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

Redefining text-to-SQL metrics by incorporating semantic and structural similarity.

Scientific reports·2025

Same author

Deep learning-based object detection algorithms in medical imaging: Systematic review.

Heliyon·2025

Same author

Comparing SMILES and SELFIES tokenization for enhanced chemical language modeling.

Scientific reports·2024

Same author

Comparing Stacking Ensemble Techniques to Improve Musculoskeletal Fracture Image Classification.

Journal of imaging·2024

Same author

Multiparametric MRI dataset for susceptibility-based radiomic feature extraction and analysis.

Scientific data·2024

Same author

Assessing robustness of quantitative susceptibility-based MRI radiomic features in patients with multiple sclerosis.

Scientific reports·2023

Search research articles

Related Experiment Video

Updated: Aug 24, 2025

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

A stacking-based artificial intelligence framework for an effective detection and localization of colon polyps.

Carina Albuquerque¹, Roberto Henriques², Mauro Castelli²

¹NOVA Information Management School (NOVA IMS), Universidade Nova de Lisboa, Campus de Campolide, 1070-312, Lisbon, Portugal. calbuquerque@novaims.unl.pt.

Scientific Reports

|October 21, 2022

Summary

This study introduces an AI-driven approach for automated polyp detection during colonoscopies, significantly improving accuracy. The method enhances polyp localization, aiding oncologists and reducing missed detections for better colorectal cancer prevention.

More Related Videos

Structured Approach to Colonoscopy Technique Optimization: A Single-Center Experience with Novice Endoscopists

Structured Approach to Colonoscopy Technique Optimization: A Single-Center Experience with Novice Endoscopists

Published on: July 11, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Related Experiment Videos

Last Updated: Aug 24, 2025

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Detection and Isolation of Cancer in Prostate Biopsies Using Stimulated Raman Histology and Artificial Intelligence

Published on: June 10, 2025

Structured Approach to Colonoscopy Technique Optimization: A Single-Center Experience with Novice Endoscopists

Structured Approach to Colonoscopy Technique Optimization: A Single-Center Experience with Novice Endoscopists

Published on: July 11, 2025

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System

Published on: July 11, 2025

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Colorectal cancer (CRC) prevention relies heavily on colonoscopy for polyp detection.
Missed polyps during standard colonoscopies can lead to delayed CRC diagnosis.
Artificial intelligence (AI) offers potential for automating and enhancing polyp detection.

Purpose of the Study:

To implement and evaluate various object detection algorithms for automated polyp detection in colonoscopy images.
To improve the mean average precision (mAP) of polyp detection using a stacking ensemble approach.
To assess the potential of AI-driven polyp detection in reducing oncologists' workload and increasing detection precision.

Main Methods:

Implementation of multiple baseline object detection algorithms for polyp identification.
Application of a stacking ensemble technique to combine baseline models for improved performance.
Experimental validation of the proposed methodology on colonoscopy datasets.

Main Results:

The proposed stacking ensemble method achieved a mean average precision (mAP) of 0.86.
This represents a 34.9% improvement in mAP compared to the best individual baseline model.
The approach demonstrated a 28.8% enhancement over the weighted boxes fusion ensemble technique.

Conclusions:

The developed AI methodology significantly enhances polyp detection accuracy and localization precision during colonoscopies.
This automated approach shows considerable promise for reducing the burden on oncologists and improving early colorectal cancer detection.
The stacking ensemble technique offers a robust strategy for advancing AI in gastrointestinal diagnostics.