1
|
Srinivasan Y, Liu A, Rameau A. Machine learning in the evaluation of voice and swallowing in the head and neck cancer patient. Curr Opin Otolaryngol Head Neck Surg 2024; 32:105-112. [PMID: 38116798 DOI: 10.1097/moo.0000000000000948] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2023]
Abstract
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.
Collapse
Affiliation(s)
- Yashes Srinivasan
- Sean Parker Institute for the Voice, Department of Otolaryngology-Head and Neck Surgery, Weill Cornell Medicine, New York, New York
| | - Amy Liu
- University of California, San Diego, School of Medicine, San Diego, California, USA
| | - Anaïs Rameau
- Sean Parker Institute for the Voice, Department of Otolaryngology-Head and Neck Surgery, Weill Cornell Medicine, New York, New York
| |
Collapse
|
2
|
Shu K, Perera S, Mahoney AS, Mao S, Coyle JL, Sejdić E. Temporal Sequence of Laryngeal Vestibule Closure and Reopening is Associated With Airway Protection. Laryngoscope 2023; 133:521-527. [PMID: 35657100 PMCID: PMC9718890 DOI: 10.1002/lary.30222] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Revised: 05/05/2022] [Accepted: 05/11/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND Upper esophageal sphincter opening (UESO), and laryngeal vestibule closure (LVC) are two essential kinematic events whose timings are crucial for adequate bolus clearance and airway protection during swallowing. Their temporal characteristics can be quantified through time-consuming analysis of videofluoroscopic swallow studies (VFSS). OBJECTIVES We sought to establish a model to predict the odds of penetration or aspiration during swallowing based on 15 temporal factors of UES and laryngeal vestibule kinematics. METHODS Manual temporal measurements and ratings of penetration and aspiration were conducted on a videofluoroscopic dataset of 408 swallows from 99 patients. A generalized estimating equation model was deployed to analyze association between individual factors and the risk of penetration or aspiration. RESULTS The results indicated that the latencies of laryngeal vestibular events and the time lapse between UESO onset and LVC were highly related to penetration or aspiration. The predictive model incorporating patient demographics and bolus presentation showed that delayed LVC by 0.1 s or delayed LVO by 1% of the swallow duration (average 0.018 s) was associated with a 17.19% and 2.68% increase in odds of airway invasion, respectively. CONCLUSION This predictive model provides insight into kinematic factors that underscore the interaction between the intricate timing of laryngeal kinematics and airway protection. Recent investigation in automatic noninvasive or videofluoroscopic detection of laryngeal kinematics would provide clinicians access to objective measurements not commonly quantified in VFSS. Consequently, the temporal and sequential understanding of these kinematics may interpret such measurements to an estimation of the risk of aspiration or penetration which would give rise to rapid computer-assisted dysphagia diagnosis. LEVEL OF EVIDENCE 2 Laryngoscope, 133:521-527, 2023.
Collapse
Affiliation(s)
- Kechen Shu
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Subashan Perera
- Division of Geriatrics, Department of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Department of Biostatistics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Amanda S. Mahoney
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Shitong Mao
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - James L. Coyle
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Department of Otolaryngology, School of Medicine, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Ervin Sejdić
- Edward S. Rogers Department of Electrical and Computer Engineering, Faculty of Applied Science and Engineering, University of Toronto, Toronto, Ontario, Canada
- North York General Hospital, Toronto, Ontario, Canada
| |
Collapse
|
3
|
Khalifa Y, Mahoney AS, Lucatorto E, Coyle JL, Sejdić E. Non-Invasive Sensor-Based Estimation of Anterior-Posterior Upper Esophageal Sphincter Opening Maximal Distension. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2023; 11:182-190. [PMID: 36873304 PMCID: PMC9976940 DOI: 10.1109/jtehm.2023.3246919] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/25/2022] [Revised: 01/25/2023] [Accepted: 02/15/2023] [Indexed: 02/22/2023]
Abstract
OBJECTIVE Dysphagia management relies on the evaluation of the temporospatial kinematic events of swallowing performed in videofluoroscopy (VF) by trained clinicians. The upper esophageal sphincter (UES) opening distension represents one of the important kinematic events that contribute to healthy swallowing. Insufficient distension of UES opening can lead to an accumulation of pharyngeal residue and subsequent aspiration which in turn can lead to adverse outcomes such as pneumonia. VF is usually used for the temporal and spatial evaluation of the UES opening; however, VF is not available in all clinical settings and may be inappropriate or undesirable for some patients. High resolution cervical auscultation (HRCA) is a noninvasive technology that uses neck-attached sensors and machine learning to characterize swallowing physiology by analyzing the swallow-induced vibrations/sounds in the anterior neck region. We investigated the ability of HRCA to noninvasively estimate the maximal distension of anterior-posterior (A-P) UES opening as accurately as the measurements performed by human judges from VF images. METHODS AND PROCEDURES Trained judges performed the kinematic measurement of UES opening duration and A-P UES opening maximal distension on 434 swallows collected from 133 patients. We used a hybrid convolutional recurrent neural network supported by attention mechanisms which takes HRCA raw signals as input and estimates the value of the A-P UES opening maximal distension as output. RESULTS The proposed network estimated the A-P UES opening maximal distension with an absolute percentage error of 30% or less for more than 64.14% of the swallows in the dataset. CONCLUSION This study provides substantial evidence for the feasibility of using HRCA to estimate one of the key spatial kinematic measurements used for dysphagia characterization and management. Clinical and Translational Impact Statement: The findings in this study have a direct impact on dysphagia diagnosis and management through providing a non-invasive and cheap way to estimate one of the most important swallowing kinematics, the UES opening distension, that contributes to safe swallowing. This study, along with other studies that utilize HRCA for swallowing kinematic analysis, paves the way for developing a widely available and easy-to-use tool for dysphagia diagnosis and management.
