Machine learning based analysis of speech dimensions in functional oropharyngeal dysphagia

Correlation between voice intensity and swallowing function in subjects with Parkinson's disease

INTRODUCTION

People with Parkinson's Disease (PD) experience reduced voice intensity and dysphagia. Organs related to voice production and swallowing are structurally and neurologically intertwined. Thus, instrumental voice assessment may be useful in identifying voice impairments that can show swallowing disorders. Timely assessment of swallowing disorders may prevent the occurrence of malnutrition, dehydration, pneumonia, and death.

OBJECTIVE

The aim of this study was to investigate the relationship between voice intensity and swallowing function in PD.

METHODS

30 participants with PD were recruited. Motor disability was evaluated by the Unified Parkinson's Disease Rating Scale part III; voice intensity was assessed by PRAAT software during sustained /a/ and 1 min of monologue. The Penetration Aspiration Scale, the Dysphagia Outcome Severity Scale, and the Videofluoroscopic Dysphagia Scale were used to assess swallowing during videofluoroscopy. Spearman correlation coefficients, logistic, and linear models were used to analyze data.

RESULTS

Voice intensity correlated with swallowing: as voice intensity decreased, swallowing function deteriorated (Spearman coefficients from -0.42 to -0.72 across scales), and this holds even when adjusted for MDS-UPDRS motor scores. Swallowing impairment is 56 times more likely (p<0.01) when the voice intensity is below the normal cut-off score (60 dB) with a positive predictive value of 93%.

CONCLUSIONS

Reduction in voice intensity is indicative of a higher risk of swallowing dysfunction. Thus, an instrumental voice analysis seems to be a non-invasive, lowcost, easy-to-use tool to identify people with PD in need of an assessment to allow for timely swallowing management and reduction of complications caused by dysphagia.

Association Between Cognitive Impairment and Dysphagia: A Two-Sample Mendelian Randomization Study

INTRODUCTION

Previous observational studies have implied a correlation between cognitive impairment and dysphagia, but some have indicated no correlation between the two. Such contradictory findings may have been influenced by small sample sizes and potential confounders. In this Mendelian randomization (MR) analysis, we genetically estimated a causal relationship between cognitive impairment and dysphagia.

METHODS

The study included a large meta-analysis of genome-wide association studies (GWAS) of cognitive impairment in 269,867 individuals of European ancestry and pooled data from a GWAS of dysphagia in 165,765 individuals of European ancestry (cases 3497, controls 161,968). We then used five different complementary MR methods, including IVW, MR-Egger, MR-RAPS, weighted median, and weighted mean, to estimate causality between cognitive impairment and dysphagia and finally also assessed heterogeneity and horizontal pleiotropy by extensive sensitivity tests.

RESULTS

No evidence of heterogeneity in the effect of instrumental variables was found in Cochran's Q test; therefore, a fixed effects model was used. IVW analysis (OR: 1.206, 95% CI: [1.041, 1.371], p = 0.00508) found that cognitive impairment was associated with an increased risk of dysphagia and that there was a causal association between the two. Also, the weighted median (OR: 1.248, 95% CI: [1.012, 1.484], p = 0.0253), weighted mode (OR: 1.216, 95% CI: [1.043, 1.389], p = 0.0412), and MR-RAPS (OR: 1.225, 95% CI: [1.069, 1.381], p = 0.00627) validated the conclusions. Furthermore, extensive sensitivity analyses found no evidence of heterogeneity or horizontal pleiotropy, confirming the reliability of this MR result.

CONCLUSION

Our MR study demonstrated a causal effect of cognitive impairment on dysphagia from a genetic perspective, suggesting that individuals with a history of cognitive impairment require specific clinical attention to prevent the development of dysphagia.

