التفاصيل البيبلوغرافية
| العنوان: |
Predicting masticatory muscle activity and deviations in mouth opening from non‐invasive temporomandibular joint complex functional analyses. |
| المؤلفون: |
Farook, Taseef Hasan, Haq, Tashreque Mohammed, Ramees, Lameesa, Dudley, James |
| المصدر: |
Journal of Oral Rehabilitation; Sep2024, Vol. 51 Issue 9, p1770-1777, 8p |
| مصطلحات موضوعية: |
JAW physiology, TEMPOROMANDIBULAR joint, RESEARCH funding, PREDICTION models, MASSETER muscle, CLINICAL decision support systems, MOUTH physiology, ELECTRODIAGNOSIS, MASTICATORY muscles, ELECTROMYOGRAPHY, PREDICTIVE validity |
| مستخلص: |
Background: A quantitative approach to predict expected muscle activity and mandibular movement from non‐invasive hard tissue assessments remains unexplored. Objectives: This study investigated the predictive potential of normalised muscle activity during various jaw movements combined with temporomandibular joint (TMJ) vibration analyses to predict expected maximum lateral deviation during mouth opening. Method: Sixty‐six participants underwent electrognathography (EGN), surface electromyography (EMG) and joint vibration analyses (JVA). They performed maximum mouth opening, lateral excursion and anterior protrusion as jaw movement activities in a single session. Multiple predictive models were trained from synthetic observations generated from the 66 human observations. Muscle function intensity and activity duration were normalised and a decision support system with branching logic was developed to predict lateral deviation. Performance of the models in predicting temporalis, masseter and digastric muscle activity from hard tissue data was evaluated through root mean squared error (RMSE) and mean absolute error. Results: Temporalis muscle intensity ranged from 0.135 ± 0.056, masseter from 0.111 ± 0.053 and digastric from 0.120 ± 0.051. Muscle activity duration varied with temporalis at 112.23 ± 126.81 ms, masseter at 101.02 ± 121.34 ms and digastric at 168.13 ± 222.82 ms. XGBoost predicted muscle intensity and activity duration and scored an RMSE of 0.03–0.05. Jaw deviations were successfully predicted with a MAE of 0.9 mm. Conclusion: Applying deep learning to EGN, EMG and JVA data can establish a quantifiable relationship between muscles and hard tissue movement within the TMJ complex and can predict jaw deviations. [ABSTRACT FROM AUTHOR] |
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| قاعدة البيانات: |
Complementary Index |
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