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.

3D extraoral morphometric changes after implant surgery: an exploratory pilot study using stereophotogrammetry.

OBJECTIVE: This pilot study aimed to evaluate, for the first time, the changes in facial tissues following the placement of a single dental implant. METHOD AND MATERIALS: Patients were scanned with a 3D facial scanner (3dMD) before implant surgery, immediately after surgery (T1), at 7 days postoperatively (T2), and at the impression stage (T3). Acquired images were processed using the 3dMDvultus (3dMD) software program and volume differences and linear depth measurements were calculated to determine the morphometric changes over time. A total of 11 patients were included in the analyses. Descriptive statistics were employed to analyze the data. RESULTS: The volumetric changes and maximum depth differences indicated an initial increase, followed by a progressive decrease in tissue volume after implant placement in the area of the surgery. The volume change values ranged between 2.5 and 3.9 cm3 for T1, whereas for T2, the volume change decreased to a range of 0.8 to 1.8 cm3. Maximum depth differences ranged between 2.06 and 2.80 mm in the soft tissues immediately after the implant surgery and reduced to around 2.01 to 0.55 mm at the impression stage. The amount of painkiller used was not related to the magnitude of linear depth measurements at any assessed time point. CONCLUSION: There was a longitudinal decrease in soft tissue volume and depth difference in extraoral soft tissues in the region of implant placement after surgery up to 6 weeks. The use of a facial scanner is a promising noninvasive method to monitor 3D morphometric changes after implant surgery.