The Study of Speech Acoustic Characteristics of Elderly Individuals with Presbyphagia in Ningbo, China

The feasibility of using acoustic parameters to predict presbyphagia has been preliminarily confirmed. Considering that age and gender can influence the results of acoustic parameters, this study aimed to further explore the specific effects of age and gender on acoustic parameter analysis of the elderly population over 60 years old with presbyphagia. A total of 45 participants were enrolled and divided into three groups (60-69 years old, 70-79 years old, and 80-89 years old). Acoustic parameters, including maximum phonation time, first to third formant frequencies (F1-F3) of /a/, /i/, and /u/, oral diadochokinesis, the acoustic vowel space, and laryngeal diadochokinesis (LDDK), were extracted and calculated. Two-way analysis of variance was used to analyze the correlations between acoustic parameters and age and gender. The result indicates that /hʌ/ LDDK rate had significant differences in age groups, presenting the 80-89 age group being significantly slower than the 60-69 age group. F1/a/, F2/a/, F2/i/, F3/i/, and F2i/F2u differed systematically between genders, with males being lower and smaller than females. Changes that were consistent with /hʌ/ LDDK regularity, confirmed by greater regularity in females. No significant differences were observed for other acoustic parameters. No significant interactions were revealed. According to the preliminary data, we hypothesized that respiratory capacity and control during vocal fold abduction weaken with aging. This highlights the importance of continuously monitoring the respiratory impact on swallowing function in elderly individuals. Additionally, gender influenced several acoustic parameters, indicating the necessity to differentiate between genders when assessing presbyphagia using acoustic parameters, especially focusing on swallowing function in elderly males in Ningbo.

Deep learning approach for dysphagia detection by syllable-based speech analysis with daily conversations

Dysphagia, a disorder affecting the ability to swallow, has a high prevalence among the older adults and can lead to serious health complications. Therefore, early detection of dysphagia is important. This study evaluated the effectiveness of a newly developed deep learning model that analyzes syllable-segmented data for diagnosing dysphagia, an aspect not addressed in prior studies. The audio data of daily conversations were collected from 16 patients with dysphagia and 24 controls. The presence of dysphagia was determined by videofluoroscopic swallowing study. The data were segmented into syllables using a speech-to-text model and analyzed with a convolutional neural network to perform binary classification between the dysphagia patients and control group. The proposed model in this study was assessed in two different aspects. Firstly, with syllable-segmented analysis, it demonstrated a diagnostic accuracy of 0.794 for dysphagia, a sensitivity of 0.901, a specificity of 0.687, a positive predictive value of 0.742, and a negative predictive value of 0.874. Secondly, at the individual level, it achieved an overall accuracy of 0.900 and area under the curve of 0.953. This research highlights the potential of deep learning modal as an early, non-invasive, and simple method for detecting dysphagia in everyday environments.

Exploring the Role of Machine Learning in Diagnosing and Treating Speech Disorders: A Systematic Literature Review

Purpose

Speech disorders profoundly impact the overall quality of life by impeding social operations and hindering effective communication. This study addresses the gap in systematic reviews concerning machine learning-based assistive technology for individuals with speech disorders. The overarching purpose is to offer a comprehensive overview of the field through a Systematic Literature Review (SLR) and provide valuable insights into the landscape of ML-based solutions and related studies.

Methods

The research employs a systematic approach, utilizing a Systematic Literature Review (SLR) methodology. The study extensively examines the existing literature on machine learning-based assistive technology for speech disorders. Specific attention is given to ML techniques, characteristics of exploited datasets in the training phase, speaker languages, feature extraction techniques, and the features employed by ML algorithms.

Originality

This study contributes to the existing literature by systematically exploring the machine learning landscape in assistive technology for speech disorders. The originality lies in the focused investigation of ML-speech recognition for impaired speech disorder users over ten years (2014-2023). The emphasis on systematic research questions related to ML techniques, dataset characteristics, languages, feature extraction techniques, and feature sets adds a unique and comprehensive perspective to the current discourse.

Findings

The systematic literature review identifies significant trends and critical studies published between 2014 and 2023. In the analysis of the 65 papers from prestigious journals, support vector machines and neural networks (CNN, DNN) were the most utilized ML technique (20%, 16.92%), with the most studied disease being Dysarthria (35/65, 54% studies). Furthermore, an upsurge in using neural network-based architectures, mainly CNN and DNN, was observed after 2018. Almost half of the included studies were published between 2021 and 2022).

Machine learning in the evaluation of voice and swallowing in the head and neck cancer patient

PURPOSE OF REVIEW

The purpose of this review is to present recent advances and limitations in machine learning applied to the evaluation of speech, voice, and swallowing in head and neck cancer.

