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Overview of Deep Learning in Gastrointestinal Endoscopy
Jun Ki Min1, Min Seob Kwak1, Jae Myung Cha1
1Department of Internal Medicine, Kyung Hee University School of Medicine, Seoul, Korea.
Gut and Liver
|January 12, 2019
Summary
Artificial intelligence (AI) is revolutionizing gastroenterology, particularly in endoscopic diagnostics. Convolutional neural networks show promise for accurate, automated detection of conditions like H. pylori infection and colorectal polyps.
Area of Science:
- Gastroenterology
- Medical Imaging
- Artificial Intelligence
Background:
- Artificial intelligence (AI) is poised to transform healthcare, with medical image-based diagnostics like endoscopy being an early frontier.
- The integration of AI into gastroenterology practice is anticipated soon, enhancing diagnostic capabilities.
Purpose of the Study:
- To review the impact of artificial intelligence on gastroenterology.
- To highlight AI's role in automatic diagnosis using endoscopic findings.
Main Methods:
- Focus on convolutional neural networks (CNNs), a deep-learning method.
- Application of CNNs in esophagogastroduodenoscopy, colonoscopy, and capsule endoscopy.
Main Results:
- CNNs demonstrated high utility in recognizing anatomical locations, H. pylori infection, and gastric cancer in esophagogastroduodenoscopy.
- CNNs showed effectiveness in detecting and classifying colorectal polyps and recognizing celiac disease and hookworm.
- CNNs were utilized for small intestine motility characterization in capsule endoscopy.
Conclusions:
- AI, particularly CNNs, is expected to significantly improve diagnostic accuracy for endoscopists by automating lesion detection and classification.
- Endoscopists must engage with this emerging AI technology to leverage its benefits in gastroenterology.
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