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Statement of Problem: Studies correlating occlusal morphology from 3-dimensional intraoral scans with both soft and hard tissue dynamic landmark tracking within the same participant population are lacking., Purpose: The purpose of this clinical study was to use 3-dimensional intraoral scanning, computer-aided design, electrognathography, and artificial intelligence to investigate the relationships between anterior occlusion and arch parameters with hard and soft tissue displacements during speech production., Material and Methods: An artificial intelligence (AI) driven software program and electrognathography was used to record the phonetic activities in 62 participants for soft tissue (ST) and hard tissue (HT) displacement. Soft tissue displacement was quantified by the mean difference between subnasale and soft tissue pogonion peaks during phonetic expressions, and hard tissue displacement was directly measured with an electrognathograph. Intercanine and intermolar distances, arch perimeters, and horizontal and vertical overlap were measured from the intraoral scan data., Results: ST and HT displacements were successfully estimated for fricative (ST=7.16 ±4.51 mm, HT=11.86 ±4.02 mm), sibilant (ST=5.11 ±3.49 mm, HT=8.24 ±3.31 mm), linguodental (ST=5.72 ±4.46 mm, HT=10.01 ±3.16 mm), and bilabial (ST=5.56 ±4.64 mm, HT=11.69 ±4.28 mm) phonetics. Vertical overlap correlated positively with hard tissue movement during all speech expressions except bilabial phonetics (ρ=.30 to.41, P<.05). Maxillary and mandibular arch perimeters showed negative correlations with soft tissue displacement during linguodental and bilabial speech (ρ=-.25 to -.41, P<.05) but were significantly correlated with hard tissue movement during all speech assessments (ρ=-.28 to -.44, P<.05). Maxillary intermolar distances negatively correlated with hard tissue phonetic expressions (ρ=-.24 to -.30, P<.05). Participant age positively correlated with soft tissue displacement during all speech patterns (ρ=.28 to.33, P<.05) and with weight increase (ρ=.27, P=.033), and hard tissue displacement (ρ=.25, P=.048) during maximum mouth opening significantly correlated with linguodental phonetics., Conclusions: Within the study population, vertical overlap, maxillary intermolar distance, and dental arch perimeters correlated significantly with mandibular displacement during phonetic expression., (Copyright © 2024 The Authors. Published by Elsevier Inc. All rights reserved.)

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., (© 2024 The Author(s). Journal of Oral Rehabilitation published by John Wiley & Sons Ltd.)

This study aimed to predict dental freeway space by examining the clinical history, habits, occlusal parameters, mandibular hard tissue movement, soft tissue motion, muscle activity, and temporomandibular joint function of 66 participants. Data collection involved video-based facial landmark tracking, mandibular electrognathography, surface electromyography of mandibular range of motion, freeway space, chewing tasks, phonetic expressions, joint vibration analysis, and 3D jaw scans of occlusion. This resulted in a dataset of 121 predictor features, with freeway space as the target variable. Six models were trained on synthetic data ranging from 500 to 25,000 observations, with 65 original observations reserved for testing: Linear Regression, Random Forest, CatBoost Regressor, XGBoost Regressor, Multilayer Perceptron Neural Network (MPNN), and TabNet. Explainable AI indicated that key predictors of freeway space included phonetics, resting temporalis muscle activity, mandibular muscle activity during clenching, body weight, mandibular hard tissue lateral displacements, and dental arch parameters. CatBoost excelled with a test error of 0.65 mm using 5000 synthetic data points, while a refined MPNN achieved the best performance with 25,000 synthetic data points and 121 unique predictors, yielding an absolute error of 0.43 mm on the 65 original observations., (© 2024. The Author(s).)

Challenges arise in accessing archived signal outputs due to proprietary software limitations. There is a notable lack of exploration in open-source mandibular EMG signal conversion for continuous access and analysis, hindering tasks such as pattern recognition and predictive modelling for temporomandibular joint complex function. To Develop a workflow to extract normalised signal parameters from images of mandibular muscle EMG and identify optimal clustering methods for quantifying signal intensity and activity durations. A workflow utilising OpenCV, variational encoders and Neurokit2 generated and augmented 866 unique EMG signals from jaw movement exercises. k-means, GMM and DBSCAN were employed for normalisation and cluster-centric signal processing. The workflow was validated with data collected from 66 participants, measuring temporalis, masseter and digastric muscles. DBSCAN (0.35 to 0.54) and GMM (0.09 to 0.24) exhibited lower silhouette scores for mouth opening, anterior protrusion and lateral excursions, while K-means performed best (0.10 to 0.11) for temporalis and masseter muscles during chewing activities. The current study successfully developed a deep learning workflow capable of extracting normalised signal data from EMG images and generating quantifiable parameters for muscle activity duration and general functional intensity., (© 2024. The Author(s).)

Studies of the frequency of heterozygous carriers for common inherited diseases of haemoglobin in over 7500 adolescent children in 25 districts in Sri Lanka have disclosed a highly significant variation over very short geographical distances. A further analysis of these findings, including their relationship to the past frequency and distribution of malaria, climatic variation, altitude, ethnic origin and consanguinity rates, have provided evidence regarding the evolutionary basis for the variable distribution of these conditions over short distances. It is likely that the complex interplay between malaria and the environment, together with related ethnic and social issues, exists in many countries across the tropical belt. Hence, these observations emphasise the importance of micromapping heterozygote distributions in high-frequency countries in order to define their true burden and the facilities required for the prevention and management of the homozygous and compound heterozygous disorders that result from their interaction., (© 2016 John Wiley & Sons Ltd.)

Anemia affects over 800 million women and children globally. Measurement of hepcidin as an index of iron status shows promise, but its diagnostic performance where hemoglobinopathies are prevalent is unclear. We evaluated the performance of hepcidin as a diagnostic test of iron deficiency in adolescents across Sri Lanka. We selected 2273 samples from a nationally representative cross-sectional study of 7526 secondary schoolchildren across Sri Lanka and analyzed associations between hepcidin and participant characteristics, iron indices, inflammatory markers, and hemoglobinopathy states. We evaluated the diagnostic accuracy of hepcidin as a test for iron deficiency with estimation of the AUC ROC , sensitivity/specificity at each hepcidin cutoff, and calculation of the Youden Index to find the optimal threshold. Hepcidin was associated with ferritin, sTfR, and hemoglobin. The AUC ROC for hepcidin as a test of iron deficiency was 0.78; hepcidin outperformed Hb and sTfR. The Youden index-predicted cutoff to detect iron deficiency (3.2 ng/mL) was similar to thresholds previously identified to predict iron utilization and identify deficiency in African populations. Neither age, sex, nor α- or β-thalassemia trait affected diagnostic properties of hepcidin. Hepcidin pre-screening would prevent most iron-replete thalassemia carriers from receiving iron whilst still ensuring most iron deficient children were supplemented. Our data indicate that the physiological relationship between hepcidin and iron status transcends specific populations. Measurement of hepcidin in individuals or populations could establish the need for iron interventions. Am. J. Hematol. 92:196-203, 2017. © 2016 Wiley Periodicals, Inc., (© 2016 Wiley Periodicals, Inc.)