Meng

et al., 2022

[27]

China

1160 cases

WLI images and NBI images

YOLOv5 deep learning algorithm

After reference to CAD system, non-experts show significantly reduced differences in accuracy (88.2% vs. 93.2%, p < 0.001), sensitivity (87.6% vs. 92.3%, p = 0.013), and specificity (89.5% vs. 94.7%, p = 0.124) compared to experts.

The CAD system in WLI and NBI combination mode can improve the diagnostic performance of superficial ESCC.

Zhao

et al., 2022

[28]

China

300 cases

NBI images

Google net model with Inception v3 image classification system

There is no statistically significant difference between AI-NBI diagnosis and doctor diagnosis in terms of sensitivity (90.0% vs. 92.0%), specificity (92.0% vs. 94.0%), and accuracy (91.0% vs. 93.0%) (P > 0.05).

AI-NBI can assist in the diagnosis of early esophageal cancer.

Chronic Atrophic Gastritis and Gastric Cancer

Zhang

et al., 2020

[30]

China

5470 images of the gastric antrum from 1699 patients

Conversion of white light endoscopic images to uncompressed BMP format

Trained CNN-CAG model using DenseNet 121 network architecture

The accuracy of the CNN system in diagnosing mild, moderate, and severe atrophic gastritis was 0.93, 0.95, and 0.99, respectively, indicating a higher detection rate for moderate and severe atrophic gastritis than for mild cases.

DenseNet network showed high performance in CAG identification.