RECENT FINDINGS

Novel machine learning models incorporating diverse data modalities with improved discriminatory capabilities have been developed for predicting toxicities following head and neck cancer therapy, including dysphagia, dysphonia, xerostomia, and weight loss as well as guiding treatment planning. Machine learning has been applied to the care of posttreatment voice and swallowing dysfunction by offering objective and standardized assessments and aiding innovative technologies for functional restoration. Voice and speech are also being utilized in machine learning algorithms to screen laryngeal cancer.

SUMMARY

Machine learning has the potential to help optimize, assess, predict, and rehabilitate voice and swallowing function in head and neck cancer patients as well as aid in cancer screening. However, existing studies are limited by the lack of sufficient external validation and generalizability, insufficient transparency and reproducibility, and no clear superior predictive modeling strategies. Algorithms and applications will need to be trained on large multiinstitutional data sets, incorporate sociodemographic data to reduce bias, and achieve validation through clinical trials for optimal performance and utility.

Prediction of dysphagia aspiration through machine learning-based analysis of patients' postprandial voices

BACKGROUND

Conventional diagnostic methods for dysphagia have limitations such as long wait times, radiation risks, and restricted evaluation. Therefore, voice-based diagnostic and monitoring technologies are required to overcome these limitations. Based on our hypothesis regarding the impact of weakened muscle strength and the presence of aspiration on vocal characteristics, this single-center, prospective study aimed to develop a machine-learning algorithm for predicting dysphagia status (normal, and aspiration) by analyzing postprandial voice limiting intake to 3 cc.

METHODS

Conducted from September 2021 to February 2023 at Seoul National University Bundang Hospital, this single center, prospective cohort study included 198 participants aged 40 or older, with 128 without suspected dysphagia and 70 with dysphagia-aspiration. Voice data from participants were collected and used to develop dysphagia prediction models using the Multi-Layer Perceptron (MLP) with MobileNet V3. Male-only, female-only, and combined models were constructed using 10-fold cross-validation. Through the inference process, we established a model capable of probabilistically categorizing a new patient's voice as either normal or indicating the possibility of aspiration.

RESULTS

The pre-trained models (mn40_as and mn30_as) exhibited superior performance compared to the non-pre-trained models (mn4.0 and mn3.0). Overall, the best-performing model, mn30_as, which is a pre-trained model, demonstrated an average AUC across 10 folds as follows: combined model 0.8361 (95% CI 0.7667-0.9056; max 0.9541), male model 0.8010 (95% CI 0.6589-0.9432; max 1.000), and female model 0.7572 (95% CI 0.6578-0.8567; max 0.9779). However, for the female model, a slightly higher result was observed with the mn4.0, which scored 0.7679 (95% CI 0.6426-0.8931; max 0.9722). Additionally, the other models (pre-trained; mn40_as, non-pre-trained; mn4.0 and mn3.0) also achieved performance above 0.7 in most cases, and the highest fold-level performance for most models was approximately around 0.9. The 'mn' in model names refers to MobileNet and the following number indicates the 'width_mult' parameter.

CONCLUSIONS

In this study, we used mel-spectrogram analysis and a MobileNetV3 model for predicting dysphagia aspiration. Our research highlights voice analysis potential in dysphagia screening, diagnosis, and monitoring, aiming for non-invasive safer, and more effective interventions.

TRIAL REGISTRATION

This study was approved by the IRB (No. B-2109-707-303) and registered on clinicaltrials.gov (ID: NCT05149976).

A deep learning-based telemonitoring application to automatically assess oral diadochokinesis in patients with bulbar amyotrophic lateral sclerosis

BACKGROUND AND OBJECTIVES

Timely identification of dysarthria progression in patients with bulbar-onset amyotrophic lateral sclerosis (ALS) is relevant to have a comprehensive assessment of the disease evolution. To this goal literature recognized the utmost importance of the assessment of the number of syllables uttered by a subject during the oral diadochokinesis (DDK) test.

METHODS

To support clinicians, this work proposes a remote deep learning-based system, which consists (i) of a web application to acquire audio tracks of bulbar-onset ALS patients and healthy control subjects while performing the oral DDK test (i.e., repeating the /pa/, /pa-ta-ka/ and /oo-ee/ syllables) and (ii) a DDK-AID network designed to process the acquired audio signals which have different duration and to output the number of per-task syllables repeated by the subject.

