Abstract
Background and study aims Artificial intelligence (AI)-assisted image classification has been shown to have
high accuracy on endoscopic diagnosis. We evaluated the potential effects of use of
an AI-assisted image classifier on training of junior endoscopists for histological
prediction of gastric lesions.
Methods An AI image classifier was built on a convolutional neural network with five convolutional
layers and three fully connected layers A Resnet backbone was trained by 2,000 non-magnified
endoscopic gastric images. The independent validation set consisted of another 1,000
endoscopic images from 100 gastric lesions. The first part of the validation set was
reviewed by six junior endoscopists and the prediction of AI was then disclosed to
three of them (Group A) while the remaining three (Group B) were not provided this
information. All endoscopists reviewed the second part of the validation set independently.
Results The overall accuracy of AI was 91.0 % (95 % CI: 89.2–92.7 %) with 97.1 % sensitivity
(95 % CI: 95.6–98.7%), 85.9 % specificity (95 % CI: 83.0–88.4 %) and 0.91 area under
the ROC (AUROC) (95 % CI: 0.89–0.93). AI was superior to all junior endoscopists in
accuracy and AUROC in both validation sets. The performance of Group A endoscopists
but not Group B endoscopists improved on the second validation set (accuracy 69.3 %
to 74.7 %; P = 0.003).
Conclusion The trained AI image classifier can accurately predict presence of neoplastic component
of gastric lesions. Feedback from the AI image classifier can also hasten the learning
curve of junior endoscopists in predicting histology of gastric lesions.
