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DOI: 10.1055/a-1660-6500
Artificial intelligence versus expert endoscopists for diagnosis of gastric cancer in patients who have undergone upper gastrointestinal endoscopy
Supported by: P-CREATE by AMED 21448169Trial Registration: ClinicalTrials.gov Registration number (trial ID): NCT04040374 Type of study: Retrospective
Abstract
Aims To compare endoscopy gastric cancer images diagnosis rate between artificial intelligence (AI) and expert endoscopists.
Patients and methods We used the retrospective data of 500 patients, including 100 with gastric cancer, matched 1:1 to diagnosis by AI or expert endoscopists. We retrospectively evaluated the noninferiority (prespecified margin 5 %) of the per-patient rate of gastric cancer diagnosis by AI and compared the per-image rate of gastric cancer diagnosis.
Results Gastric cancer was diagnosed in 49 of 49 patients (100 %) in the AI group and 48 of 51 patients (94.12 %) in the expert endoscopist group (difference 5.88, 95 % confidence interval: −0.58 to 12.3). The per-image rate of gastric cancer diagnosis was higher in the AI group (99.87 %, 747 /748 images) than in the expert endoscopist group (88.17 %, 693 /786 images) (difference 11.7 %).
Conclusions Noninferiority of the rate of gastric cancer diagnosis by AI was demonstrated but superiority was not demonstrated.
Publication History
Received: 30 August 2020
Accepted after revision: 12 October 2021
Accepted Manuscript online:
04 October 2021
Article published online:
04 May 2022
© 2021. The Author(s). This is an open access article published by Thieme under the terms of the Creative Commons Attribution-NonDerivative-NonCommercial License, permitting copying and reproduction so long as the original work is given appropriate credit. Contents may not be used for commercial purposes, or adapted, remixed, transformed or built upon. (https://creativecommons.org/licenses/by-nc-nd/4.0/)
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References
- 1 Hirasawa T, Aoyama K, Tanimoto T. et al. Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images. Gastric Cancer 2018; 21: 653-660
- 2 Ogawa R, Nishikawa J, Hideura E. et al. Objective assessment of the utility of chromoendoscopy with a support vector machine. J Gastrointest Cancer 2019; 50: 386-391
- 3 Ali H, Yasmin M, Sharif M. et al. Computer-assisted gastric abnormalities detection using hybrid texture descriptors for chromoendoscopy images. Comput Methods Programs Biomed 2018; 157: 39-47
- 4 Sakai Y, Takemoto S, Hori K. et al. Automatic detection of early gastric cancer in endoscopic images using a transferring convolutional neural network. Conf Proc IEEE Eng Med Biol Soc 2018; 2018: 4138-4141
- 5 Kanesaka T, Lee TC, Uedo N. et al. Computer-aided diagnosis for identifying and delineating early gastric cancers in magnifying narrow-band imaging. Gastrointest Endosc 2018; 87: 1339-1344
- 6 Wu L, Zhou W, Wan X. et al. A deep neural network improves endoscopic detection of early gastric cancer without blind spots. Endoscopy 2019; 51: 522-531
- 7 Lee JH, Kim YJ, Kim YW. et al. Spotting malignancies from gastric endoscopic images using deep learning. Surg Endosc 2019; 33: 3790-3797
- 8 Riaz F, Silva FB, Ribeiro MD. et al. Invariant Gabor texture descriptors for classification of gastroenterology images. IEEE Trans Biomed Eng 2012; 59: 2893-2904
- 9 Liu DY, Gan T, Rao NN. et al. Identification of lesion images from gastrointestinal endoscope based on feature extraction of combinational methods with and without learning process. Med Image Anal 2016; 32: 281-294
- 10 Kubota K, Kuroda J, Yoshida M. et al. Medical image analysis: computer-aided diagnosis of gastric cancer invasion on endoscopic images. Surg Endosc 2012; 26: 1485-1489
- 11 Zhu Y, Wang QC, Xu MD. et al. Application of convolutional neural network in the diagnosis of the invasion depth of gastric cancer based on conventional endoscopy. Gastrointest Endosc 2019; 89: 806-815.e1
- 12 Watanabe K, Nagata N, Shimbo T. et al. Accuracy of endoscopic diagnosis of Helicobacter pylori infection according to level of endoscopic experience and the effect of training. BMC Gastroenterol 2013; 13: 128