RESULTS

The DDK-AID network overcomes the comparative method achieving a mean Accuracy of 90.23 in counting syllables repeated by the eleven bulbar-onset ALS-patients while performing the oral DDK test.

CONCLUSIONS

The proposed remote monitoring system, in the light of the achieved performance, represents an important step towards the implementation of self-service telemedicine systems which may ensure customised care plans.

A fully integrated, standalone stretchable device platform with in-sensor adaptive machine learning for rehabilitation

Post-surgical treatments of the human throat often require continuous monitoring of diverse vital and muscle activities. However, wireless, continuous monitoring and analysis of these activities directly from the throat skin have not been developed. Here, we report the design and validation of a fully integrated standalone stretchable device platform that provides wireless measurements and machine learning-based analysis of diverse vibrations and muscle electrical activities from the throat. We demonstrate that the modified composite hydrogel with low contact impedance and reduced adhesion provides high-quality long-term monitoring of local muscle electrical signals. We show that the integrated triaxial broad-band accelerometer also measures large body movements and subtle physiological activities/vibrations. We find that the combined data processed by a 2D-like sequential feature extractor with fully connected neurons facilitates the classification of various motion/speech features at a high accuracy of over 90%, which adapts to the data with noise from motion artifacts or the data from new human subjects. The resulting standalone stretchable device with wireless monitoring and machine learning-based processing capabilities paves the way to design and apply wearable skin-interfaced systems for the remote monitoring and treatment evaluation of various diseases.

Machine-learning assisted swallowing assessment: a deep learning-based quality improvement tool to screen for post-stroke dysphagia

Introduction

Post-stroke dysphagia is common and associated with significant morbidity and mortality, rendering bedside screening of significant clinical importance. Using voice as a biomarker coupled with deep learning has the potential to improve patient access to screening and mitigate the subjectivity associated with detecting voice change, a component of several validated screening protocols.

Methods

In this single-center study, we developed a proof-of-concept model for automated dysphagia screening and evaluated the performance of this model on training and testing cohorts. Patients were admitted to a comprehensive stroke center, where primary English speakers could follow commands without significant aphasia and participated on a rolling basis. The primary outcome was classification either as a pass or fail equivalent using a dysphagia screening test as a label. Voice data was recorded from patients who spoke a standardized set of vowels, words, and sentences from the National Institute of Health Stroke Scale. Seventy patients were recruited and 68 were included in the analysis, with 40 in training and 28 in testing cohorts, respectively. Speech from patients was segmented into 1,579 audio clips, from which 6,655 Mel-spectrogram images were computed and used as inputs for deep-learning models (DenseNet and ConvNext, separately and together). Clip-level and participant-level swallowing status predictions were obtained through a voting method.

Results

The models demonstrated clip-level dysphagia screening sensitivity of 71% and specificity of 77% (F1 = 0.73, AUC = 0.80 [95% CI: 0.78-0.82]). At the participant level, the sensitivity and specificity were 89 and 79%, respectively (F1 = 0.81, AUC = 0.91 [95% CI: 0.77-1.05]).

Discussion

This study is the first to demonstrate the feasibility of applying deep learning to classify vocalizations to detect post-stroke dysphagia. Our findings suggest potential for enhancing dysphagia screening in clinical settings. https://github.com/UofTNeurology/masa-open-source.

Biomechanical Parameters of Voice in Parkinson's Disease Patients

INTRODUCTION

Previous research on voice in Parkinson's disease (PD) has consistently demonstrated alterations in acoustic parameters, including fundamental frequency (F0), maximum phonation time, Shimmer, and Jitter. However, investigations into acoustic parameter alterations in individuals with PD are limited.

METHODS

We conducted an experimental study involving 20 PD patients (six women and fourteen men). Subjective measures of voice (VHI-30 scale and GRBAS) and objective measures using the OnlineLAB App tool for analyzing biomechanical correlates of voice were recorded. The app analyzed a total of 22 biomechanical parameters of voice.

RESULTS

The results of subjective measures were consistent with findings from previous studies. However, the results of objective measures did not align with studies that employed acoustic measures.

