Background and Aims
Diagnosing the invasion depth of gastric cancer (GC) is necessary to determine the
optimal method of treatment. Although the efficacy of evaluating macroscopic features
and EUS has been reported, there is a need for more accurate and objective methods.
The primary aim of this study was to test the efficacy of novel artificial intelligence
(AI) systems in predicting the invasion depth of GC.
Methods
A total of 16,557 images from 1084 cases of GC for which endoscopic resection or surgery
was performed between January 2013 and June 2019 were extracted. Cases were randomly
assigned to training and test datasets at a ratio of 4:1. Through transfer learning
leveraging a convolutional neural network architecture, ResNet50, 3 independent AI
systems were developed. Each system was trained to predict the invasion depth of GC
using conventional white-light imaging (WLI), nonmagnifying narrow-band imaging (NBI),
and indigo-carmine dye contrast imaging (Indigo).
Results
The area under the curve of the WLI AI system was .9590. The lesion-based sensitivity,
specificity, accuracy, positive predictive value, and negative predictive value of
the WLI AI system were 84.4%, 99.4%, 94.5%, 98.5%, and 92.9%, respectively. The lesion-based
accuracies of the WLI, NBI, and Indigo AI systems were 94.5%, 94.3%, and 95.5%, respectively,
with no significant difference.
Conclusions
These new AI systems trained with multiple images from different angles and distances
could predict the invasion depth of GC with high accuracy. The lesion-based accuracy
of the WLI, NBI, and Indigo AI systems was not significantly different.
Graphical abstract
Graphical Abstract
Abbreviations:
AI (artificial intelligence), GC (gastric cancer), Indigo (indigo-carmine dye contrast imaging), NBI (narrow-band imaging), SM2 (cancer with submucosal invasion ≥500 μm), WLI (white-light imaging)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: June 24, 2020
Accepted:
June 15,
2020
Received:
April 29,
2020
Footnotes
DISCLOSURE: The following author disclosed financial relationships: T. Tada: CEO and shareholder for AI Medical Service Inc. All other authors disclosed no financial relationships.
Identification
Copyright
© 2020 by the American Society for Gastrointestinal Endoscopy
