Background and Aims
Abbreviations:AI (artificial intelligence), AUC (area under curve), BLI (blue laser imaging), CADe (computer-aided detection), CI (confidence interval), DCNN (deep convolutional neutral network), GA (gastric atrophy), IEE (image-enhanced endoscopy), IM (intestinal metaplasia), MAPS II (management of epithelial precancerous conditions and lesions in the stomach), ME (magnifying endoscopy), NBI (narrow-band imaging), NPV (negative predictive value), PPV (positive predictive value), ROC (receiver operating characteristic), WLE (white-light endoscopy)
Purchase one-time access:Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
One-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
- Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.CA Cancer J Clin. 2018; 68: 394-424
- Human gastric carcinogenesis: a multistep and multifactorial process—first American Cancer Society award lecture on cancer epidemiology and prevention.Cancer Res. 1992; 52: 6735-6740
- The two histological main types of gastric carcinoma: diffuse and so-called intestinal-type carcinoma. An attempt at a histo-clinical classification.Acta Pathol Microbiol Scand. 1965; 64: 31-49
- Gastric atrophy, metaplasia, and dysplasia: a clinical perspective.J Clin Gastroenterol. 2003; 36 (discussion S61-S62): S29-S36
- Correlation between the prevalence of gastritis and gastric cancer in Japan.Cancer Causes Control. 1993; 4: 17-20
- Gastric atrophy and atrophic gastritis—nebulous concepts in search of a definition.Aliment Pharmacol Ther. 1998; 12: 17-23
- Histologic intestinal metaplasia and endoscopic atrophy are predictors of gastric cancer development after Helicobacter pylori eradication.Gastrointest Endosc. 2016; 84: 618-624
- Adenocarcinoma risk in gastric atrophy and intestinal metaplasia: a systematic review.BMC Gastroenterol. 2017; 17: 157
- Advanced endoscopic imaging: European Society of Gastrointestinal Endoscopy (ESGE) technology review.Endoscopy. 2016; 48: 1029-1045
- Narrow-band imaging: clinical application in gastrointestinal endoscopy.GE Port J Gastroenterol. 2018; 26: 40-53
- Where should gastric biopsies be performed when areas of intestinal metaplasia are observed?.Endosc Int Open. 2019; 7: E1636-E1639
- Management of epithelial precancerous conditions and lesions in the stomach (MAPS II): European Society of Gastrointestinal Endoscopy (ESGE), European Helicobacter and Microbiota Study Group (EHMSG), European Society of Pathology (ESP), and Sociedade Portuguesa de Endoscopia Digestiva (SPED) guideline update 2019.Endoscopy. 2019; 51: 365-388
- A multicenter prospective study of the real-time use of narrow-band imaging in the diagnosis of premalignant gastric conditions and lesions.Endoscopy. 2016; 48: 723-730
- Extending magnifying NBI diagnosis of intestinal metaplasia in the stomach: the white opaque substance marker.Endoscopy. 2017; 49: 529-535
- A new method of diagnosing gastric intestinal metaplasia: narrow-band imaging with magnifying endoscopy.Endoscopy. 2006; 38: 819-824
- Image-enhanced endoscopy for gastric preneoplastic conditions and neoplastic lesions: a systematic review and meta-analysis.Endoscopy. 2020; 52: 1048-1065
- Dermatologist-level classification of skin cancer with deep neural networks.Nature. 2017; 542: 115-118
- Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs.JAMA. 2016; 316: 2402-2410
- Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes.JAMA. 2017; 318: 2211-2223
- Deep learning.Nature. 2015; 521: 436
- Deep learning in biomedicine.Nat Biotechnol. 2018; 36: 829-838
- Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): a randomised controlled study.Lancet Gastroenterol Hepatol. 2020; 5: 352-361
- Randomised controlled trial of WISENSE, a real-time quality improving system for monitoring blind spots during esophagogastroduodenoscopy.Gut. 2019; 68: 2161-2169
- A novel artificial intelligence system for the assessment of bowel preparation (with video).Gastrointest Endosc. 2020; 91: 428-435
- Automated and real-time validation of gastroesophageal varices under esophagogastroduodenoscopy using deep convolutional neural network: a multicenter retrospective study (with video).Gastrointest Endosc. 2021; 93: 422-432.e3
Zhang Y, Li F, Yuan F, et al. Diagnosing chronic atrophic gastritis by gastroscopy using artificial intelligence. Dig Liver Dis 2020;52:566-572.
