Preparation of image databases for artificial intelligence algorithm development in gastrointestinal endoscopy

• Main Authors: Chang Bong Yang (Author), Sang Hoon Kim (Author), Yun Jeong Lim (Author)
• Format: Book
• Published: Korean Society of Gastrointestinal Endoscopy, 2022-09-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 2234-2400; 2234-2443; 10.5946/ce.2021.229
• Keywords: artificial intelligence; deep learning; gastrointestinal endoscopy; Internal medicine; RC31-1245; Diseases of the digestive system. Gastroenterology; RC799-869
• Source: Clinical Endoscopy, Vol 55, Iss 5, Pp 594-604 (2022)

Over the past decade, technological advances in deep learning have led to the introduction of artificial intelligence (AI) in medical imaging. The most commonly used structure in image recognition is the convolutional neural network, which mimics the action of the human visual cortex. The applications of AI in gastrointestinal endoscopy are diverse. Computer-aided diagnosis has achieved remarkable outcomes with recent improvements in machine-learning techniques and advances in computer performance. Despite some hurdles, the implementation of AI-assisted clinical practice is expected to aid endoscopists in real-time decision-making. In this summary, we reviewed state-of-the-art AI in the field of gastrointestinal endoscopy and offered a practical guide for building a learning image dataset for algorithm development.