CONCLUSIONS

The biomechanical analysis revealed alterations in various parameters according to gender. These findings open up a new avenue of research in voice analysis for patients with PD, whether through acoustic or biomechanical analysis, aiming to determine whether the observed changes in these patients' voices are attributable to age or disease progression. This line of investigation will help elucidate the relative contribution of these factors to vocal alterations in PD patients and provide a more comprehensive understanding of the underlying mechanisms.

Applied Machine Learning Techniques to Diagnose Voice-Affecting Conditions and Disorders: Systematic Literature Review

BACKGROUND

Normal voice production depends on the synchronized cooperation of multiple physiological systems, which makes the voice sensitive to changes. Any systematic, neurological, and aerodigestive distortion is prone to affect voice production through reduced cognitive, pulmonary, and muscular functionality. This sensitivity inspired using voice as a biomarker to examine disorders that affect the voice. Technological improvements and emerging machine learning (ML) technologies have enabled possibilities of extracting digital vocal features from the voice for automated diagnosis and monitoring systems.

OBJECTIVE

This study aims to summarize a comprehensive view of research on voice-affecting disorders that uses ML techniques for diagnosis and monitoring through voice samples where systematic conditions, nonlaryngeal aerodigestive disorders, and neurological disorders are specifically of interest.

METHODS

This systematic literature review (SLR) investigated the state of the art of voice-based diagnostic and monitoring systems with ML technologies, targeting voice-affecting disorders without direct relation to the voice box from the point of view of applied health technology. Through a comprehensive search string, studies published from 2012 to 2022 from the databases Scopus, PubMed, and Web of Science were scanned and collected for assessment. To minimize bias, retrieval of the relevant references in other studies in the field was ensured, and 2 authors assessed the collected studies. Low-quality studies were removed through a quality assessment and relevant data were extracted through summary tables for analysis. The articles were checked for similarities between author groups to prevent cumulative redundancy bias during the screening process, where only 1 article was included from the same author group.

RESULTS

In the analysis of the 145 included studies, support vector machines were the most utilized ML technique (51/145, 35.2%), with the most studied disease being Parkinson disease (PD; reported in 87/145, 60%, studies). After 2017, 16 additional voice-affecting disorders were examined, in contrast to the 3 investigated previously. Furthermore, an upsurge in the use of artificial neural network-based architectures was observed after 2017. Almost half of the included studies were published in last 2 years (2021 and 2022). A broad interest from many countries was observed. Notably, nearly one-half (n=75) of the studies relied on 10 distinct data sets, and 11/145 (7.6%) used demographic data as an input for ML models.

CONCLUSIONS

This SLR revealed considerable interest across multiple countries in using ML techniques for diagnosing and monitoring voice-affecting disorders, with PD being the most studied disorder. However, the review identified several gaps, including limited and unbalanced data set usage in studies, and a focus on diagnostic test rather than disorder-specific monitoring. Despite the limitations of being constrained by only peer-reviewed publications written in English, the SLR provides valuable insights into the current state of research on ML-based voice-affecting disorder diagnosis and monitoring and highlighting areas to address in future research.

Computer-aided screening of aspiration risks in dysphagia with wearable technology: a Systematic Review and meta-analysis on test accuracy

Aspiration caused by dysphagia is a prevalent problem that causes serious health consequences and even death. Traditional diagnostic instruments could induce pain, discomfort, nausea, and radiation exposure. The emergence of wearable technology with computer-aided screening might facilitate continuous or frequent assessments to prompt early and effective management. The objectives of this review are to summarize these systems to identify aspiration risks in dysphagic individuals and inquire about their accuracy. Two authors independently searched electronic databases, including CINAHL, Embase, IEEE Xplore^® Digital Library, PubMed, Scopus, and Web of Science (PROSPERO reference number: CRD42023408960). The risk of bias and applicability were assessed using QUADAS-2. Nine (n = 9) articles applied accelerometers and/or acoustic devices to identify aspiration risks in patients with neurodegenerative problems (e.g., dementia, Alzheimer's disease), neurogenic problems (e.g., stroke, brain injury), in addition to some children with congenital abnormalities, using videofluoroscopic swallowing study (VFSS) or fiberoptic endoscopic evaluation of swallowing (FEES) as the reference standard. All studies employed a traditional machine learning approach with a feature extraction process. Support vector machine (SVM) was the most famous machine learning model used. A meta-analysis was conducted to evaluate the classification accuracy and identify risky swallows. Nevertheless, we decided not to conclude the meta-analysis findings (pooled diagnostic odds ratio: 21.5, 95% CI, 2.7-173.6) because studies had unique methodological characteristics and major differences in the set of parameters/thresholds, in addition to the substantial heterogeneity and variations, with sensitivity levels ranging from 21.7% to 90.0% between studies. Small sample sizes could be a critical problem in existing studies (median = 34.5, range 18-449), especially for machine learning models. Only two out of the nine studies had an optimized model with sensitivity over 90%. There is a need to enlarge the sample size for better generalizability and optimize signal processing, segmentation, feature extraction, classifiers, and their combinations to improve the assessment performance. Systematic Review Registration: (https://www.crd.york.ac.uk/prospero/), identifier (CRD42023408960).

