Malocclusion in children with speech sound disorders and motor speech involvement: a cross-sectional clinical study in Swedish children

Prevalence of malocclusions and parafunctional habits in pediatric patients with developmental dyslexia

Objective

The study aimed to assess the prevalence of dental malocclusion, orthodontic parameters, and parafunctional habits in children with developmental dyslexia (DD).

Methods

Forty pediatric patients (67.5% boys and 32.5% girls, mean age: 11.02 ± 2.53 years, range: 6-15 years) with DD were compared with 40 age- and sex-matched healthy participants for prevalence of dental malocclusion, orthodontic parameters, and parafunctional habits. Dental examinations were performed by an orthodontist.

Results

Pediatric patients with DD exhibited a significantly higher prevalence of Angle Class III malocclusion (22.5% vs. 5.0%, P = 0.024), deep bite (27.5% vs. 7.5%, P = 0.019), midline deviation (55.0% vs. 7.5%, P < 0.0001), midline diastemas (32.5% vs. 7.5%, P = 0.010), wear facets (92.5% vs. 15.0%, P < 0.0001), self-reported nocturnal teeth grinding (82.5% vs. 7.5%, P < 0.0001), nail biting (35.0% vs. 0.0%, P < 0.0001), and atypical swallowing (85.0% vs. 17.5%, P < 0.0001) compared to that in healthy controls.

Conclusions

Pediatric patients with DD showed a higher prevalence of Class III malocclusion, greater orthodontic vertical and transverse discrepancies, and incidence of parafunctional activities. Clinicians and dentists should be aware of the vulnerability of children with dyslexia for exhibiting malocclusion and encourage early assessment and multidisciplinary intervention.

Comparison of Speech Defects in Different Types of Malocclusion

Introduction Speech has a great impact on human evolution, allowing for the widespread knowledge and advancement of tools. Difficulty in pronouncing one or more sounds is the most common speech impairment. Speech defects are more commonly associated with class III malocclusion patients (difficulty in pronouncing 's' and 't' sounds), the second in line is class II malocclusion (difficulty in pronouncing 's' and 'z' sounds), and speech distortions are least affected in class I malocclusion (difficulty in pronouncing 's' and 'Sh'). Most patients with dentofacial disharmonies and speech distortions need orthodontic care and orthognathic surgery to resolve their issues with mastication, aesthetics, and speech. Aims and objectives To compare and assess speech difficulties in different types of malocclusion. Materials and methods The study was conducted over 160 subjects for three and half months. All of them were evaluated for speech defects before they received orthodontic treatment. The main basis of this study is according to Angle's classification of malocclusion. The subjects were segregated according to Angle's classification of malocclusion. Malocclusion traits that are included in this study are Angle's class I, Angle's class II division I and division II, and Angle's class III. Results According to the results, out of 160 subjects, labio-dental speech defects are observed in 8% where n=13 of the study participants, linguodental speech defects are observed in 2% where n=3, lingua-alveolar speech defects are present in 54% where n=86, and bilabial speech defects are observed in 2% where n=3 of the study participants. Here 'n' represents the frequency of the subjects. Severe speech defects are seen in Angle's class III malocclusion. Results according to the type of malocclusion include: labio-dental speech defects are seen in 37.5% in class I, 25% in class II division I, 0% in class II division II, and 37.5% in class III. Linguodental speech defects are seen in class III malocclusion subjects only. Lingua-alveolar sounds are seen in 27.8% of class I, 29.6% of class II division I, 1.9% of class II division II, and 40.7% of class III. Bilabial speech defects are only seen in class II division I subjects. According to the results, only lingua-alveolar speech defects are statistically significant, and more severe speech defects were observed in class III malocclusion. Conclusion Speech plays an important role in affecting the quality of life of people. Different types of malocclusion traits are associated with different types of speech defects.

Diagnosis of pathological speech with streamlined features for long short-term memory learning

BACKGROUND

Pathological speech diagnosis is crucial for identifying and treating various speech disorders. Accurate diagnosis aids in developing targeted intervention strategies, improving patients' communication abilities, and enhancing their overall quality of life. With the rising incidence of speech-related conditions globally, including oral health, the need for efficient and reliable diagnostic tools has become paramount, emphasizing the significance of advanced research in this field.

METHODS

This paper introduces novel features for deep learning in the analysis of short voice signals. It proposes the incorporation of time-space and time-frequency features to accurately discern between two distinct groups: Individuals exhibiting normal vocal patterns and those manifesting pathological voice conditions. These advancements aim to enhance the precision and reliability of diagnostic procedures, paving the way for more targeted treatment approaches.

RESULTS

Utilizing a publicly available voice database, this study carried out training and validation using long short-term memory (LSTM) networks learning on the combined features, along with a data balancing strategy. The proposed approach yielded promising performance metrics: 90% accuracy, 93% sensitivity, 87% specificity, 88% precision, an F1 score of 0.90, and an area under the receiver operating characteristic curve of 0.96. The results surpassed those obtained by the networks trained using wavelet-time scattering coefficients, as well as several algorithms trained with alternative feature types.

CONCLUSIONS

The incorporation of time-frequency and time-space features extracted from short segments of voice signals for LSTM learning demonstrates significant promise as an AI tool for the diagnosis of speech pathology. The proposed approach has the potential to enhance the accuracy and allow for real-time pathological speech assessment, thereby facilitating more targeted and effective therapeutic interventions.