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Comment
. 2023 Jan;26(1):116-122.
doi: 10.1007/s10120-022-01330-9. Epub 2022 Aug 30.

Cooperation between artificial intelligence and endoscopists for diagnosing invasion depth of early gastric cancer

Atsushi Goto #  1 , Naoto Kubota #  2 , Jun Nishikawa  2 , Ryo Ogawa  3 , Koichi Hamabe  3 , Shinichi Hashimoto  3 , Hiroyuki Ogihara  4 , Yoshihiko Hamamoto  5 , Hideo Yanai  6 , Osamu Miura  7 , Taro Takami  3
Affiliations
Comment

Cooperation between artificial intelligence and endoscopists for diagnosing invasion depth of early gastric cancer

Atsushi Goto et al. Gastric Cancer. 2023 Jan.

Abstract

Background and study aims: The diagnostic ability of endoscopists to determine invasion depth of early gastric cancer is not favorable. We designed an artificial intelligence (AI) classifier for differentiating intramucosal and submucosal gastric cancers and examined it to establish a diagnostic method based on cooperation between AI and endoscopists.

Patients and methods: We prepared 500 training images using cases of mainly depressed-type early gastric cancer from 250 intramucosal cancers and 250 submucosal cancers. We also prepared 200 test images each of 100 cancers from another institution. We designed an AI classifier to differentiate between intramucosal and submucosal cancers by deep learning. We examined the performance of the AI classifier and the majority vote of the endoscopists as high confidence and low confidence diagnostic probability, respectively, and cooperatively combined them to establish a diagnostic method providing high accuracy.

Results: Internal evaluation of the training images showed that accuracy, sensitivity, specificity, and F1 measure by the AI classifier were 77%, 76%, 78%, and 0.768, and those of the majority vote of the endoscopists were 72.6%, 53.6%, 91.6%, and 0.662, respectively. A diagnostic method based on cooperation between AI and the endoscopists showed that the respective values were 78.0%, 76.0%, 80.0%, and 0.776 for the test images. The value of F1 measure was especially higher than those by AI or the endoscopists alone.

Conclusions: Cooperation between AI and endoscopists improved the diagnostic ability to determine invasion depth of early gastric cancer.

Keywords: AI classifier; Early gastric cancer; Invasion depth.

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Conflict of interest statement

The authors declare that they have no conflict of interest.

Figures

Fig. 1
Fig. 1
Diagnostic method of determining invasion depth by cooperation between AI and the endoscopists. If the diagnosis by AI and the endoscopists was consistent, the diagnosis was considered final (indicated by the blue cells). If the diagnosis by AI and the endoscopists differed, the diagnosis with the higher confidence level was adopted (indicated by the yellow cells). Diagnoses by AI and the endoscopists that did not agree at the same confidence level are considered mismatches (indicated by the pink cells). M, intramucosal cancer; SM, submucosal invasion; AI, artificial intelligence; Mismatch 1, AI diagnosis is SM but that of the endoscopists is M; Mismatch 2, AI diagnosis is M but that of the endoscopists is SM
Fig. 2
Fig. 2
Cases misdiagnosed by AI. Lesser curvature of the gastric body, ⌀44 mm, well-differentiated adenocarcinoma
See this image and copyright information in PMC

Comment on

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