Collapse
Affiliation(s)
- Yassin Khalifa
- Department of Biomedical EngineeringCairo UniversityGiza12613Egypt
- Department of Electrical and Computer EngineeringSwanson School of EngineeringUniversity of PittsburghPittsburghPA15260USA
- Case Western Reserve University School of MedicineClevelandOH44106USA
- University Hospitals Harrington Heart and Vascular InstituteClevelandOH44106USA
| | - Amanda S. Mahoney
- Department of Communication Science and DisordersUniversity of PittsburghPittsburghPA15260USA
| | - Erin Lucatorto
- Department of Communication Science and DisordersUniversity of PittsburghPittsburghPA15260USA
| | - James L. Coyle
- Department of Communication Science and DisordersUniversity of PittsburghPittsburghPA15260USA
- Department of OtolaryngologyUniversity of PittsburghPittsburghPA15260USA
| | - Ervin Sejdić
- The Edward S. Rogers Sr. Department of Electrical and Computer EngineeringFaculty of Applied Science and EngineeringUniversity of TorontoTorontoONM5S 1A1Canada
- North York General HospitalTorontoONM2K 1E1Canada
| |
Collapse
|
4
|
Khalifa Y, Donohue C, Coyle JL, Sejdic E. Autonomous Swallow Segment Extraction Using Deep Learning in Neck-Sensor Vibratory Signals From Patients With Dysphagia. IEEE J Biomed Health Inform 2023; 27:956-967. [PMID: 36417738 PMCID: PMC10079637 DOI: 10.1109/jbhi.2022.3224323] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
Abstract
Dysphagia occurs secondary to a variety of underlying etiologies and can contribute to increased risk of adverse events such as aspiration pneumonia and premature mortality. Dysphagia is primarily diagnosed and characterized by instrumental swallowing exams such as videofluoroscopic swallowing studies. videofluoroscopic swallowing studies involve the inspection of a series of radiographic images for signs of swallowing dysfunction. Though effective, videofluoroscopic swallowing studies are only available in certain clinical settings and are not always desirable or feasible for certain patients. Because of the limitations of current instrumental swallow exams, research studies have explored the use of acceleration signals collected from neck sensors and demonstrated their potential in providing comparable radiation-free diagnostic value as videofluoroscopic swallowing studies. In this study, we used a hybrid deep convolutional recurrent neural network that can perform multi-level feature extraction (localized and across time) to annotate swallow segments automatically via multi-channel swallowing acceleration signals. In total, we used signals and videofluoroscopic swallowing study images of 3144 swallows from 248 patients with suspected dysphagia. Compared to other deep network variants, our network was superior at detecting swallow segments with an average area under the receiver operating characteristic curve value of 0.82 (95% confidence interval: 0.807-0.841), and was in agreement with up to 90% of the gold standard-labeled segments.
Collapse
|
5
|
Konradi J, Zajber M, Betz U, Drees P, Gerken A, Meine H. AI-Based Detection of Aspiration for Video-Endoscopy with Visual Aids in Meaningful Frames to Interpret the Model Outcome. SENSORS (BASEL, SWITZERLAND) 2022; 22:9468. [PMID: 36502169 PMCID: PMC9736280 DOI: 10.3390/s22239468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/19/2022] [Revised: 11/29/2022] [Accepted: 11/30/2022] [Indexed: 06/17/2023]
Abstract
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.
Collapse
Affiliation(s)
- Jürgen Konradi
- Institute of Physical Therapy, Prevention and Rehabilitation, University Medical Center of the Johannes Gutenberg-University Mainz, 55131 Mainz, Germany
| | - Milla Zajber
- Department for Health Care & Nursing, Catholic University of Applied Sciences, 55122 Mainz, Germany
| | - Ulrich Betz
- Institute of Physical Therapy, Prevention and Rehabilitation, University Medical Center of the Johannes Gutenberg-University Mainz, 55131 Mainz, Germany
| | - Philipp Drees
- Department of Orthopedics and Trauma Surgery, University Medical Center of the Johannes Gutenberg-University Mainz, 55131 Mainz, Germany
| | - Annika Gerken
- Fraunhofer Institute for Digital Medicine MEVIS, 28359 Bremen, Germany
| | - Hans Meine
- Fraunhofer Institute for Digital Medicine MEVIS, 28359 Bremen, Germany
| |
Collapse
|
6
|
Bandini A, Smaoui S, Steele CM. Automated pharyngeal phase detection and bolus localization in videofluoroscopic swallowing study: Killing two birds with one stone? COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2022; 225:107058. [PMID: 35961072 PMCID: PMC9983708 DOI: 10.1016/j.cmpb.2022.107058] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 07/26/2022] [Accepted: 08/03/2022] [Indexed: 06/15/2023]
Abstract
BACKGROUND AND OBJECTIVE The videofluoroscopic swallowing study (VFSS) is a gold-standard imaging technique for assessing swallowing, but analysis and rating of VFSS recordings is time consuming and requires specialized training and expertise. Researchers have recently demonstrated that it is possible to automatically detect the pharyngeal phase of swallowing and to localize the bolus in VFSS recordings via computer vision approaches, fostering the development of novel techniques for automatic VFSS analysis. However, training of algorithms to perform these tasks requires large amounts of annotated data that are seldom available. In this paper, we demonstrate that the challenges of pharyngeal phase detection and bolus localization can be solved together using a single approach. METHODS We propose a deep-learning framework that jointly tackles pharyngeal phase detection and bolus localization in a weakly-supervised manner, requiring only the initial and final frames of the pharyngeal phase as ground truth annotations for the training. Our approach stems from the observation that bolus presence in the pharynx is the most prominent visual feature upon which to infer whether individual VFSS frames belong to the pharyngeal phase. We conducted extensive experiments with multiple convolutional neural networks (CNNs) on a dataset of 1245 bolus-level clips from 59 healthy subjects. RESULTS We demonstrated that the pharyngeal phase can be detected with an F1-score higher than 0.9. Moreover, by processing the class activation maps of the CNNs, we were able to localize the bolus with promising results, obtaining correlations with ground truth trajectories higher than 0.9, without any manual annotations of bolus location used for training purposes. CONCLUSIONS Once validated on a larger sample of participants with swallowing disorders, our framework will pave the way for the development of intelligent tools for VFSS analysis to support clinicians in swallowing assessment.
