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Endoscopic Procedures I: Esophagogastroduodenoscopy

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Author Spotlight: Generation of and Comparison Between Patient-Derived Gastric Organoids from Different Regions of the Stomach

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Deep learning-based anatomical site classification for upper gastrointestinal endoscopy.

Qi He¹, Sophia Bano², Omer F Ahmad²

¹Key Laboratory of Mechanism Theory and Equipment Design of Ministry of Education, Tianjin University, Tianjin, China.

International Journal of Computer Assisted Radiology and Surgery

|May 8, 2020

Summary

This study introduces a deep learning framework for automated upper gastrointestinal endoscopic reporting using standardized oesophagogastroduodenoscopy (EGD) images. The method shows feasibility and improved performance in classifying EGD images, aiding quality control.

Keywords:

Artificial intelligence Deep learning Endoscopy Gastroenterology

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Area of Science:

Medical Imaging
Artificial Intelligence
Gastroenterology

Background:

Upper gastrointestinal (GI) endoscopic image documentation is crucial for quality control but faces challenges due to similar site appearances and patient variations.
Computer-assisted techniques struggle with the variability in endoscopic images.

Purpose of the Study:

To propose a standardized set of oesophagogastroduodenoscopy (EGD) images for routine capture.
To evaluate the efficiency of these images for deep learning-based classification methods.
To develop an automated endoscopic reporting system.

Main Methods:

A novel EGD image dataset was created, standardizing upper GI endoscopy following guideline landmarks.
Images were annotated by an expert clinician.
Several deep learning classification models were trained using the annotated dataset.

Main Results:

The study analyzed 3704 EGD images from 211 patients.
A close agreement was found between predicted labels and expert-labeled ground truth.
Limitations of current static image classification for EGD were identified.

Conclusions:

A framework for automated EGD report generation using deep learning was presented.
The proposed method is feasible for EGD image classification.
The approach demonstrated potential for improved performance and qualitative results.