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

Abstract

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.

Keywords: Artificial intelligence; Computer-aided diagnosis; Deep learning; Gastrointestinal endoscopy; Learning dataset.

Fig. 1.

Layers of the convolutional neural networks.

Fig. 2.

Class activation map of a capsule endoscopy image. (A) Erosions with depression of the mucosa are highlighted in red. (B) Detection of vascular lesions on the small-bowel mucosa is visualized in a class activation map.

Fig. 3.

Annotation process of capsule endoscopy images for the development of convolutional neural networks. Three categorial numbers will be applied to each image in terms of medical significance, degree of protrusion and type of lesion (e.g., vascular, inflammatory, polypoid) according to a predefined reference standard.