Collapse
Affiliation(s)
- Andrea Bandini
- KITE Research Institute - Toronto Rehabilitation Institute, University Health Network, 550 University Avenue, Toronto, ON M5G 2A2, Canada; The BioRobotics Institute, Scuola Superiore Sant'Anna, Pisa, Italy; Department of Excellence in Robotics and AI, Scuola Superiore Sant'Anna, Pisa, Italy
| | - Sana Smaoui
- KITE Research Institute - Toronto Rehabilitation Institute, University Health Network, 550 University Avenue, Toronto, ON M5G 2A2, Canada; Rehabilitation Sciences Institute, Temerty Faculty of Medicine, University of Toronto, ON, Canada
| | - Catriona M Steele
- KITE Research Institute - Toronto Rehabilitation Institute, University Health Network, 550 University Avenue, Toronto, ON M5G 2A2, Canada; Rehabilitation Sciences Institute, Temerty Faculty of Medicine, University of Toronto, ON, Canada.
| |
Collapse
|
7
|
da Costa BOI, Dantas AMX, Machado LDS, da Silva HJ, Pernambuco L, Lopes LW. Wearable technology use for the analysis and monitoring of functions related to feeding and communication. Codas 2022; 34:e20210278. [PMID: 35894374 PMCID: PMC9886183 DOI: 10.1590/2317-1782/20212021278pt] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2021] [Accepted: 11/18/2021] [Indexed: 02/03/2023] Open
Affiliation(s)
| | - Alana Moura Xavier Dantas
- Programa de Pós-graduação em Odontologia, Cidade Universitária, Universidade Federal de Pernambuco – UFPE - Recife (PE), Brasil.
| | - Liliane dos Santos Machado
- Programa de Pós-graduação em Modelos de Decisão e Saúde, Universidade Federal da Paraíba – UFPB - João Pessoa (PB), Brasil.
| | - Hilton Justino da Silva
- Programa de Pós-graduação em Odontologia, Cidade Universitária, Universidade Federal de Pernambuco – UFPE - Recife (PE), Brasil.
| | - Leandro Pernambuco
- Programa de Pós-graduação em Modelos de Decisão e Saúde, Universidade Federal da Paraíba – UFPB - João Pessoa (PB), Brasil.
| | - Leonardo Wanderley Lopes
- Programa de Pós-graduação em Modelos de Decisão e Saúde, Universidade Federal da Paraíba – UFPB - João Pessoa (PB), Brasil.
