Convolutional Neural Network for Differentiating Gastric Cancer from Gastritis Using Magnified Endoscopy with Narrow Band Imaging

Horiuchi, Yusuke; Aoyama, Kazuharu; Tokai, Yoshitaka; Hirasawa, Toshiaki; Yoshimizu, Shoichi; Ishiyama, Akiyoshi; Yoshio, Toshiyuki; Tsuchida, Takaaki; Fujisaki, Junko; Tada, Takeshi

doi:10.1007/s10620-019-05862-6

Cited by 119 publications

(89 citation statements)

References 21 publications

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“…[13][14][15][16][17][18][19] Recently, a few reports have assessed the usefulness of computer-aided diagnosis (CAD) systems for ME-NBI diagnosis of EGC using AI. 20,21 These results are almost equivalent to or slightly lower than the previously reported diagnostic accuracies of ME-NBI (90.4% 6 and 96.1% 7 ) performed by endoscopists in Japan. Moreover, although maximal magnification was stated as the method used in these two articles, the water immersion technique was not used.…”

Section: Introductionsupporting

confidence: 67%

Application of artificial intelligence using a convolutional neural network for diagnosis of early gastric cancer based on magnifying endoscopy with narrow‐band imaging

Ueyama

Kato²,

Akazawa

et al. 2020

J of Gastro and Hepatol

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105

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gathered ME-NBI images and patients' clinical information. Kato Y provided valuable advice regarding the technical information and managed the AI-assisted CNN-CAD system and analyzed the data in this manuscript. All authors gave final approval for publication. Ueyama H, Nagahara A, and Tada T take full responsibility for the work as a whole, including the study design, access to data, and the decision to submit and publish the manuscript.

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Section: Introductionsupporting

confidence: 67%

Application of artificial intelligence using a convolutional neural network for diagnosis of early gastric cancer based on magnifying endoscopy with narrow‐band imaging

Ueyama

Kato²,

Akazawa

et al. 2020

J of Gastro and Hepatol

Self Cite

105

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“…All 4245 studies were screened and 106 full-length articles and/or abstracts were assessed. Nineteen studies 2 5 6 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 reported on the detection and/ or classification of gastrointestinal neoplastic lesions by CNN. Among the 19 studies, five 6 12 15 17 26 reported on efficacy of CNN in diagnosing esophageal neoplasia, eight 5 14 16 18 19 22 23 25 reported on use of CNN in neoplasia of the stomach and six 2 11 13 20 21 24 evaluated use of CNN in diagnosing colorectal neoplasia.…”

Section: Resultsmentioning

confidence: 99%

“…Nineteen studies 2 5 6 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 reported on the detection and/ or classification of gastrointestinal neoplastic lesions by CNN. Among the 19 studies, five 6 12 15 17 26 reported on efficacy of CNN in diagnosing esophageal neoplasia, eight 5 14 16 18 19 22 23 25 reported on use of CNN in neoplasia of the stomach and six 2 11 13 20 21 24 evaluated use of CNN in diagnosing colorectal neoplasia. Seven studies 5 11 12 14 19 20 25 used standard WLE, eight used NBI (magnifying and/ or non-magnifying) 2 6 13 15 18 22 23 26 and four 16 17 21 24 used a combination of standard WLE and/or NBI and/or chromo-endoscopy images ( Table 1 ).…”

Section: Resultsmentioning

confidence: 99%

Accuracy of convolutional neural network-based artificial intelligence in diagnosis of gastrointestinal lesions based on endoscopic images: A systematic review and meta-analysis

