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A convolutional neural network for bleeding detection in capsule endoscopy using real clinical data.

Dorothee Turck¹, Thomas Dratsch², Lorenz Schröder¹

¹Department of Medicine, University of Cologne, Cologne, Germany.

Minimally Invasive Therapy & Allied Technologies : MITAT : Official Journal of the Society for Minimally Invasive Therapy

|August 28, 2023

Summary

A new convolutional neural network accurately detects gastrointestinal bleeding in capsule endoscopy videos. This AI tool shows promise for improving diagnostic accuracy and reducing reading time in clinical practice.

Keywords:

Machine learning bleeding detection capsule endoscopy convolutional neural networks small bowel

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Area of Science:

Medical Imaging
Artificial Intelligence
Gastroenterology

Background:

Capsule endoscopy is a key tool for visualizing the gastrointestinal tract.
Interpreting capsule endoscopy videos is time-consuming and requires expert analysis.
Developing automated methods can aid in pathology detection.

Purpose of the Study:

To develop and validate a convolutional neural network (CNN) for detecting gastrointestinal bleeding.
To utilize realistic clinical data for training and testing the AI model.
To assess the performance of the CNN in a single-center setting.

Main Methods:

A convolutional neural network (Inception V3) was developed using transfer learning.
The model was trained and validated on capsule endoscopy videos from 133 patients.
A dataset comprised 125 pathological bleeding findings and 103 non-pathological findings.

Main Results:

The CNN achieved an overall accuracy of 90.6% for bleeding detection.
Sensitivity for detecting bleedings was 89.4%, with a specificity of 91.7%.
The model demonstrated high performance on realistic clinical data.

Conclusions:

Convolutional neural networks can effectively detect gastrointestinal bleedings in capsule endoscopy videos.
The developed AI model shows potential for improving diagnostic accuracy.
This technology may significantly reduce the time required for capsule endoscopy video interpretation.