| |
Collapse
|
8
|
Ma JK, Wrench AA. Automated assessment of hyoid movement during normal swallow using ultrasound. INTERNATIONAL JOURNAL OF LANGUAGE & COMMUNICATION DISORDERS 2022; 57:615-629. [PMID: 35285113 PMCID: PMC9314830 DOI: 10.1111/1460-6984.12712] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Accepted: 02/10/2022] [Indexed: 05/31/2023]
Abstract
BACKGROUND The potential for using ultrasound by speech and language therapists (SLTs) as an adjunct clinical tool to assess swallowing function has received increased attention during the COVID-19 pandemic, with a recent review highlighting the need for further research on normative data, objective measurement, elicitation protocol and training. The dynamic movement of the hyoid, visible in ultrasound, is crucial in facilitating bolus transition and protection of the airway during a swallow and has shown promise as a biomarker of swallowing function. AIMS To examine the kinematics of the hyoid during a swallow using ultrasound imaging and to relate the patterns to the different stages of a normal swallow. To evaluate the accuracy and robustness of two different automatic hyoid tracking methods relative to manual hyoid position estimation. METHODS & PROCEDURES Ultrasound data recorded from 15 healthy participants swallowing a 10 ml water bolus delivered by cup or spoon were analysed. The movement of the hyoid was tracked using manually marked frame-to-frame positions, automated hyoid shadow tracking and deep neural net (DNN) tracking. Hyoid displacement along the horizontal image axis (HxD) was charted throughout a swallow, and the maximum horizontal displacement (HxD max) and maximum hyoid velocity (HxV max) along the same axis were automatically calculated. OUTCOMES & RESULTS The HxD and HxV of 10 ml swallows are similar to values reported in the literature. The trajectory of the hyoid movement and its location at significant swallow event time points showed increased hyoid displacement towards the peak of the swallow. Using an interclass correlation coefficient, HxD max and HxV max values derived from the DNN tracker and shadow tracker are shown to be in high agreement and moderate agreement, respectively, when compared with values derived from manual tracking. CONCLUSIONS & IMPLICATIONS The similarity of the hyoid tracking results using ultrasound to previous reports based on different instrumental tools supports the possibility of using hyoid movement as a measure of swallowing function in ultrasound. The use of machine learning to automatically track the hyoid movement potentially provides a reliable and efficient way to quantify swallowing function. These findings contribute towards improving the clinical utility of ultrasound as a swallowing assessment tool. Further research on both normative and clinical populations is needed to validate hyoid movement metrics as a means of differentiating normal and abnormal swallows and to verify the reliability of automatic tracking. WHAT THIS PAPER ADDS What is already known on this subject There is growing interest in the use of ultrasound as an adjunct tool for assessing swallowing function. However, there is currently insufficient knowledge about the patterning and timing of lingual and hyoid movement in a typical swallow. We know that movement of the hyoid plays an essential role in bolus transition and airway protection. However, manual tracking of hyoid movement is time-consuming and restricts the extent of large-scale normative studies. What this study adds We show that hyoid movement can be tracked automatically, providing measurable continuous positional data. Measurements derived from this objective data are comparable with similar measures previously reported using videofluoroscopy and of the two automatic trackers assessed, the DNN approach demonstrates better robustness and higher agreement with manually derived measures. Using this kinematic data, hyoid movement can be related to different stages of swallowing. Clinical implications of this study This study contributes towards our understanding of the kinematics of a typical swallow by evaluating an automated hyoid tracking method, paving the way for future studies of typical and disordered swallow. The challenges of image acquisition highlight issues to be considered when establishing clinical protocols. The application of machine learning enhances the utility of ultrasound swallowing assessment by reducing the labour required and permitting a wider range of hyoid measurements. Further research in normative and clinical populations is facilitated by automatic data extraction allowing the validity of prospective hyoid measures in differentiating different types of swallows to be rigorously assessed.
Collapse
Affiliation(s)
- Joan K.‐Y. Ma
- Clinical Audiology, Speech and Language Research CentreQueen Margaret UniversityEdinburghUK
| | - Alan A. Wrench
- Clinical Audiology, Speech and Language Research CentreQueen Margaret UniversityEdinburghUK
- Articulate Instruments LtdEdinburgh, EH21 6UUUK
| |
Collapse
|
9
|
Are Oropharyngeal Dysphagia Screening Tests Effective in Preventing Pneumonia? J Clin Med 2022; 11:jcm11020370. [PMID: 35054063 PMCID: PMC8780873 DOI: 10.3390/jcm11020370] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2021] [Revised: 01/08/2022] [Accepted: 01/12/2022] [Indexed: 12/15/2022] Open
Abstract
Oropharyngeal dysphagia, a clinical condition that indicates difficulty in moving food and liquid from the oral cavity to the esophagus, has a markedly high prevalence in the elderly. The number of elderly people with oropharyngeal dysphagia is expected to increase due to the aging of the world's population. Understanding the current situation of dysphagia screening is crucial when considering future countermeasures. We report findings from a literature review including citations on current objective dysphagia screening tests: the Water Swallowing Test, Mann Assessment of Swallowing Ability, and the Gugging Swallowing Screen. Pneumonia can be predicted using the results of the screening tests discussed in this review, and the response after the screening tests is important for prevention. In addition, although interdisciplinary team approaches prevent and reduce aspiration, optimal treatment is a challenging. Intervention studies with multiple factors focusing on the elderly are needed.
Collapse
|
10
|
George MM, Tolley NS. AIM in Otolaryngology and Head and Neck Surgery. Artif Intell Med 2022. [DOI: 10.1007/978-3-030-64573-1_198] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/09/2022]
|
11
|
Frakking TT, Chang AB, Carty C, Newing J, Weir KA, Schwerin B, So S. Using an Automated Speech Recognition Approach to Differentiate Between Normal and Aspirating Swallowing Sounds Recorded from Digital Cervical Auscultation in Children. Dysphagia 2022; 37:1482-1492. [PMID: 35092488 PMCID: PMC9643257 DOI: 10.1007/s00455-022-10410-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 01/19/2022] [Indexed: 12/16/2022]
Abstract
Use of machine learning to accurately detect aspirating swallowing sounds in children is an evolving field. Previously reported classifiers for the detection of aspirating swallowing sounds in children have reported sensitivities between 79 and 89%. This study aimed to investigate the accuracy of using an automatic speaker recognition approach to differentiate between normal and aspirating swallowing sounds recorded from digital cervical auscultation in children. We analysed 106 normal swallows from 23 healthy children (median 13 months; 52.1% male) and 18 aspirating swallows from 18 children (median 10.5 months; 61.1% male) who underwent concurrent videofluoroscopic swallow studies with digital cervical auscultation. All swallowing sounds were on thin fluids. A support vector machine classifier with a polynomial kernel was trained on feature vectors that comprised the mean and standard deviation of spectral subband centroids extracted from each swallowing sound in the training set. The trained support vector machine was then used to classify swallowing sounds in the test set. We found high accuracy in the differentiation of aspirating and normal swallowing sounds with 98% overall accuracy. Sensitivity for the detection of aspiration and normal swallowing sounds were 89% and 100%, respectively. There were consistent differences in time, power spectral density and spectral subband centroid features between aspirating and normal swallowing sounds in children. This study provides preliminary research evidence that aspirating and normal swallowing sounds in children can be differentiated accurately using machine learning techniques.
