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

Lim, Hyo-Jung; Lai, Derek Ka-Hei; So, Bryan Pak-Hei; Yip, C.K.; Cheung, Daphne Sze Ki; Cheung, James Chung-Wai; Wong, Duo Wai-Chi

doi:10.3390/ijerph20042998

Cited by 5 publications

(4 citation statements)

References 39 publications

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“…In addition, only one study accounted for the non-swallowing task. A previous review commented that protocol heterogeneity might hinder the translational potential of wearable technology on swallowing assessment ( So et al, 2023 ) and that a unified framework was necessary to account for both swallowing and non-swallowing activities ( Lim et al, 2023 ).…”

Section: Discussionmentioning

confidence: 99%

“…As dysphagia or neurodegenerative problems deteriorate gradually and aspiration could occur unexpectedly ( Lim et al, 2023 ), it is necessary to develop accessible and reliable instrumental screening tools that enable continuous or frequent assessments of aspiration risks. Wearable technology with computer-aided diagnosis/screening might be a potential alternative to bedside questionnaires and instrumental diagnostic instruments (such as VFSS and FEES) ( So et al, 2023 ).…”

Section: Introductionmentioning

confidence: 99%

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

Lai

Cheng

Lim

et al. 2023

Front. Bioeng. Biotechnol.

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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).

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Lai

Cheng

Lim

et al. 2023

Front. Bioeng. Biotechnol.

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“…Secondly, our protocol design did not purport to cover every swallowing task. While we took reference from the comprehensive assessment protocol for swallowing (CAPS) [91], identifying the fewest swallowing tasks necessary to accurately depict swallowing functions would be helpful to develop the instrument for dysphagia screening and lessen the time and inconvenience during the assessment, which warrant further investigations. The inclination of the camera was determined based on our pilot experiment that better captured the frontal view of the neck area.…”

Section: Discussionmentioning

confidence: 99%

Transformer Models and Convolutional Networks with Different Activation Functions for Swallow Classification Using Depth Video Data

Lai

Cheng

et al. 2023

Mathematics

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Dysphagia is a common geriatric syndrome that might induce serious complications and death. Standard diagnostics using the Videofluoroscopic Swallowing Study (VFSS) or Fiberoptic Evaluation of Swallowing (FEES) are expensive and expose patients to risks, while bedside screening is subjective and might lack reliability. An affordable and accessible instrumented screening is necessary. This study aimed to evaluate the classification performance of Transformer models and convolutional networks in identifying swallowing and non-swallowing tasks through depth video data. Different activation functions (ReLU, LeakyReLU, GELU, ELU, SiLU, and GLU) were then evaluated on the best-performing model. Sixty-five healthy participants (n = 65) were invited to perform swallowing (eating a cracker and drinking water) and non-swallowing tasks (a deep breath and pronouncing vowels: “/eɪ/”, “/iː/”, “/aɪ/”, “/oʊ/”, “/u:/”). Swallowing and non-swallowing were classified by Transformer models (TimeSFormer, Video Vision Transformer (ViViT)), and convolutional neural networks (SlowFast, X3D, and R(2+1)D), respectively. In general, convolutional neural networks outperformed the Transformer models. X3D was the best model with good-to-excellent performance (F1-score: 0.920; adjusted F1-score: 0.885) in classifying swallowing and non-swallowing conditions. Moreover, X3D with its default activation function (ReLU) produced the best results, although LeakyReLU performed better in deep breathing and pronouncing “/aɪ/” tasks. Future studies shall consider collecting more data for pretraining and developing a hyperparameter tuning strategy for activation functions and the high dimensionality video data for Transformer models.

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“…21,22 The clinical bedside swallowing examination (CBSE) is a noninstrumental assessment used to acquire essential cognitive and behavioral information on physical and functional swallowing abilities. 23 It is a sufficient aspiration screening tool, and in particular, it provides a thorough and organized evaluation of swallowing components and an assessment of the risk of dysphagia. 24 During this clinical assessment, a food bolus is given to the patient, and swallowing is examined by the speech and language pathologist (SLP).…”

Section: Introductionmentioning

confidence: 99%

Exploring the diagnostic accuracy and applicability of the Gugging Swallowing Screen in children with feeding and/or swallowing disorders

Georgiou,

Voniati,

Papaleontiou

et al. 2024

Neurogastroenterology Motil

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BackgroundThe Gugging Swallowing Screen (GUSS) is a bedside dysphagia screening tool that has been designed to determine the risk of aspiration in acute stroke patients. There is no evidence in the literature for the GUSS for the pediatric population. The present study aimed to determine the diagnostic accuracy of GUSS as a screening tool in the Greek language for children with dysphagia.MethodsEighty‐Greek‐Cypriot children aged 3–12 years who had dysphagia participated in this retrospective study. The translated into Greek GUSS was administered twice (pre‐ and post‐therapy) to each patient throughout 24 sessions of dysphagia therapy.Key ResultsThe GUSS showed a high internal consistency (Cronbach's α = 0.826), good test–retest reliability (rs = 0.767), convergent validity compared to the Greek Pediatric Eating Assessment tool‐10 (PEDI‐EAT‐10) total score (rs = −0.365), and inter‐rater reliability (κ = 0.863). A total cutoff points equal to 13.00 was also calculated. Aspiration was identified by the GUSS with low sensitivity and high specificity (PPV 100%, NPV 57%, LR+ NA, LR− 0.79); dysphagia/penetration was identified with high sensitivity and low specificity (PPV 33%, NPV 100%, LR+ 0.102, LR− NA).Conclusions & InferencesThe pediatric version of GUSS has been found to be a valuable tool in identifying the risk of aspiration as that of adults. It proved to be used as a good screening guide for selecting and confirming the existence of dysphagia from instrumental assessments. This is the first study of the pediatric version of GUSS, and future studies on this topic are needed.

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

Cited by 5 publications

References 39 publications

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

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

Transformer Models and Convolutional Networks with Different Activation Functions for Swallow Classification Using Depth Video Data

Exploring the diagnostic accuracy and applicability of the Gugging Swallowing Screen in children with feeding and/or swallowing disorders

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