Pilot Study: Magnetic Motion Analysis for Swallowing Detection Using MEMS Cantilever Actuators

The swallowing process involves complex muscle coordination mechanisms. When alterations in such mechanisms are produced by neurological conditions or diseases, a swallowing disorder known as dysphagia occurs. The instrumental evaluation of dysphagia is currently performed by invasive and experience-dependent techniques. Otherwise, non-invasive magnetic methods have proven to be suitable for various biomedical applications and might also be applicable for an objective swallowing assessment. In this pilot study, we performed a novel approach for deglutition evaluation based on active magnetic motion sensing with permanent magnet cantilever actuators. During the intake of liquids with different consistency, we recorded magnetic signals of relative movements between a stationary sensor and a body-worn actuator on the cricoid cartilage. Our results indicate the detection capability of swallowing-related movements in terms of a characteristic pattern. Consequently, the proposed technique offers the potential for dysphagia screening and biofeedback-based therapies.

A Comprehensive Assessment Protocol for Swallowing (CAPS): Paving the Way towards Computer-Aided Dysphagia Screening

Dysphagia is one of the most common problems among older adults, which might lead to aspiration pneumonia and eventual death. It calls for a feasible, reliable, and standardized screening or assessment method to prompt rehabilitation measures and mitigate the risks of dysphagia complications. Computer-aided screening using wearable technology could be the solution to the problem but is not clinically applicable because of the heterogeneity of assessment protocols. The aim of this paper is to formulate and unify a swallowing assessment protocol, named the Comprehensive Assessment Protocol for Swallowing (CAPS), by integrating existing protocols and standards. The protocol consists of two phases: the pre-test phase and the assessment phase. The pre-testing phase involves applying different texture or thickness levels of food/liquid and determining the required bolus volume for the subsequent assessment. The assessment phase involves dry (saliva) swallowing, wet swallowing of different food/liquid consistencies, and non-swallowing (e.g., yawning, coughing, speaking, etc.). The protocol is designed to train the swallowing/non-swallowing event classification that facilitates future long-term continuous monitoring and paves the way towards continuous dysphagia screening.

AI-Based Detection of Aspiration for Video-Endoscopy with Visual Aids in Meaningful Frames to Interpret the Model Outcome

Disorders of swallowing often lead to pneumonia when material enters the airways (aspiration). Flexible Endoscopic Evaluation of Swallowing (FEES) plays a key role in the diagnostics of aspiration but is prone to human errors. An AI-based tool could facilitate this process. Recent non-endoscopic/non-radiologic attempts to detect aspiration using machine-learning approaches have led to unsatisfying accuracy and show black-box characteristics. Hence, for clinical users it is difficult to trust in these model decisions. Our aim is to introduce an explainable artificial intelligence (XAI) approach to detect aspiration in FEES. Our approach is to teach the AI about the relevant anatomical structures, such as the vocal cords and the glottis, based on 92 annotated FEES videos. Simultaneously, it is trained to detect boluses that pass the glottis and become aspirated. During testing, the AI successfully recognized the glottis and the vocal cords but could not yet achieve satisfying aspiration detection quality. While detection performance must be optimized, our architecture results in a final model that explains its assessment by locating meaningful frames with relevant aspiration events and by highlighting suspected boluses. In contrast to comparable AI tools, our framework is verifiable and interpretable and, therefore, accountable for clinical users.