Collapse
Affiliation(s)
- Thuy T. Frakking
- Research Development Unit, Caboolture Hospital, Metro North Hospital & Health Service, McKean St, Caboolture, QLD 4510 Australia ,Centre for Clinical Research, School of Medicine, The University of Queensland, Herston, QLD 4029 Australia ,Speech Pathology Department, Gold Coast University Hospital, Gold Coast Hospital & Health Service, 1 Hospital Boulevard, Southport, QLD 4215 Australia
| | - Anne B. Chang
- Department of Respiratory Medicine, Queensland Children’s Hospital, 501 Stanley St, South Brisbane, QLD 4101 Australia ,Child Health Division, Menzies School of Health Research, Charles Darwin University, PO Box 41096, Casuarina, NT 0811 Australia ,Australian Centre for Health Services Innovation, Queensland University of Technology, Level 7, 62 Graham St, South Brisbane, QLD 4101 Australia
| | - Christopher Carty
- Research Development Unit, Caboolture Hospital, Metro North Hospital & Health Service, McKean St, Caboolture, QLD 4510 Australia ,Griffith Centre of Biomedical and Rehabilitation Engineering, Menzies Health Institute Queensland, Griffith University, Gold Coast, 4222 Australia
| | - Jade Newing
- School of Engineering and Built Environment, Griffith University, Parklands Dr, Southport, QLD 4215 Australia
| | - Kelly A. Weir
- Menzies Health Institute QLD & School of Health Sciences & Social Work, Griffith University, Gold Coast Campus, 1 Parklands Avenue, Southport, QLD 4222 Australia ,Allied Health Research, Gold Coast University Hospital, Gold Coast Hospital & Health Service, 1 Hospital Boulevard, Southport, QLD 4215 Australia
| | - Belinda Schwerin
- School of Engineering and Built Environment, Griffith University, Parklands Dr, Southport, QLD 4215 Australia
| | - Stephen So
- School of Engineering and Built Environment, Griffith University, Parklands Dr, Southport, QLD 4215 Australia
| |
Collapse
|
12
|
Schwartz R, Khalifa Y, Lucatorto E, Perera S, Coyle J, Sejdic E. A Preliminary Investigation of Similarities of High Resolution Cervical Auscultation Signals Between Thin Liquid Barium and Water Swallows. IEEE JOURNAL OF TRANSLATIONAL ENGINEERING IN HEALTH AND MEDICINE 2022; 10:4900109. [PMID: 34963825 PMCID: PMC8694539 DOI: 10.1109/jtehm.2021.3134926] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 08/27/2021] [Accepted: 12/03/2021] [Indexed: 11/06/2022]
Abstract
Dysphagia, commonly referred to as abnormal swallowing, affects millions of people annually. If not diagnosed expeditiously, dysphagia can lead to more severe complications, such as pneumonia, nutritional deficiency, and dehydration. Bedside screening is the first step of dysphagia characterization and is usually based on pass/fail tests in which a nurse observes the patient performing water swallows to look for dysphagia overt signs such as coughing. Though quick and convenient, bedside screening only provides low-level judgment of impairment, lacks standardization, and suffers from subjectivity. Recently, high resolution cervical auscultation (HRCA) has been investigated as a less expensive and non-invasive method to diagnose dysphagia. It has shown strong preliminary evidence of its effectiveness in penetration-aspiration detection as well as multiple swallow kinematics. HRCA signals have traditionally been collected and investigated in conjunction with videofluoroscopy exams which are performed using barium boluses including thin liquid. An HRCA-based bedside screening is highly desirable to expedite the initial dysphagia diagnosis and overcome all the drawbacks of the current pass/fail screening tests. However, all research conducted for using HRCA in dysphagia is based on thin liquid barium boluses and thus not guaranteed to provide valid results for water boluses used in bedside screening. If HRCA signals show no significant differences between water and thin liquid barium boluses, then the same algorithms developed on thin liquid barium boluses used in diagnostic imaging studies, it can be then directly used with water boluses. This study investigates the similarities and differences between HRCA signals from thin liquid barium swallows compared to those signals from water swallows. Multiple features from the time, frequency, time-frequency, and information-theoretic domain were extracted from each type of swallow and a group of linear mixed models was tested to determine the significance of differences. Machine learning classifiers were fit to the data as well to determine if the swallowed material (thin liquid barium or water) can be correctly predicted from an unlabeled set of HRCA signals. The results demonstrated that there is no systematic difference between the HRCA signals of thin liquid barium swallows and water swallows. While no systematic difference was discovered, the evidence of complete conformity between HRCA signals of both materials was inconclusive. These results must be validated further to confirm conformity between the HRCA signals of thin liquid barium swallows and water swallows.
