Application of artificial intelligence using a convolutional neural network for detecting gastric cancer in endoscopic images
Open accessAbstract
Background Image recognition using artificial intelligence with deep learning through convolutional neural networks (CNNs) has dramatically improved and been increasingly applied to medical fields for diagnostic imaging. We developed a CNN that can automatically detect gastric cancer in endoscopic images.Methods A CNN-based diagnostic system was constructed based on Single Shot MultiBox Detector architecture and trained using 13,584 endoscopic images of gastric cancer. To evaluate the diagnostic accuracy, an independent test set of 2296 stomach images collected from 69 consecutive patients with 77 gastric cancer lesions was applied to the constructed CNN.Results The CNN required 47 s to analyze 2296 test images. The CNN correctly diagnosed 71 of 77 gastric cancer lesions with an overall sensitivity of 92.2%, and 161 non-cancerous lesions were detected as gastric cancer, resulting in a positive predictive value of 30.6%. Seventy of the 71 lesions (98.6%) with a diameter of 6 mm or more as well as all invasive cancers were correctly detected. All missed lesions were superficially depressed and differentiated-type intramucosal cancers that were difficult to distinguish from gastritis even for experienced endoscopists. Nearly half of the false-positive lesions were gastritis with changes in color tone or an irregular mucosal surface.Conclusion The constructed CNN system for detecting gastric cancer could process numerous stored endoscopic images in a very short time with a clinically relevant diagnostic ability. It may be well applicable to daily clinical practice to reduce the burden of endoscopists.
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Applications of AI in gastroenterology include esophageal cancer screening, detecting gastric cancer, detecting stomach infections such as H. Pylori, and finding hookworms.
“Applications include esophageal cancer screening 46 , detecting gastric cancer 47,48 , detecting stomach infections such as H. Pylori 49 , and even finding hookworms 50 .”
From Deep learning-enabled medical computer vision (2021) · cites this paper as [47]
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- Authors
- Hirasawa, T., Aoyama, K., Tanimoto, T., Ishihara, S., Shichijo, S., Ozawa, T., Ohnishi, T., Fujishiro, M., Matsuo, K., Fujisaki, J., & Tada, T.
- Affiliations
- The Cancer Institute Hospital (JP)
- AI Medical Service Inc. (Japan) (JP)
- Medical Governance Research Institute (JP)
- Sanno Hospital (JP)
- Osaka International Cancer Institute (JP)
- The University of Tokyo (JP)
- Tokatsu Hospital (JP)
- Tada Tomohiro The Institute of Gastroenterology & Proctology (JP)
- Corresponding author
- Hirasawa, Toshiaki
- ORCID
- Venue
- Gastric Cancer
- Published
- 2018-01-15
- Volume
- 21
- Issue
- 4
- Pages
- 653-660
- ISSN
- 1436-3291 (ISSN-L)
- 1436-3305
- OpenAlex
- W2783201053
- Cited by
- 744
- References
- 36
- Citations by year
- 2026: 62 · 2025: 62 · 2024: 81 · 2023: 104 · 2022: 114 · 2021: 122 · 2020: 122 · 2019: 58 · 2018: 17 · 2017: 1
- FWCI
- 44.94
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Gastric Cancer (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Japan Society for the Promotion of Science (17K19673)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
- Health Sciences › Medicine › Oncology › Colorectal Cancer Screening and Detection
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Gastric Cancer Management and Outcomes
- Keywords
- gastric cancer detection, endoscopic images, convolutional neural networks, Single Shot MultiBox Detector, computer-aided diagnosis, false-positive lesions, gastritis, intramucosal carcinoma
- MeSH terms
- Adult, Aged, Aged, 80 and over, Artificial Intelligence, Female, Humans, Image Processing, Computer-Assisted, Male, Middle Aged, Stomach Neoplasms, Endoscopy, Gastrointestinal, Neural Networks, Computer
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