Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy

Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy

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...

You might also read

Related Articles

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

Sort by

Same author

Inter- and Intraobserver Variability in Bowel Preparation Scoring for Colon Capsule Endoscopy: Impact of AI-Assisted Assessment Feasibility Study.

Cancers·2025

Same author

AI-Assisted Evaluation of Colon Cleanliness in Capsule Endoscopy Videos.

Diagnostics (Basel, Switzerland)·2025

Same author

The EUROCROPSML time series benchmark dataset for few-shot crop type classification in Europe.

Scientific data·2025

Same author

Study of capsule endoscopy delivery at scale through enhanced artificial intelligence-enabled analysis (the CESCAIL study).

Colorectal disease : the official journal of the Association of Coloproctology of Great Britain and Ireland·2023

Same author

Clinicians' Guide to Artificial Intelligence in Colon Capsule Endoscopy-Technology Made Simple.

Diagnostics (Basel, Switzerland)·2023

Same author

Artificial intelligence for the detection of polyps or cancer with colon capsule endoscopy.

Therapeutic advances in gastrointestinal endoscopy·2021

Same journal

Correction: Luca et al. Global and Regional Diagnostic Results of Progress Toward Cervical Cancer Elimination, According to the WHO Strategy: A Systematic Literature Review with Narrative Synthesis. <i>Diagnostics</i> 2026, <i>16</i>, 1224.

Diagnostics (Basel, Switzerland)·2026

Same journal

Association Between Systemic Inflammatory Response Biomarkers and Disease Activity in Systemic Lupus Erythematosus: A Multi-Center Retrospective Study.

Diagnostics (Basel, Switzerland)·2026

Same journal

Vertebrogenic Low Back Pain and Basivertebral Nerve Ablation: A Review of Mechanisms, Imaging-Driven Selection, and Clinical Outcomes.

Diagnostics (Basel, Switzerland)·2026

Same journal

Multivalvular Carcinoid Heart Disease: The Role of Echocardiography in Diagnosis and Selection for Heterotopic Bicaval Valve Implantation.

Diagnostics (Basel, Switzerland)·2026

Same journal

Data-Efficient and Explainable Multimodal Survival Prediction in NSCLC Using Deep Image Embeddings, Clinical Variables, and Gradient-Boosted Trees.

Diagnostics (Basel, Switzerland)·2026

Same journal

Anomalous Left Coronary Artery from the Pulmonary Artery: Cinematic Volume Rendering Technique for Enhanced Anatomic Visualization.

Diagnostics (Basel, Switzerland)·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Oct 2, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Sequential Models for Endoluminal Image Classification.

Joana Reuss^1,2, Guillem Pascual¹, Hagen Wenzek³

¹Department of Mathematics and Computer Science, Universitat de Barcelona, 08007 Barcelona, Spain.

Diagnostics (Basel, Switzerland)

|February 25, 2022

Summary

This summary is machine-generated.

This study introduces a bidirectional Long Short-Term Memory Network (BLSTM) for polyp detection in Wireless Capsule Endoscopy (WCE) videos. The BLSTM model significantly improves detection performance by considering the temporal nature of WCE data.

Keywords:

endoluminal image classification neural networks polyp detection sequential models wireless capsule endoscopy (WCE)

More Related Videos

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

Related Experiment Videos

Last Updated: Oct 2, 2025

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Automated Midline Shift and Intracranial Pressure Estimation based on Brain CT Images

Published on: April 13, 2013

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Three-Dimensional Phase Resolved Functional Lung Magnetic Resonance Imaging

Published on: June 21, 2024

Area of Science:

Medical Imaging
Machine Learning in Healthcare

Background:

Wireless Capsule Endoscopy (WCE) aids in diagnosing digestive system issues.
Machine learning for WCE faces challenges with scarce, unbalanced datasets.

Purpose of the Study:

To improve polyp detection in WCE videos using machine learning.
To leverage sequential models for enhanced analysis of temporal WCE data.

Main Methods:

Development of a bidirectional Long Short-Term Memory (BLSTM) network.
Extraction of spatial and temporal features from WCE video data.
Comparison of BLSTM performance against non-sequential architectures.

Main Results:

The BLSTM network achieved a high Area under the Curve (AUC) of 93.83%.
The proposed method demonstrated superior performance compared to previous models.
Spatial and temporal feature extraction enhanced detection accuracy.

Conclusions:

Sequential models, specifically BLSTM, effectively address data limitations in WCE analysis.
The developed method shows potential for reducing physician analysis time for WCE videos.
This approach offers a promising advancement in automated polyp detection for WCE.