Collapse
Affiliation(s)
- Ryan Schwartz
- Department of Electrical and Computer EngineeringSwanson School of EngineeringUniversity of Pittsburgh Pittsburgh PA 15261 USA
| | - Yassin Khalifa
- Department of Electrical and Computer EngineeringSwanson School of EngineeringUniversity of Pittsburgh Pittsburgh PA 15261 USA
| | - Erin Lucatorto
- Department of Communication Science and DisordersSchool of Health and Rehabilitation SciencesUniversity of Pittsburgh Pittsburgh PA 15260 USA
| | - Subashan Perera
- Division of Geriatric MedicineDepartment of MedicineUniversity of Pittsburgh Pittsburgh PA 15261 USA
| | - James Coyle
- Department of Communication Science and DisordersSchool of Health and Rehabilitation SciencesUniversity of Pittsburgh Pittsburgh PA 15260 USA
| | - Ervin Sejdic
- Department of Electrical and Computer EngineeringSwanson School of EngineeringUniversity of Pittsburgh Pittsburgh PA 15261 USA
- The Edward S. Rogers Department of Electrical and Computer EngineeringFaculty of Applied Science and EngineeringUniversity of Toronto Toronto ON M5S 2E4 Canada
- North York General Hospital Toronto ON M2K 1E1 Canada
| |
Collapse
|
13
|
Garand KL, Beall J, Hill EG, Davidson K, Blair J, Pearson W, Martin-Harris B. Effects of Presbyphagia on Oropharyngeal Swallowing Observed during Modified Barium Swallow Studies. J Nutr Health Aging 2022; 26:973-980. [PMID: 36437764 PMCID: PMC10324474 DOI: 10.1007/s12603-022-1854-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
OBJECTIVES Understanding how aging impacts swallowing can help differentiate typical from atypical behaviors. This study aimed to quantify age-related swallowing alterations observed during a modified barium swallow study. DESIGN Cross-sectional study. SETTING Adult fluoroscopy suite in a metropolitan hospital at an academic center. PARTICIPANTS 195 healthy adults distributed across 3 age categories: 21-39; 40-59; 60+ years. MEASUREMENTS 17 physiologic components of swallowing across three functional domains (oral, pharyngeal, esophageal), including summed composite scores (Oral Total [OT] and Pharyngeal Total [PT]), from the validated and standardized Modified Barium Swallow Impairment Profile. RESULTS Most components (65%) demonstrated no impairment (scores of "0"). The odds of a worse (higher) score increased significantly with age for: Tongue Control during Bolus Hold, Hyolaryngeal Movement, Laryngeal Closure, Pharyngeal Contraction, and Pharyngoesophageal Segment Opening. OT and PT scores for 40-59-year-olds were worse than the youngest group (p=.01 and p <.001, respectively). Adults 60+ years had significantly worse PT scores among all groups (p-values <.01). CONCLUSION Oropharyngeal swallowing physiology evolves as healthy adults age and should be considered during clinical decision-making.
Collapse
Affiliation(s)
- K L Garand
- Kendrea Garand, University of South Alabama, Mobile, AL, USA,
| | | | | | | | | | | | | |
Collapse
|
14
|
Costa BOID, Dantas AMX, Machado LDS, Silva HJD, Pernambuco L, Lopes LW. Wearable technology use for the analysis and monitoring of functions related to feeding and communication. Codas 2022. [DOI: 10.1590/2317-1782/20212021278en] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/22/2022] Open
|
15
|
Characterizing Effortful Swallows from Healthy Community Dwelling Adults Across the Lifespan Using High-Resolution Cervical Auscultation Signals and MBSImP Scores: A Preliminary Study. Dysphagia 2021; 37:1103-1111. [PMID: 34537905 PMCID: PMC8449695 DOI: 10.1007/s00455-021-10368-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 09/10/2021] [Indexed: 11/16/2022]
Abstract
There is growing enthusiasm to develop inexpensive, non-invasive, and portable methods that accurately assess swallowing and provide biofeedback during dysphagia treatment. High-resolution cervical auscultation (HRCA), which uses acoustic and vibratory signals from non-invasive sensors attached to the anterior laryngeal framework during swallowing, is a novel method for quantifying swallowing physiology via advanced signal processing and machine learning techniques. HRCA has demonstrated potential as a dysphagia screening method and diagnostic adjunct to VFSSs by determining swallowing safety, annotating swallow kinematic events, and classifying swallows between healthy participants and patients with a high degree of accuracy. However, its feasibility as a non-invasive biofeedback system has not been explored. This study investigated 1. Whether HRCA can accurately differentiate between non-effortful and effortful swallows; 2. Whether differences exist in Modified Barium Swallow Impairment Profile (MBSImP) scores (#9, #11, #14) between non-effortful and effortful swallows. We hypothesized that HRCA would accurately classify non-effortful and effortful swallows and that differences in MBSImP scores would exist between the types of swallows. We analyzed 247 thin liquid 3 mL command swallows (71 effortful) to minimize variation from 36 healthy adults who underwent standardized VFSSs with concurrent HRCA. Results revealed differences (p < 0.05) in 9 HRCA signal features between non-effortful and effortful swallows. Using HRCA signal features as input, decision trees classified swallows with 76% accuracy, 76% sensitivity, and 77% specificity. There were no differences in MBSImP component scores between non-effortful and effortful swallows. While preliminary in nature, this study demonstrates the feasibility/promise of HRCA as a biofeedback method for dysphagia treatment.