Post-stroke respiratory complications using machine learning with voice features from mobile devices

Abnormal voice may identify those at risk of post-stroke aspiration. This study was aimed to determine whether machine learning algorithms with voice recorded via a mobile device can accurately classify those with dysphagia at risk of tube feeding and post-stroke aspiration pneumonia and be used as digital biomarkers. Voice samples from patients referred for swallowing disturbance in a university-affiliated hospital were collected prospectively using a mobile device. Subjects that required tube feeding were further classified to high risk of respiratory complication, based on the voluntary cough strength and abnormal chest x-ray images. A total of 449 samples were obtained, with 234 requiring tube feeding and 113 showing high risk of respiratory complications. The eXtreme gradient boosting multimodal models that included abnormal acoustic features and clinical variables showed high sensitivity levels of 88.7% (95% CI 82.6–94.7) and 84.5% (95% CI 76.9–92.1) in the classification of those at risk of tube feeding and at high risk of respiratory complications; respectively. In both cases, voice features proved to be the strongest contributing factors in these models. Voice features may be considered as viable digital biomarkers in those at risk of respiratory complications related to post-stroke dysphagia.

The global prevalence of oropharyngeal dysphagia in different populations: a systematic review and meta-analysis

Background

Oropharyngeal dysphagia (OD) refers to any abnormality in the physiology of swallowing in the upper gastrointestinal tract, which leads to the related clinical complications, such as malnutrition, dehydration, and sever complication, such as aspiration pneumonia, suffocation, and eventually, premature death. The previous studies indicated a various range of prevalence of OD. The present systematic review and meta-analysis aimed to standardize the global prevalence of OD in different populations.

Methods

A systematic literature review was conducted using Embase, Scopus, PubMed, Web of Science (WoS) databases, and Google Scholar motor engine using related MeSH/Emtree and Free Text words, with no time limitation until November 2021. The heterogeneity among studies was quantified using I² index and the random effects model was used, due to the high heterogeneity among the results of studies included in the meta-analysis.

Results

The systematic literature search retrieved 2092 studies. After excluding the irrelevant studies, ultimately 27 articles with a sample size of 9841 were included in the meta-analysis. After combining the studies, the overall estimate of the global prevalence rate of OD was 43.8% (95% CI 33.3–54.9%) and the highest prevalence rate was estimated in Africa with 64.2% (95% CI 53.2–73.9%). Given the subgroup analysis based on the study population, the highest prevalence of OD was related to Dementia with 72.4% (95% CI 26.7–95.0%). The results of meta-regression indicated that the prevalence of OD has an increasing trend with the enhancement of year of publication and mean age.

Conclusion

The results of the present systematic review and meta-analysis revealed that the prevalence of OD is high in different populations and its trend has been increasing in recent years. Therefore, the appropriate strategies should be applied to reduce the prevalence of OD by finding its causation and monitoring at all levels, as well as providing feedback to hospitals.

Oral and Laryngeal Diadochokinesis Across the Life Span: A Scoping Review of Methods, Reference Data, and Clinical Applications

PURPOSE

The aim of this study was to conduct a scoping review of research on oral and laryngeal diadochokinesis (DDK) in children and adults, either typically developing/developed or with a clinical diagnosis.

METHOD

Searches were conducted with PubMed/MEDLINE, Google Scholar, CINAHL, and legacy sources in retrieved articles. Search terms included the following: DDK, alternating motion rate, maximum repetition rate, sequential motion rate, and syllable repetition rate.

RESULTS

Three hundred sixty articles were retrieved and included in the review. Data source tables for children and adults list the number and ages of study participants, DDK task, and language(s) spoken. Cross-sectional data for typically developing children and typically developed adults are compiled for the monosyllables /pʌ/, /tʌ/, and /kʌ/; the trisyllable /pʌtʌkʌ/; and laryngeal DDK. In addition, DDK results are summarized for 26 disorders or conditions.

DISCUSSION

A growing number of multidisciplinary reports on DDK affirm its role in clinical practice and research across the world. Atypical DDK is not a well-defined singular entity but rather a label for a collection of disturbances associated with diverse etiologies, including motoric, structural, sensory, and cognitive. The clinical value of DDK can be optimized by consideration of task parameters, analysis method, and population of interest.