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

You might also read

Related Articles

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

Sort by
Same author

Innovations in Advanced Endoscopic Resection of Early Upper Gastrointestinal Cancer.

Journal of clinical medicine·2026
Same author

Dupilumab for Eosinophilic Esophagitis in Adolescents and Adults: Real-World Outcomes Across Different Dosing Regimens from the EoE CONNECT Registry.

The American journal of gastroenterology·2026
Same author

Letter: Peripheral Blood Eosinophils as Complementary Biomarkers for Diagnosis and Follow-Up in Eosinophilic Oesophagitis-Authors' Reply.

Alimentary pharmacology & therapeutics·2026
Same author

An inverse probability of treatment weighted comparison between vedolizumab, ustekinumab, and tofacitinib in anti-TNF-experienced ulcerative colitis patients.

Therapeutic advances in gastroenterology·2026
Same author

The Immune Architecture of Eosinophilic Esophagitis: Mechanisms, Therapeutic Targets, and Precision Management.

ImmunoTargets and therapy·2026
Same author

Metabolic Dysfunction-Associated Steatotic Liver Disease and Incretin Receptor Agonists: A Metabolic Approach to Halting Liver Disease Progression.

Medicina (Kaunas, Lithuania)·2026
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

Sensors (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: Oct 7, 2025

Simultaneous Laryngopharyngeal and Conventional Esophageal pH Monitoring
06:46

Simultaneous Laryngopharyngeal and Conventional Esophageal pH Monitoring

Published on: December 14, 2020

3.0K

Integrated Relaxation Pressure Classification and Probe Positioning Failure Detection in High-Resolution Esophageal

Zoltan Czako1, Teodora Surdea-Blaga2, Gheorghe Sebestyen1

  • 1Computer Science Department, Technical University of Cluj-Napoca, 400027 Cluj-Napoca, Romania.

Sensors (Basel, Switzerland)
|January 11, 2022
PubMed
Summary
This summary is machine-generated.

Machine learning models can now automatically analyze esophageal manometry images to detect catheter positioning errors and classify integrated relaxation pressure (IRP). This AI approach achieves over 90% accuracy, streamlining diagnosis of esophageal motility disorders.

Keywords:
convolutional neural networkhigh-resolution esophageal manometryintegrated relaxation pressuremachine learning

More Related Videos

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography
09:42

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography

Published on: January 24, 2025

757
Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression
11:26

Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression

Published on: December 10, 2014

12.5K

Related Experiment Videos

Last Updated: Oct 7, 2025

Simultaneous Laryngopharyngeal and Conventional Esophageal pH Monitoring
06:46

Simultaneous Laryngopharyngeal and Conventional Esophageal pH Monitoring

Published on: December 14, 2020

3.0K
Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography
09:42

Acquisition and Semi-Automated Analysis of Respiratory Muscle Surface Electromyography

Published on: January 24, 2025

757
Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression
11:26

Assessing Cerebral Autoregulation via Oscillatory Lower Body Negative Pressure and Projection Pursuit Regression

Published on: December 10, 2014

12.5K

Area of Science:

  • Gastroenterology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • High-resolution esophageal manometry is crucial for diagnosing esophageal motility disorders.
  • The Chicago algorithm, utilizing integrated relaxation pressure (IRP), is standard for diagnosis.
  • Current manometry procedures are time-consuming and require significant human interpretation.

Purpose of the Study:

  • To develop a machine learning (ML) solution for detecting probe positioning failures in esophageal manometry.
  • To create an automated classifier for determining normal versus elevated integrated relaxation pressure (IRP) from raw manometry images.
  • To reduce human intervention in the Chicago Classification process.

Main Methods:

  • Image preprocessing involved identifying the swallowing event as the region of interest.
  • Images were resized and rescaled for input into deep learning models.
  • The InceptionV3 deep learning model was employed for image classification and IRP assessment.

Main Results:

  • The ML models achieved over 90% accuracy in classifying catheter positioning.
  • The models also demonstrated high accuracy in determining the IRP class.
  • Successful detection of probe positioning failures and IRP classification from raw images was achieved.

Conclusions:

  • Machine learning, specifically deep learning with InceptionV3, shows high accuracy for automated esophageal manometry analysis.
  • This AI-driven approach can significantly reduce the time and human effort required for diagnosing esophageal motility disorders.
  • This study represents a significant step towards the full automation of the Chicago Classification system.