Collapse
|
16
|
Donohue C, Khalifa Y, Mao S, Perera S, Sejdić E, Coyle JL. Characterizing Swallows From People With Neurodegenerative Diseases Using High-Resolution Cervical Auscultation Signals and Temporal and Spatial Swallow Kinematic Measurements. JOURNAL OF SPEECH, LANGUAGE, AND HEARING RESEARCH : JSLHR 2021; 64:3416-3431. [PMID: 34428093 PMCID: PMC8642099 DOI: 10.1044/2021_jslhr-21-00134] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/09/2021] [Revised: 04/21/2021] [Accepted: 05/21/2021] [Indexed: 06/13/2023]
Abstract
Purpose The prevalence of dysphagia in patients with neurodegenerative diseases (ND) is alarmingly high and frequently results in morbidity and accelerated mortality due to subsequent adverse events (e.g., aspiration pneumonia). Swallowing in patients with ND should be continuously monitored due to the progressive disease nature. Access to instrumental swallow evaluations can be challenging, and limited studies have quantified changes in temporal/spatial swallow kinematic measures in patients with ND. High-resolution cervical auscultation (HRCA), a dysphagia screening method, has accurately differentiated between safe and unsafe swallows, identified swallow kinematic events (e.g., laryngeal vestibule closure [LVC]), and classified swallows between healthy adults and patients with ND. This study aimed to (a) compare temporal/spatial swallow kinematic measures between patients with ND and healthy adults and (b) investigate HRCA's ability to annotate swallow kinematic events in patients with ND. We hypothesized there would be significant differences in temporal/spatial swallow measurements between groups and that HRCA would accurately annotate swallow kinematic events in patients with ND. Method Participants underwent videofluoroscopic swallowing studies with concurrent HRCA. We used linear mixed models to compare temporal/spatial swallow measurements (n = 170 ND patient swallows, n = 171 healthy adult swallows) and deep learning machine-learning algorithms to annotate specific temporal and spatial kinematic events in swallows from patients with ND. Results Differences (p < .05) were found between groups for several temporal and spatial swallow kinematic measures. HRCA signal features were used as input to machine-learning algorithms and annotated upper esophageal sphincter (UES) opening, UES closure, LVC, laryngeal vestibule reopening, and hyoid bone displacement with 66.25%, 85%, 68.18%, 70.45%, and 44.6% accuracy, respectively, compared to human judges' measurements. Conclusion This study demonstrates HRCA's potential in characterizing swallow function in patients with ND and other patient populations.
Collapse
Affiliation(s)
- Cara Donohue
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, PA
| | - Yassin Khalifa
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, PA
| | - Shitong Mao
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, PA
| | - Subashan Perera
- Division of Geriatric Medicine, Department of Medicine, University of Pittsburgh, PA
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, PA
- Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, PA
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, PA
- Intelligent Systems Program, School of Computing and Information, University of Pittsburgh, PA
| | - James L. Coyle
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, PA
- Department of Otolaryngology, School of Medicine, University of Pittsburgh Medical Center, PA
| |
Collapse
|
17
|
Donohue C, Khalifa Y, Mao S, Perera S, Sejdić E, Coyle JL. Establishing Reference Values for Temporal Kinematic Swallow Events Across the Lifespan in Healthy Community Dwelling Adults Using High-Resolution Cervical Auscultation. Dysphagia 2021; 37:664-675. [PMID: 34018024 DOI: 10.1007/s00455-021-10317-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2020] [Accepted: 05/13/2021] [Indexed: 11/29/2022]
Abstract
Few research studies have investigated temporal kinematic swallow events in healthy adults to establish normative reference values. Determining cutoffs for normal and disordered swallowing is vital for differentially diagnosing presbyphagia, variants of normal swallowing, and dysphagia; and for ensuring that different swallowing research laboratories produce consistent results in common measurements from different samples within the same population. High-resolution cervical auscultation (HRCA), a sensor-based dysphagia screening method, has accurately annotated temporal kinematic swallow events in patients with dysphagia, but hasn't been used to annotate temporal kinematic swallow events in healthy adults to establish dysphagia screening cutoffs. This study aimed to determine: (1) Reference values for temporal kinematic swallow events, (2) Whether HRCA can annotate temporal kinematic swallow events in healthy adults. We hypothesized (1) Our reference values would align with a prior study; (2) HRCA would detect temporal kinematic swallow events as accurately as human judges. Trained judges completed temporal kinematic measurements on 659 swallows (N = 70 adults). Swallow reaction time and LVC duration weren't different (p > 0.05) from a previously published historical cohort (114 swallows, N = 38 adults), while other temporal kinematic measurements were different (p < 0.05), suggesting a need for further standardization to feasibly pool data analyses across laboratories. HRCA signal features were used as input to machine learning algorithms and annotated UES opening (69.96% accuracy), UES closure (64.52% accuracy), LVC (52.56% accuracy), and LV re-opening (69.97% accuracy); providing preliminary evidence that HRCA can noninvasively and accurately annotate temporal kinematic measurements in healthy adults to determine dysphagia screening cutoffs.
