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Diagnosing chronic atrophic gastritis by gastroscopy using artificial intelligence.

Yaqiong Zhang¹, Fengxia Li², Fuqiang Yuan³

¹Department of Gastroenterology, Shanxi Provincial People's Hospital of Shanxi Medical University, Taiyuan, China.

Digestive and Liver Disease : Official Journal of the Italian Society of Gastroenterology and the Italian Association for the Study of the Liver

|February 17, 2020

Summary

A new deep learning model significantly improves chronic atrophic gastritis diagnosis from endoscopic images. This AI tool offers higher accuracy than experts, potentially reducing physician workload and healthcare costs.

Keywords:

Artificial intelligence Chronic atrophic gastritis Convolutional neural network Stomach cancer

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Area of Science:

Medical Imaging
Artificial Intelligence in Medicine
Gastroenterology

Background:

Endoscopic diagnosis of chronic atrophic gastritis has limited sensitivity (42%).
Multipoint biopsy is more accurate but not always accessible.

Purpose of the Study:

To develop a convolutional neural network (CNN) for enhanced chronic atrophic gastritis diagnosis.
To improve diagnostic accuracy compared to traditional methods.

Main Methods:

Trained a CNN model using 5470 gastric antrum images from 1699 patients.
Validated the model's performance using five-fold cross-validation.
Compared CNN diagnoses against three expert endoscopists.

Main Results:

The CNN model achieved high diagnostic accuracy (0.942), sensitivity (0.945), and specificity (0.940).
Performance surpassed that of human experts.
Detection rates for mild, moderate, and severe gastritis were 93%, 95%, and 99% respectively.

Conclusions:

A CNN model can accurately diagnose chronic atrophic gastritis from gastroscopic images.
This AI approach can alleviate endoscopist burden, streamline diagnostics, and lower costs.