Kanai M, Togo R, Ogawa T, et al. Chronic atrophic gastritis detection with a convolutional neural network considering stomach regions. World J Gastroenterol 2020;26:3650-9.
Togo R, Yamamichi N, Mabe K, et al. Detection of gastritis by a deep convolutional neural network from double-contrast upper gastrointestinal barium X-ray radiography. J Gastroenterol 2019;54:321-9.
- Deep-learning based detection of gastric precancerous conditions.Gut. 2020; 69: 4-6
- A deep neural network improves endoscopic detection of early gastric cancer without blind spots.Endoscopy. 2019; 51: 522-531
- Classification and grading of gastritis. The updated Sydney System. International Workshop on the Histopathology of Gastritis, Houston 1994.Am J Surg Pathol. 1996; 20: 1161-1181
He K, Zhang X, Ren S, et al. Deep residual learning for image recognition. In IEEE Conference on Computer Vision & Pattern Recognition (CVPR), Las Vegas, Nevada, USA, 2016, 770-8. Available at: https://ieeexplore.ieee.org/document/7780459. Accessed June 30, 2021.
- Very deep convolutional networks for large-scale image recognition. ICLR.2015 (arXiv:1409.1556. Available at: https://arxiv.org/abs/1409.1556)
- Densely connected convolutional networks..2017 (IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, Hawaii, USA, 2017. 2261-9. Available at: https://ieeexplore.ieee.org/document/8099726. Accessed June 30, 2021)
- EfficientNet: rethinking model scaling for convolutional neural networks. ICML.2019 (arXiv:1905..11946. Available at: https://arxiv.org/abs/1905.11946)
- Transfer learning for visual categorization: a survey.IEEE Trans Neural Netw Learn Syst. 2015; 26: 1019-1034
Abadi MAA, Barham P, Brevdo E, et al. TensorFlow: Large-scale machine learning on heterogeneous distributed systems. arXiv preprint 2016;arXiv:1603.04467.
- The dropout learning algorithm.Artif Intell. 2014; 210: 78-122
- Automatic early stopping using cross validation: quantifying the criteria.Neural Netw. 1998; 11: 761-767
- The calculation of posterior distribution by data augmentation.J Am Stat Assoc. 1987; 82: 528
- The correlation of endoscopic and histological diagnosis of gastric atrophy.Dig Dis Sci. 2010; 55: 1364-1375
- Relationship of gastroscopic features to histological findings in gastritis and Helicobacter pylori infection in a general population sample.Endoscopy. 2003; 35: 946-950
- A multicenter randomized comparison between high-definition white light endoscopy and narrow band imaging for detection of gastric lesions.Eur J Gastroenterol Hepatol. 2015; 27: 1473-1478
- Systematic review of the diagnosis of gastric premalignant conditions and neoplasia with high-resolution endoscopic technologies.Scand J Gastroenterol. 2013; 48: 1108-1117
- Narrow-band imaging versus white light versus mapping biopsy for gastric intestinal metaplasia: a prospective blinded trial.Gastrointest Endosc. 2017; 86: 857-865
- Diagnostic ability of magnifying endoscopy with blue laser imaging for early gastric cancer: a prospective study.Gastric Cancer. 2017; 20: 297-303
- Magnifying blue laser imaging versus magnifying narrow-band imaging for the diagnosis of early gastric cancer: a prospective, multicenter, comparative study.Digestion. 2017; 96: 127-134
- Overview of deep learning in gastrointestinal endoscopy.Gut Liver. 2019; 13: 388-393
- Intelligent diagnosis of gastric intestinal metaplasia based on convolutional neural network and limited number of endoscopic images.Comput Biol Med. 2020; 126: 1-8
DISCLOSURE: The following author received research support for this study from the National Natural Science Foundation of China (grant no. 81672387): H. Yu. All other authors disclosed no financial relationships. Research support for this study was provided in part by the Project of Hubei Provincial Clinical Research Center for Digestive Disease Minimally Invasive Incision (grant no. 2018BCC337) and Hubei Province Major Science and Technology Innovation Project (grant no. 2018-916-000-008).