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Swin-PSAxialNet: An Efficient Multi-Organ Segmentation Technique

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Published on: July 5, 2024

AI support for colonoscopy quality control using CNN and transformer architectures.

Jian Chen¹, Ganhong Wang², Jingjie Zhou¹

¹Department of Gastroenterology, Changshu Hospital Affiliated to Soochow University, Suzhou, 215500, China.

BMC Gastroenterology

|August 9, 2024

Summary

This study developed an AI system for colonoscopy quality control. The EfficientNetB2 model achieved high accuracy, improving patient care and procedural efficiency.

Keywords:

Artificial intelligence Colonoscopy Colonoscopy quality control Deep learning

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Area of Science:

Medical Imaging
Artificial Intelligence
Gastroenterology

Background:

Developing deep learning models for colonoscopy quality control is crucial.
Exploring model decision-making mechanisms enhances trust and interpretability.

Purpose of the Study:

To construct and evaluate deep learning models for colonoscopy quality control.
To investigate the decision-making processes of these AI models.

Main Methods:

Utilized 4,189 colonoscopy images for transfer learning and fine-tuning of eight pre-trained CNN and Transformer models.
Evaluated model performance using AUC, Precision, and F1 score; employed Grad-CAM and SHAP for interpretability.
Deployed the best model (EfficientNetB2) in ONNX format for real-time monitoring.

Main Results:

EfficientNetB2 achieved superior performance with 0.992 accuracy on the validation set and 0.996 AUC on the test set.
Interpretability analysis identified key image regions used by the model for decision-making.
The deployed model achieved over 60 frames per second inference speed.

Conclusions:

An AI-assisted quality system using EfficientNetB2 integrates four key colonoscopy quality indicators.
This system offers comprehensive management and enhancement of quality indicators for clinical applications.