
OBJECTIVES: To evaluate the linkage underpinning different clinical conditions as painful TMD and neck pain in patients affected by primary headaches. MATERIALS AND METHODS: In this machine learning study, data from medical records of patients with headaches as migraine, tension-type headache (TTH) and other primary ones, referring to a University Hospital over a 10-year period were analysed. VAS was used to evaluate the intensity of the TMD and neck pain. Moreover, the magnetic resonance imaging was used to supplement the clinical data. RESULTS: A total of 300 patients (72 male, 228 female), mean aged 37.78 ± 5.11 years, were included. Higher TMD and neck pain VAS in migraine patients were reported. The machine learning analysis focussed on type of primary headache demonstrated that a higher TMD VAS was correlated to migraine, whereas a higher neck pain VAS was correlated to TTH or migraine. Concerning the TMD type, arthrogenous and mixed TMD were correlated to mild-moderate TMD pain (depending on neck pain intensity), whereas myogenic TMD was correlated to moderate-severe TMD pain. CONCLUSIONS: Machine-learning approach highlighted the complexity of diagnosis process and demonstrated that neck pain might be an influential variable on the belonging to different group of headaches in TMD patients.