scholarly journals Characterizing Swallows From People With Neurodegenerative Diseases Using High-Resolution Cervical Auscultation Signals and Temporal and Spatial Swallow Kinematic Measurements

2021 ◽  
pp. 1-16
Author(s):  
Cara Donohue ◽  
Yassin Khalifa ◽  
Shitong Mao ◽  
Subashan Perera ◽  
Ervin Sejdić ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Neurodegenerative Diseases ◽  
Learning Algorithms ◽  
Healthy Adults ◽  
Machine Learning Algorithms ◽  
Cervical Auscultation ◽  
Laryngeal Vestibule ◽  
Temporal And Spatial ◽  
Kinematic Measures

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.

scholarly journals Skin Characterizations by Using Contact Capacitive Imaging and High-Resolution Ultrasound Imaging with Machine Learning Algorithms

2021 ◽  
Vol 11 (18) ◽  
pp. 8714
Author(s):  
Elena Chirikhina ◽  
Andrey Chirikhin ◽  
Sabina Dewsbury-Ennis ◽  
Francesco Bianconi ◽  
Perry Xiao
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Stratum Corneum ◽  
Image Classification ◽  
Water Content ◽  
Ultrasound Imaging ◽  
Skin Diseases ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Body Parts

We present our latest research on skin characterizations by using Contact Capacitive Imaging and High-Resolution Ultrasound Imaging with Machine Learning algorithms. Contact Capacitive Imaging is a novel imaging technology based on the dielectric constant measurement principle, with which we have studied the skin water content of different skin sites and performed image classification by using pre-trained Deep Learning Neural Networks through Transfer Learning. The results show lips and nose have the lowest water content, whilst cheek, eye corner and under-eye have the highest water content. The classification yields up to 83.8% accuracy. High-Resolution Ultrasound Imaging is a state-of-the-art ultrasound technology, and can produce high-resolution images of the skin and superficial soft tissue to a vertical resolution of about 40 microns, with which we have studied the thickness of different skin layers, such as stratum corneum, epidermis and dermis, around different locations on the face and around different body parts. The results show the chin has the highest stratum corneum thickness, and the arm has the lowest stratum corneum thickness. We have also developed two feature-based image classification methods which yield promising results. The outcomes of this study could provide valuable guidelines for cosmetic/medical research, and methods developed in this study can also be extended for studying damaged skin or skin diseases. The combination of Contact Capacitive Imaging and High-Resolution Ultrasound Imaging could be a powerful tool for skin studies.

Landslide detection by machine learning on high-resolution DEMs

2021 ◽  
Author(s):  
Mihai Niculita
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Binary Classification ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Single Event ◽  
High Resolution Data ◽  
Susceptibility Modeling ◽  
Study Case ◽  
High Level

&lt;p&gt;Machine learning algorithms are increasingly used in geosciences for the detection of susceptibility modeling of certain landforms or processes. The increased availability of high-resolution data and the increase of available machine learning algorithms opens up the possibility of creating datasets for the training of models for automatic detection of specific landforms. In this study, we tested the usage of LiDAR DEMs for creating a dataset of labeled images representing shallow single event landslides in order to use them for the detection of other events. The R stat implementation of the keras high-level neural networks API was used to build and test the proposed approach. A 5m LiDAR DEM was cut in 25 by 25 pixels tiles, and the tiles that overlayed shallow single event landslides were labeled accordingly, while the tiles that did not contain landslides were randomly selected to be labeled as non-landslides. The binary classification approach was tested with 255 grey levels elevation images and 255 grey levels shading images, the shading approach giving better results. The presented study case shows the possibility of using machine learning in the landslide detection on high-resolution DEMs.&lt;/p&gt;

Automatic Estimation of Laryngeal Vestibule Closure Duration Using High-Resolution Cervical Auscultation Signals

2020 ◽  
Vol 5 (6) ◽  
pp. 1647-1656
Author(s):  
Aliaa Sabry ◽  
Amanda S. Mahoney ◽  
Shitong Mao ◽  
Yassin Khalifa ◽  
Ervin Sejdić ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Resolution ◽  
Signal Analysis ◽  
Machine Learning Techniques ◽  
Cervical Auscultation ◽  
Noninvasive Methods ◽  
Data Set ◽  
Closure Duration ◽  
Learning Techniques ◽  
Laryngeal Vestibule

Purpose Safe swallowing requires adequate protection of the airway to prevent swallowed materials from entering the trachea or lungs (i.e., aspiration). Laryngeal vestibule closure (LVC) is the first line of defense against swallowed materials entering the airway. Absent LVC or mistimed/shortened closure duration can lead to aspiration, adverse medical consequences, and even death. LVC mechanisms can be judged commonly through the videofluoroscopic swallowing study; however, this type of instrumentation exposes patients to radiation and is not available or acceptable to all patients. There is growing interest in noninvasive methods to assess/monitor swallow physiology. In this study, we hypothesized that our noninvasive sensor-based system, which has been shown to accurately track hyoid displacement and upper esophageal sphincter opening duration during swallowing, could predict laryngeal vestibule status, including the onset of LVC and the onset of laryngeal vestibule reopening, in real time and estimate the closure duration with a comparable degree of accuracy as trained human raters. Method The sensor-based system used in this study is high-resolution cervical auscultation (HRCA). Advanced machine learning techniques enable HRCA signal analysis through feature extraction and complex algorithms. A deep learning model was developed with a data set of 588 swallows from 120 patients with suspected dysphagia and further tested on 45 swallows from 16 healthy participants. Results The new technique achieved an overall mean accuracy of 74.90% and 75.48% for the two data sets, respectively, in distinguishing LVC status. Closure duration ratios between automated and gold-standard human judgment of LVC duration were 1.13 for the patient data set and 0.93 for the healthy participant data set. Conclusions This study found that HRCA signal analysis using advanced machine learning techniques can effectively predict laryngeal vestibule status (closure or opening) and further estimate LVC duration. HRCA is potentially a noninvasive tool to estimate LVC duration for diagnostic and biofeedback purposes without X-ray imaging.

Sign in / Sign up

Export Citation Format

Share Document