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An Optimal Artificial Intelligence System for Real-Time Endoscopic Prediction of Invasion Depth in Early Gastric Cancer

 Jie-Hyun Kim  ;  Sang-Il Oh  ;  So-Young Han  ;  Ji-Soo Keum  ;  Kyung-Nam Kim  ;  Jae-Young Chun  ;  Young-Hoon Youn  ;  Hyojin Park 
 CANCERS, Vol.14(23) : 6000, 2022-12 
Journal Title
Issue Date
artificial intelligence ; convolutional neural networks ; endoscopy ; gastric cancer ; video
We previously constructed a VGG-16 based artificial intelligence (AI) model (image classifier [IC]) to predict the invasion depth in early gastric cancer (EGC) using endoscopic static images. However, images cannot capture the spatio-temporal information available during real-time endoscopy-the AI trained on static images could not estimate invasion depth accurately and reliably. Thus, we constructed a video classifier [VC] using videos for real-time depth prediction in EGC. We built a VC by attaching sequential layers to the last convolutional layer of IC v2, using video clips. We computed the standard deviation (SD) of output probabilities for a video clip and the sensitivities in the manner of frame units to observe consistency. The sensitivity, specificity, and accuracy of IC v2 for static images were 82.5%, 82.9%, and 82.7%, respectively. However, for video clips, the sensitivity, specificity, and accuracy of IC v2 were 33.6%, 85.5%, and 56.6%, respectively. The VC performed better analysis of the videos, with a sensitivity of 82.3%, a specificity of 85.8%, and an accuracy of 83.7%. Furthermore, the mean SD was lower for the VC than IC v2 (0.096 vs. 0.289). The AI model developed utilizing videos can predict invasion depth in EGC more precisely and consistently than image-trained models, and is more appropriate for real-world situations.
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Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Jie-Hyun(김지현) ORCID logo https://orcid.org/0000-0002-9198-3326
Park, Hyo Jin(박효진) ORCID logo https://orcid.org/0000-0003-4814-8330
Youn, Young Hoon(윤영훈) ORCID logo https://orcid.org/0000-0002-0071-229X
Chun, Jaeyoung(천재영) ORCID logo https://orcid.org/0000-0002-4212-0380
Han, So-Young(한소영)
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