Evaluation of a Wireless Tongue Tracking System on the Identification of Phoneme Landmarks

Accurate estimation of biological age and its application in disease prediction using a multimodal image Transformer system

Aging in an individual refers to the temporal change, mostly decline, in the body's ability to meet physiological demands. Biological age (BA) is a biomarker of chronological aging and can be used to stratify populations to predict certain age-related chronic diseases. BA can be predicted from biomedical features such as brain MRI, retinal, or facial images, but the inherent heterogeneity in the aging process limits the usefulness of BA predicted from individual body systems. In this paper, we developed a multimodal Transformer-based architecture with cross-attention which was able to combine facial, tongue, and retinal images to estimate BA. We trained our model using facial, tongue, and retinal images from 11,223 healthy subjects and demonstrated that using a fusion of the three image modalities achieved the most accurate BA predictions. We validated our approach on a test population of 2,840 individuals with six chronic diseases and obtained significant difference between chronological age and BA (AgeDiff) than that of healthy subjects. We showed that AgeDiff has the potential to be utilized as a standalone biomarker or conjunctively alongside other known factors for risk stratification and progression prediction of chronic diseases. Our results therefore highlight the feasibility of using multimodal images to estimate and interrogate the aging process.

Measuring Articulatory Patterns in Amyotrophic Lateral Sclerosis Using a Data-Driven Articulatory Consonant Distinctiveness Space Approach

PURPOSE

The aim of this study was to leverage data-driven approaches, including a novel articulatory consonant distinctiveness space (ACDS) approach, to better understand speech motor control in amyotrophic lateral sclerosis (ALS).

METHOD

Electromagnetic articulography was used to record tongue and lip movement data during the production of 10 consonants from healthy controls (n = 15) and individuals with ALS (n = 47). To assess phoneme distinctness, speech data were analyzed using two classification algorithms, Procrustes matching (PM) and support vector machine (SVM), and the area/volume of the ACDS. Pearson's correlation coefficient was used to examine the relationship between bulbar impairment and the ACDS. Analysis of variance was used to examine the effects of bulbar impairment on consonant distinctiveness and consonant classification accuracies in clinical subgroups.

RESULTS

There was a significant relationship between the ACDS and intelligible speaking rate (area, p = .003; volume, p = .010), and the Amyotrophic Lateral Sclerosis Functional Rating Scale-Revised (ALSFRS-R) bulbar subscore (area, p = .009; volume, p = .027). Consonant classification performance followed a consistent pattern with bulbar severity, where consonants produced by speakers with more severe ALS were classified less accurately (SVM = 75.27%; PM = 74.54%) than the healthy, asymptomatic, and mild-moderate groups. In severe ALS, area of the ACDS was significantly condensed compared to both asymptomatic (p = .004) and mild-moderate (p = .013) groups. There was no statistically significant difference in area between the severe ALS group and healthy speakers (p = .292).

CONCLUSIONS

Our comprehensive approach is sensitive to early oromotor changes in response due to disease progression. The preserved articulatory consonant space may capture the use of compensatory adaptations to counteract influences of neurodegeneration.

SUPPLEMENTAL MATERIAL

https://doi.org/10.23641/asha.22044320.