Jove
Visualize
Contact Us

Related Concept Videos

Assessment of the Abdomen I: Inspection and Auscultation01:25

Assessment of the Abdomen I: Inspection and Auscultation

188
Introduction
The abdominal examination is a cornerstone of clinical medicine, serving as a critical tool in diagnosing various gastrointestinal (GI) diseases. It involves a systematic approach that includes inspection and auscultation, each with distinct yet complementary roles in assessing the abdomen. This article will delve into these two primary methods healthcare professionals use to examine the abdomen.
Inspection of the Abdomen
The first step in any abdominal examination is inspection....
188
Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy01:26

Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy

28
This lesson explores three gastrointestinal imaging techniques: radionuclide testing, colonic transit studies, and virtual colonoscopy.
Radionuclide Testing
Radionuclide testing is a sophisticated medical technique for assessing gastrointestinal motility. It focuses on gastric emptying and colonic transit time. Radioactive markers track the movement of food through the digestive system, providing insights into gastrointestinal disorders.
In gastric emptying studies, a meal's liquid and...
28

You might also read

Related Articles

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

Sort by
Same author

Optical-Resolution Photoacoustic Microscopy-Based Virtual Staining: A Wavelet-Enhanced Contrastive Translation Approach With Structure Preservation.

Journal of biophotonics·2026
Same author

Hybrid sequencing reveals incompleteness of the H37Rv reference genome and highlights lineage-specific genomic divergence in <i>Mycobacterium tuberculosis</i>.

Microbial genomics·2026
Same author

Guiding Fast Ion Beam by Suppressing Secondary Ions.

Physical review letters·2026
Same author

Influence of Multi-Cue Interaction on Human Depth Perception in Three-Dimensional Space.

Sensors (Basel, Switzerland)·2026
Same author

Community-based "X-ray+Xpert® MTB/RIF ultra pooling test" case-finding strategy among high-risk groups in rural areas: a prospective application study.

Emerging microbes & infections·2026
Same author

The latent tuberculosis infection survey using two interferon γ release assay tests among the elderly in a well-confined rural county in Eastern China.

BMC geriatrics·2025
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 Experiment Video

Updated: May 7, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.3K

Enhancing bowel sound recognition with self-attention and self-supervised pre-training.

Yansuo Yu1, Mingwu Zhang1, Zhennian Xie2

  • 1Academy of Artificial Intelligence, Beijing Institute of Petrochemical Technology, Beijing, China.

Plos One
|December 31, 2024
PubMed
Summary

This study introduces a deep learning method using Branchformer and self-supervised learning to automatically recognize bowel sounds. This automated approach improves accuracy, especially with limited data, aiding gastrointestinal disorder diagnosis.

More Related Videos

Targeted Training of Ultrasonic Vocalizations in Aged and Parkinsonian Rats
11:00

Targeted Training of Ultrasonic Vocalizations in Aged and Parkinsonian Rats

Published on: August 8, 2011

19.7K
Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
09:09

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

370

Related Experiment Videos

Last Updated: May 7, 2025

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception
05:48

Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

Published on: August 9, 2024

1.3K
Targeted Training of Ultrasonic Vocalizations in Aged and Parkinsonian Rats
11:00

Targeted Training of Ultrasonic Vocalizations in Aged and Parkinsonian Rats

Published on: August 8, 2011

19.7K
Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody
09:09

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

370

Area of Science:

  • Medical signal processing
  • Artificial intelligence in healthcare
  • Gastroenterology

Background:

  • Bowel sounds are crucial indicators of gastrointestinal (GI) tract function and health.
  • Current manual auscultation for bowel sound analysis is subjective, time-consuming, and labor-intensive.
  • There is a need for objective, automated, and non-invasive methods for GI condition assessment.

Purpose of the Study:

  • To develop and validate a novel deep learning-based method for automated bowel sound recognition.
  • To enhance the accuracy and efficiency of bowel sound analysis for clinical applications.
  • To address the limitations of manual auscultation in diagnosing and monitoring GI disorders.

Main Methods:

  • Implementation of the Branchformer architecture, utilizing self-attention and convolutional gating for feature extraction.
  • Application of a self-supervised pre-training strategy on extensive unlabeled audio data for general sound representation learning.
  • Fine-tuning the model on a specific dataset of bowel sounds for task-oriented recognition.
  • Parallel processing of self-attention and convolutional gated Multi-layer Perceptron branches to capture audio signal dependencies.

Main Results:

  • The proposed deep learning method demonstrated superior recognition performance compared to existing baseline models.
  • The self-supervised pre-training strategy proved effective, particularly in data-limited scenarios.
  • The Branchformer architecture successfully captured complex patterns in bowel sound audio signals.
  • Experimental validation on public datasets confirmed the model's effectiveness.

Conclusions:

  • The developed deep learning approach offers an efficient and automated solution for clinical bowel sound monitoring.
  • This method facilitates earlier diagnosis and improved treatment strategies for gastrointestinal disorders.
  • The integration of Branchformer and self-supervised learning represents a significant advancement in medical audio signal analysis.