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10.08.2020 | Original Article―Alimentary Tract | Ausgabe 11/2020

Journal of Gastroenterology 11/2020

Real-time assessment of video images for esophageal squamous cell carcinoma invasion depth using artificial intelligence

Journal of Gastroenterology > Ausgabe 11/2020
Yusaku Shimamoto, Ryu Ishihara, Yusuke Kato, Ayaka Shoji, Takahiro Inoue, Katsunori Matsueda, Muneaki Miyake, Kotaro Waki, Mitsuhiro Kono, Hiromu Fukuda, Noriko Matsuura, Koji Nagaike, Kenji Aoi, Katsumi Yamamoto, Takuya Inoue, Masanori Nakahara, Akihiro Nishihara, Tomohiro Tada
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Although optimal treatment of superficial esophageal squamous cell carcinoma (SCC) requires accurate evaluation of cancer invasion depth, the current process is rather subjective and may vary by observer. We, therefore, aimed to develop an AI system to calculate cancer invasion depth.


We gathered and selected 23,977 images (6857 WLI and 17,120 NBI/BLI images) of pathologically proven superficial esophageal SCC from endoscopic videos and still images of superficial esophageal SCC taken in our facility, to use as a learning dataset. We annotated the images with information [such as magnified endoscopy (ME) or non-ME, pEP-LPM, pMM, pSM1, and pSM2-3 cancers] based on pathologic diagnosis of the resected specimens. We created a model using a convolutional neural network. Performance of the AI system was compared with that of invited experts who used the same validation video set, independent of the learning dataset.


Accuracy, sensitivity, and specificity with non-magnified endoscopy (ME) were 87%, 50%, and 99% for the AI system and 85%, 45%, 97% for the experts. Accuracy, sensitivity, and specificity with ME were 89%, 71%, and 95% for the AI system and 84%, 42%, 97% for the experts.


Most diagnostic parameters were higher when done by the AI system than by the experts. These results suggest that our AI system could potentially provide useful support during endoscopies.

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