Buccofacial Apraxia, Oropharyngeal Dysphagia, and Dementia Severity in Community-Dwelling Elderly Patients

Presbyphagia: Dysphagia in the elderly

Dysphagia has been classified as a “geriatric syndrome” and can lead to serious complications that result in a tremendous burden on population health and healthcare resources worldwide. A characteristic age-related change in swallowing is defined as “presbyphagia.” Medical imaging has shown some changes that seriously affect the safety and efficacy of swallowing. However, there is a general lack of awareness of the effects of aging on swallowing function and a belief that these changes are part of normal aging. Our review provides an overview of presbyphagia, which has been a neglected health problem for a long time. Attention and awareness of dysphagia in the elderly population should be strengthened, and targeted intervention measures should be actively implemented.

Cognitive Impairment and Mild to Moderate Dysphagia in Elderly Patients: A Retrospective Controlled Study

Background: To investigate whether cognitive impairment in elderly patients could correlate with the severity of swallowing disorders detectable through the endoscopic fiber optic evaluation. Methods: Elderly patients (≥65 years) performing a swallowing evaluation were included and divided according to the Dysphagia outcome and severity scale (DOSS). Neurological evaluation and Mini-Mental test examination (MMET) were administered to detect cognitive impairment. Results: Significantly worse swallowing function was reported in the cognitive impairment group than the control one (40% vs 19%; P = .001). A different significant distribution of swallowing performance was detected according to the patient's MMET score (P < .001; P < .001; P = .01). At the ANOVA test among dependent variables assessed, only age>65 and MMET<10 were significantly correlated with swallowing function (F = 3.862, P = .028; F = 17.49, P = .000). Conclusions: The elderly patient has an increased risk for unrecognized swallowing disorders, with a prevalence of mild to moderate forms. Assessment of cognitive performance could facilitate the identification of swallowing disorders by providing a higher level of suspicion for silent aspiration in subjects with poor MMET scores.

Longitudinal Speech Biomarkers for Automated Alzheimer's Detection

We introduce a novel audio processing architecture, the Open Voice Brain Model (OVBM), improving detection accuracy for Alzheimer's (AD) longitudinal discrimination from spontaneous speech. We also outline the OVBM design methodology leading us to such architecture, which in general can incorporate multimodal biomarkers and target simultaneously several diseases and other AI tasks. Key in our methodology is the use of multiple biomarkers complementing each other, and when two of them uniquely identify different subjects in a target disease we say they are orthogonal. We illustrate the OBVM design methodology by introducing sixteen biomarkers, three of which are orthogonal, demonstrating simultaneous above state-of-the-art discrimination for two apparently unrelated diseases such as AD and COVID-19. Depending on the context, throughout the paper we use OVBM indistinctly to refer to the specific architecture or to the broader design methodology. Inspired by research conducted at the MIT Center for Brain Minds and Machines (CBMM), OVBM combines biomarker implementations of the four modules of intelligence: The brain OS chunks and overlaps audio samples and aggregates biomarker features from the sensory stream and cognitive core creating a multi-modal graph neural network of symbolic compositional models for the target task. In this paper we apply the OVBM design methodology to the automated diagnostic of Alzheimer's Dementia (AD) patients, achieving above state-of-the-art accuracy of 93.8% using only raw audio, while extracting a personalized subject saliency map designed to longitudinally track relative disease progression using multiple biomarkers, 16 in the reported AD task. The ultimate aim is to help medical practice by detecting onset and treatment impact so that intervention options can be longitudinally tested. Using the OBVM design methodology, we introduce a novel lung and respiratory tract biomarker created using 200,000+ cough samples to pre-train a model discriminating cough cultural origin. Transfer Learning is subsequently used to incorporate features from this model into various other biomarker-based OVBM architectures. This biomarker yields consistent improvements in AD detection in all the starting OBVM biomarker architecture combinations we tried. This cough dataset sets a new benchmark as the largest audio health dataset with 30,000+ subjects participating in April 2020, demonstrating for the first time cough cultural bias.