et al. 2020

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Background and study aims Recently, a growing body of evidence has been amassed on evaluation of artificial intelligence (AI) known as deep learning in computer-aided diagnosis of gastrointestinal lesions by means of convolutional neural networks (CNN). We conducted this meta-analysis to study pooled rates of performance for CNN-based AI in diagnosis of gastrointestinal neoplasia from endoscopic images. Methods Multiple databases were searched (from inception to November 2019) and studies that reported on the performance of AI by means of CNN in the diagnosis of gastrointestinal tumors were selected. A random effects model was used and pooled accuracy, sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) were calculated. Pooled rates were categorized based on the gastrointestinal location of lesion (esophagus, stomach and colorectum). Results Nineteen studies were included in our final analysis. The pooled accuracy of CNN in esophageal neoplasia was 87.2 % (76–93.6) and NPV was 92.1 % (85.9–95.7); the accuracy in lesions of stomach was 85.8 % (79.8–90.3) and NPV was 92.1 % (85.9–95.7); and in colorectal neoplasia the accuracy was 89.9 % (82–94.7) and NPV was 94.3 % (86.4–97.7). Conclusions Based on our meta-analysis, CNN-based AI achieved high accuracy in diagnosis of lesions in esophagus, stomach, and colorectum.

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“…15,31 Recently, artificial intelligence is being increasingly used in clinical medicine including gastroenterology 32,33 endoscopy, 34 and hepatology, 15 radiology, 35 pathology, 36 dentistry, 37 oncology, 38 cardiology, 39 dermatology, 40 neurosurgery, 41 gynecology, 42 and in medical research, particularly big data analysis. Whereas convolutional neural network is the usual network used for image analysis, 43 feed-forward multilayer perceptron networks are the modeling technique for clinical prediction and have been used in the current study as well. Particular advantages of artificial intelligence, that place this technology potentially in higher position than the other modeling techniques include nonlinear method of data analysis, ability to continue learning like human brain by back-propagation and autocorrection, and inclusion of all the variables for prediction rather than a limited number of parameters.…”

Section: Discussionmentioning

confidence: 99%

Prediction of outcome of treatment of acute severe ulcerative colitis using principal component analysis and artificial intelligence

et al. 2020

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Background and Aim: About 15% patients with acute severe ulcerative colitis (UC) fail to respond to medical treatment and may require colectomy. An early prediction of response may help the treating team and the patients and their family to prepare for alternative treatment options. Methods: Data of 263 patients (mean age 37.0 AE 14.0-years, 176, 77% male) with acute severe UC admitted during a 12-year period were used to study predictors of response using univariate analysis, multivariate linear principal component analysis (PCA), and nonlinear artificial neural network (ANN). Results: Of 263 patients, 231 (87.8%) responded to the initial medical treatment that included oral prednisolone (n = 14, 5.3%), intravenous (IV) hydrocortisone (n = 238, 90.5%), IV cyclosporine (n = 9, 3.4%), and inflixmab (n = 2, 0.7%), and 28 (10.6%) did not respond and the remaining 4 (1.5%) died, all of whom did were also nonresponders. Nonresponding patients had to stay longer in the hospital and died more often. On univariate analysis, the presence of complications, the need for use of cyclosporin, lower Hb, platelets, albumin, serum potassium, and higher C-reactive protein were predictors of nonresponse. Hb and albumin were strong predictive factors on both PCA and ANN. Though the nonlinear modeling using ANN had a good predictive accuracy for the response, its accuracy for predicting nonresponse was lower. Conclusion: It is possible to predict the response to medical treatment in patients with UC using linear and nonlinear modeling technique. Serum albumin and Hb are strong predictive factors.

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Convolutional Neural Network for Differentiating Gastric Cancer from Gastritis Using Magnified Endoscopy with Narrow Band Imaging

Cited by 119 publications

References 21 publications

Application of artificial intelligence using a convolutional neural network for diagnosis of early gastric cancer based on magnifying endoscopy with narrow‐band imaging

Application of artificial intelligence using a convolutional neural network for diagnosis of early gastric cancer based on magnifying endoscopy with narrow‐band imaging

Accuracy of convolutional neural network-based artificial intelligence in diagnosis of gastrointestinal lesions based on endoscopic images: A systematic review and meta-analysis

Prediction of outcome of treatment of acute severe ulcerative colitis using principal component analysis and artificial intelligence

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