Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Reliability and Validity01:29

Reliability and Validity

14.0K
Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.
14.0K
Simplified Synchronous Machine Model01:30

Simplified Synchronous Machine Model

788
The Synchronous Machine Model is a fundamental tool in analyzing and ensuring the transient stability of power systems. This model simplifies the representation of a synchronous machine under balanced three-phase positive-sequence conditions, assuming constant excitation and ignoring losses and saturation. The model is pivotal for understanding the behavior of synchronous generators connected to a power grid, particularly during transient events.
In this model, each generator is connected to a...
788
Wind Turbine Machine Models01:24

Wind Turbine Machine Models

609
In the growing field of wind energy, incorporating wind turbine models into transient stability analysis is essential. Induction and synchronous machines are the primary models used, with induction machines being prevalent due to their simplicity and reliability.
Induction machines interact through the rotating magnetic field generated by the stator and the rotor. The key parameter is slip, which is the difference between synchronous speed and rotor speed relative to synchronous speed. Slip is...
609
In Vitro Drug Release Testing: Overview, Development and Validation01:10

In Vitro Drug Release Testing: Overview, Development and Validation

350
In vitro dissolution and drug release tests assess how quickly and how much of a drug is released from its dosage form into an aqueous medium under standardized laboratory conditions. These tests are essential tools in pharmaceutical development and quality assurance, offering insight into the drug's performance before clinical use.During formulation development, dissolution testing identifies incomplete or inconsistent drug release issues. It also supports decisions on selecting the optimal...
350
Machines01:19

Machines

579
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
579
Predicting Molecular Geometry02:27

Predicting Molecular Geometry

46.0K
VSEPR Theory for Determination of Electron Pair Geometries
46.0K

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Heterogeneity in endpoints, monitoring methods and blanking periods in clinical trials of atrial fibrillation ablation: a systematic review and meta-analysis.

Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing·2026
Same author

Late Fracture of Stylet-driven Lead Intended for Conduction System Pacing 2 Years After Implant.

The Journal of innovations in cardiac rhythm management·2026
Same author

When "Pneumonia" Does Not Resolve: A Case of Pulmonary Embolism Leading to Cavitary Pulmonary Infarction.

Cureus·2026
Same author

Evaluation of the METRA + intervention on mental health and social functioning in Afghan refugee adolescents in Pakistan: a pilot study.

European journal of psychotraumatology·2026
Same author

Communication Experiences of Neurological Voice Disorder Patients With Automatic Voice Assistants and Human Listeners.

Journal of voice : official journal of the Voice Foundation·2026
Same author

Patient Perceived Experience of Automated Voice Assistants in Oral Cavity Cancer.

Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery·2026
Same journal

Remote Assessment of Parkinson Disease Using Deep Learning on Structured Mouse-Trace Data From Suspected Cases: Machine-Learning Pilot Feasibility Study.

JMIR formative research·2026
Same journal

Perspectives on Continuous Glucose Monitoring Among Adults with Type 2 Diabetes in the United Kingdom: Cross-Sectional Survey.

JMIR formative research·2026
Same journal

Real-World Engagement With a Generative AI Conversational Agent for Mental Health Support: Retrospective Descriptive Study.

JMIR formative research·2026
Same journal

Improving Models to Predict Care Utilization Using Machine Learning: Retrospective Observational Study.

JMIR formative research·2026
Same journal

Implementing a Commercial AI Fracture Detection Tool in Health Care Using the Non-Adoption, Abandonment, Scale-Up, Spread, and Sustainability Framework: A Formative Evaluation Study.

JMIR formative research·2026
Same journal

An Evaluation of the Usability and Feasibility of the 50K4Life Mobile App for Delivering Walking Challenges to Public School Administrative Employees: Beta Testing Study.

JMIR formative research·2026
See all related articles
  1. Home
  3. Prediction Of Aspiration Risk By Using Vocal Biomarkers: Machine Learning Development And Validation Study.
  1. Home
  3. Prediction Of Aspiration Risk By Using Vocal Biomarkers: Machine Learning Development And Validation Study.

Related Experiment Video

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

1.0K

Prediction of Aspiration Risk by Using Vocal Biomarkers: Machine Learning Development and Validation Study.

Cyril Varghese1, Jianwei Zhang2, Sara Charney3

  • 1Division of Pulmonary and Department of Critical Care Medicine, Mayo Clinic in Arizona, Phoenix, AZ, United States.

JMIR Formative Research
|February 4, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Machine learning accurately predicts aspiration risk by analyzing vowel phonations. This novel algorithm offers a non-invasive tool to assess swallowing safety, comparable to expert clinicians.

Keywords:
ARDSILDacute respiratory distress syndromeartificial intelligenceaspirationinterstitial lung diseaselung transplantspeechvoice

More Related Videos

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.6K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

744

Related Experiment Videos

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

1.0K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.6K
Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

744

Area of Science:

  • Otolaryngology
  • Speech Science
  • Artificial Intelligence

Background:

  • Aspiration poses risks for respiratory diseases, but current diagnostic methods are invasive or unreliable.
  • Subjective bedside evaluations lack consistency, while tests like VFSS and FEES are resource-intensive.

Purpose of the Study:

  • To develop and validate a machine learning (ML) algorithm for predicting aspiration risk.
  • The algorithm analyzes acoustic features of simple vowel phonations.

Main Methods:

  • Retrospective analysis of [i] vowel phonations from 163 patients, recording acoustic features.
  • Supervised ML model trained to differentiate high-risk vs. low-risk aspirators, using VFSS for ground truth.
  • Model validated on an external cohort and compared to Speech Language Pathologists (SLPs).

Main Results:

  • ML model showed significant differences in risk scores between high-risk (0.530) and low-risk (0.243) aspiration groups (p<0.001).
  • Achieved an Area Under the Curve (AUC) of 0.76 in the development cohort and 0.70 in the external cohort.
  • ML model performance was comparable to trained SLPs in classifying aspiration risk.

Conclusions:

  • Quantifiable voice characteristics in Otolaryngology (ENT) patients correlate with aspiration risk.
  • An ML model analyzing sustained phonation can effectively detect differences between high- and low-risk aspirators.
  • This approach offers a promising, non-invasive method for aspiration risk assessment.