Collapse
Affiliation(s)
- Cara Donohue
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, 6035 Forbes Tower, Pittsburgh, PA, 15260, USA
| | - Yassin Khalifa
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Shitong Mao
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Subashan Perera
- Division of Geriatrics, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15260, USA.,Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15260, USA.,Department of Biomedical Informatics, School of Medicine Intelligent Systems Program, School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - James L Coyle
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, 6035 Forbes Tower, Pittsburgh, PA, 15260, USA. .,Department of Otolaryngology, School of Medicine, University of Pittsburgh Medical Center, Pittsburgh, PA, 15260, USA.
| |
Collapse
|
18
|
Rahmani MH, Berkvens R, Weyn M. Chest-Worn Inertial Sensors: A Survey of Applications and Methods. SENSORS (BASEL, SWITZERLAND) 2021; 21:2875. [PMID: 33921900 PMCID: PMC8074221 DOI: 10.3390/s21082875] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/08/2021] [Revised: 04/14/2021] [Accepted: 04/14/2021] [Indexed: 01/16/2023]
Abstract
Inertial Measurement Units (IMUs) are frequently implemented in wearable devices. Thanks to advances in signal processing and machine learning, applications of IMUs are not limited to those explicitly addressing body movements such as Activity Recognition (AR). On the other hand, wearing IMUs on the chest offers a few advantages over other body positions. AR and posture analysis, cardiopulmonary parameters estimation, voice and swallowing activity detection and other measurements can be approached through chest-worn inertial sensors. This survey tries to introduce the applications that come with the chest-worn IMUs and summarizes the existing methods, current challenges and future directions associated with them. In this regard, this paper references a total number of 57 relevant studies from the last 10 years and categorizes them into seven application areas. We discuss the inertial sensors used as well as their placement on the body and their associated validation methods based on the application categories. Our investigations show meaningful correlations among the studies within the same application categories. Then, we investigate the data processing architectures of the studies from the hardware point of view, indicating a lack of effort on handling the main processing through on-body units. Finally, we propose combining the discussed applications in a single platform, finding robust ways for artifact cancellation, and planning optimized sensing/processing architectures for them, to be taken more seriously in future research.
Collapse
Affiliation(s)
| | | | - Maarten Weyn
- IDLab-Faculty of Applied Engineering, University of Antwerp-imec, Sint-Pietersvliet 7, 2000 Antwerp, Belgium; (M.H.R.); (R.B.)
| |
Collapse
|
19
|
George MM, Tolley NS. AIM in Otolaryngology and Head & Neck Surgery. Artif Intell Med 2021. [DOI: 10.1007/978-3-030-58080-3_198-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
|
20
|
Donohue C, Khalifa Y, Perera S, Sejdić E, Coyle JL. How Closely do Machine Ratings of Duration of UES Opening During Videofluoroscopy Approximate Clinician Ratings Using Temporal Kinematic Analyses and the MBSImP? Dysphagia 2020; 36:707-718. [PMID: 32955619 DOI: 10.1007/s00455-020-10191-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Accepted: 09/14/2020] [Indexed: 10/23/2022]
Abstract
Clinicians evaluate swallow kinematic events by analyzing videofluoroscopy (VF) images for dysphagia management. The duration of upper esophageal sphincter opening (DUESO) is one important temporal swallow event, because reduced DUESO can result in pharyngeal residue and penetration/aspiration. VF is frequently used for evaluating swallowing but exposes patients to radiation and is not always feasible/readily available. High resolution cervical auscultation (HRCA) is a non-invasive, sensor-based dysphagia screening method that uses signal processing and machine learning to characterize swallowing. We investigated HRCA's ability to annotate DUESO and predict Modified Barium Swallow Impairment Profile (MBSImP) scores (component #14). We hypothesized that HRCA and machine learning techniques would detect DUESO with similar accuracy as human judges. Trained judges completed temporal kinematic measurements of DUESO on 719 swallows (116 patients) and 50 swallows (15 age-matched healthy adults). An MBSImP certified clinician completed MBSImP ratings on 100 swallows. A multi-layer convolutional recurrent neural network (CRNN) using HRCA signal features for input was used to detect DUESO. Generalized estimating equations models were used to determine statistically significant HRCA signal features for predicting DUESO MBSImP scores. A support vector machine (SVM) classifier and a leave-one-out procedure was used to predict DUESO MBSImP scores. The CRNN detected UES opening within a 3-frame tolerance for 82.6% of patient and 86% of healthy swallows and UES closure for 72.3% of patient and 64% of healthy swallows. The SVM classifier predicted DUESO MBSImP scores with 85.7% accuracy. This study provides evidence of HRCA's feasibility in detecting DUESO without VF images.
Collapse
Affiliation(s)
- Cara Donohue
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, 6035 Forbes Tower, Pittsburgh, PA, 15260, USA
| | - Yassin Khalifa
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Subashan Perera
- Division of Geriatrics, Department of Medicine, University of Pittsburgh, Pittsburgh, PA, 15261, USA
| | - Ervin Sejdić
- Department of Electrical and Computer Engineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15260, USA.,Department of Bioengineering, Swanson School of Engineering, University of Pittsburgh, Pittsburgh, PA, 15260, USA.,Department of Biomedical Informatics, School of Medicine, University of Pittsburgh, Pittsburgh, PA, 15260, USA.,Intelligent Systems Program, School of Computing and Information, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - James L Coyle
- Department of Communication Science and Disorders, School of Health and Rehabilitation Sciences, University of Pittsburgh, 6035 Forbes Tower, Pittsburgh, PA, 15260, USA.
| |
Collapse
|