Deep Learning-Based Swallowing Monitor for Realtime Detection of Swallow Duration

Kuramoto, Naomi; Ichimura, Kazuhiro; Jayatilake, Dushyantha; Shimokakimoto, Tomoya; Hidaka, Kikue; Suzuki, Kenji

doi:10.1109/embc44109.2020.9176721

Cited by 11 publications

(10 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Kuramoto et al (2020) evaluated swallowing sounds by pattern analysis. We used a machine learning algorithm to evaluate sound quality in short frames and quantify the intensity of swallowing strength (Kuramoto et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

“…The sternal notch is a good landmark and serves as a stable site for auscultation, and this may be a factor in the relative stability of measurements from recordings made with the electronic stethoscope at the top of the sternum compared with the results of previous studies using recordings made with a laryngeal microphone at the side of the neck. Kuramoto et al (2020) evaluated swallowing sounds by pattern analysis. We used a machine learning algorithm to evaluate sound quality in short frames and quantify the intensity of swallowing strength (Kuramoto et al, 2020).…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Real‐time assessment of swallowing sound using an electronic stethoscope and an artificial intelligence system

Suzuki

Shimizu

Ohshimo

et al. 2022

Clinical & Exp Dental Res

View full text Add to dashboard Cite

Objectives: Daily assessments of swallowing function and interventions such as rehabilitation and dietary adjustments are necessary to improve dysphagia. Cervical auscultation is convenient for health care providers for assessing swallowing ability.Although this method allows for swallowing sound evaluations, sensory evaluations with this method are difficult. Thus, we aimed to assess swallowing sound by the combined use of an electronic stethoscope and an artificial intelligence (AI) system that incorporates sound recognition.Material and Methods: Herein, 20 fifth-year dentistry student volunteers were included; each participant was drank 10 ml and then 20 ml of water in different positions (sitting and supine). We developed an algorithm for indexing bolus inflow sounds using AI, which compared the swallowing sounds and created a new index. Results:The new index value used for swallowing sound was significantly higher in men than in women and in the sitting position than in the supine position. A software for acoustic analysis confirmed that the swallowing index was significantly higher in men than in women as well as in the sitting position than in the supine position.These results were similar to those obtained using the new index. However, the new index substantially differed between sexes in terms of posture compared with effective sound pressure. Conclusions:We developed a new algorithm for indexing swallowing sounds using a stethoscope and an AI system, which could identify swallowing sounds. For future research and development, evaluations of patients with dysphagia are necessary to determine the efficacy of the new index for bedside screening of swallowing conditions.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Real‐time assessment of swallowing sound using an electronic stethoscope and an artificial intelligence system

Suzuki

Shimizu

Ohshimo

et al. 2022

Clinical & Exp Dental Res

View full text Add to dashboard Cite

show abstract

“…Advances in machine learning confer the possibility of further improvement of accuracy when detecting swallowing sounds [22][23][24][25]. Khlaifi et al combined the Mel Frequency Cepstral Coefficient (MFCC) and a Gaussian Mixture Model (GMM) to achieve an 84.57% recognition rate [23].…”

Section: Introductionmentioning

confidence: 99%

Novel Approach Combining Shallow Learning and Ensemble Learning for the Automated Detection of Swallowing Sounds in a Clinical Database

Kimura,

Emoto,

Suzuki

et al. 2024

Sensors

View full text Add to dashboard Cite

Cervical auscultation is a simple, noninvasive method for diagnosing dysphagia, although the reliability of the method largely depends on the subjectivity and experience of the evaluator. Recently developed methods for the automatic detection of swallowing sounds facilitate a rough automatic diagnosis of dysphagia, although a reliable method of detection specialized in the peculiar feature patterns of swallowing sounds in actual clinical conditions has not been established. We investigated a novel approach for automatically detecting swallowing sounds by a method wherein basic statistics and dynamic features were extracted based on acoustic features: Mel Frequency Cepstral Coefficients and Mel Frequency Magnitude Coefficients, and an ensemble learning model combining Support Vector Machine and Multi-Layer Perceptron were applied. The evaluation of the effectiveness of the proposed method, based on a swallowing-sounds database synchronized to a video fluorographic swallowing study compiled from 74 advanced-age patients with dysphagia, demonstrated an outstanding performance. It achieved an F1-micro average of approximately 0.92 and an accuracy of 95.20%. The method, proven effective in the current clinical recording database, suggests a significant advancement in the objectivity of cervical auscultation. However, validating its efficacy in other databases is crucial for confirming its broad applicability and potential impact.

show abstract

“…The only high potential application is a CNN for aspiration detection of VFSS videos, with an accuracy of AUC of 1.00 [ 25 ], but, as described above, VFSS is limited in its clinical use. Further studies investigated the possibility of identifying dysphagia by means of localization of the hyoid bone or hyoid bone movements by an AI tool: on the one hand, the detection of auscultations, swallowing sounds, and vibrations is used [ 26 , 27 , 28 , 29 ], and on the other hand, video material (VFSS or ultrasound) [ 30 , 31 , 32 ] is used. Both approaches yield good results.…”

Section: Introductionmentioning

confidence: 99%

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

Konradi

Zajber

Betz

et al. 2022

Sensors

View full text Add to dashboard Cite

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.

show abstract

Deep Learning-Based Swallowing Monitor for Realtime Detection of Swallow Duration

Cited by 11 publications

References 6 publications

Real‐time assessment of swallowing sound using an electronic stethoscope and an artificial intelligence system

Real‐time assessment of swallowing sound using an electronic stethoscope and an artificial intelligence system

Novel Approach Combining Shallow Learning and Ensemble Learning for the Automated Detection of Swallowing Sounds in a Clinical Database

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

Contact Info

Product

Resources

About