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Automatic Classification of GI Organs in Wireless Capsule Endoscopy Using a No-Code Platform-Based Deep Learning

Joowon Chung¹, Dong Jun Oh², Junseok Park³

¹Department of Internal Medicine, Nowon Eulji Medical Center, Eulji University School of Medicine, Seoul 01830, Republic of Korea.

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|May 16, 2023

Summary

This summary is machine-generated.

A new AI model accurately classifies gastrointestinal organs from capsule endoscopy videos. This technology aids in identifying transitional areas, improving diagnostic efficiency for various GI conditions.

Keywords:

artificial intelligence automated machine learning automatic organ classification capsule endoscopy

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Area of Science:

Gastroenterology
Medical Imaging
Artificial Intelligence

Background:

Capsule endoscopy (CE) generates numerous images, complicating automatic organ classification.
Direct application of AI to CE videos is challenging due to image redundancy and irrelevance.

Purpose of the Study:

To develop a deep learning algorithm for classifying gastrointestinal (GI) organs from CE videos.
To propose a novel method for visualizing GI transitional areas using AI.
To enhance the diagnostic utility of CE by improving organ identification and boundary detection.

Main Methods:

A deep learning algorithm was developed using a no-code platform for GI organ classification (esophagus, stomach, small bowel, colon).
The model was trained on 37,307 images from 24 CE videos and tested on 39,781 images from 30 CE videos.
Validation involved 100 CE videos with diverse pathologies, and transitional areas were visualized by adjusting AI score cut-offs.

Main Results:

The AI model achieved high overall accuracy (0.98), precision (0.89), recall (0.97), and F1 score (0.92).
Average accuracies per organ were: esophagus (0.98), stomach (0.96), small bowel (0.87), and colon (0.87).
Adjusting the AI score cut-off improved performance metrics and enabled intuitive visualization of transitional areas.

Conclusions:

The developed AI model demonstrates high accuracy for GI organ classification in CE videos.
The proposed visualization method effectively locates transitional areas by adjusting AI score cut-offs.
This AI-driven approach enhances the interpretation of CE findings and aids in clinical decision-making.