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

Contrastive multimodal deep learning for survival prediction in grade 2/3 gliomas.

JNCI cancer spectrum·2026

Same author

Multimodal contrastive learning for non-invasive chondroid bone tumor classification and grading using radiographs.

BMC medical imaging·2026

Same author

Predicting targeted therapy resistance in non-small cell lung cancer using multimodal machine learning.

Journal of thoracic disease·2025

Same author

Transformer-based representation learning for robust gene expression modeling and cancer prognosis.

Scientific reports·2025

Same author

Improving Colorectal Cancer Screening and Risk Assessment through Predictive Modeling on Medical Images and Records.

The American journal of pathology·2025

Same author

'Ion-freeze' efficiency in perovskite solar cells: time scales for ion immobilization.

EES solar·2025

Search research articles

Related Experiment Video

Updated: Feb 24, 2026

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

Deep Learning for Classification of Colorectal Polyps on Whole-slide Images.

Bruno Korbar^1,2, Andrea M Olofson³, Allen P Miraflor³

¹Department of Biomedical Data Science, Geisel School of Medicine at Dartmouth, One Medical Center Drive, Lebanon, NH 03756, USA.

Journal of Pathology Informatics

|August 23, 2017

Summary

An automated deep learning method accurately classifies colorectal polyps from whole-slide images, aiding pathologists in diagnosis and risk assessment. This AI tool improves accuracy and efficiency in identifying polyp types for better patient care.

Keywords:

Colorectal polyps deep learning digital pathology histopathological characterization

More Related Videos

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Related Experiment Videos

Last Updated: Feb 24, 2026

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

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

Published on: October 4, 2024

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Area of Science:

Computational pathology
Artificial intelligence in medicine
Digital pathology

Background:

Accurate histopathological characterization of colorectal polyps is crucial for cancer risk assessment and surveillance.
Current methods face significant inter- and intra-observer variability, impacting diagnostic consistency.
Automated analysis offers a potential solution to standardize polyp classification.

Purpose of the Study:

To develop and validate an automated image analysis method for classifying colorectal polyps.
To assist pathologists in accurate polyp characterization and diagnosis using whole-slide images.
To improve the efficiency and reliability of colorectal cancer risk stratification.

Main Methods:

Deep learning techniques, specifically residual network architecture, were employed for image analysis.
The method was trained on a dataset of 2074 annotated crop images covering five common polyp types.
Performance was evaluated on an independent test set of 239 whole-slide images using standard machine learning metrics.

Main Results:

The developed deep learning method achieved a high overall accuracy of 93.0% (95% CI: 89.0%-95.9%) in classifying colorectal polyps.
The system demonstrated strong performance in differentiating between hyperplastic, sessile serrated, traditional serrated, tubular, and tubulovillous/villous polyps.
The residual network architecture proved most effective for this classification task.

Conclusions:

The automated image analysis method significantly aids pathologists by reducing cognitive burden.
The tool enhances diagnostic efficacy in histopathological characterization of colorectal polyps.
Improved characterization supports more accurate risk assessment and follow-up recommendations for patients.