A technical review of artificial intelligence as applied to gastrointestinal endoscopy: clarifying the terminology

Abstract

Background and aim The growing number of publications on the application of artificial intelligence (AI) in medicine underlines the enormous importance and potential of this emerging field of research. In gastrointestinal endoscopy, AI has been applied to all segments of the gastrointestinal tract most importantly in the detection and characterization of colorectal polyps. However, AI research has been published also in the stomach and esophagus for both neoplastic and non-neoplastic disorders. The various technical as well as medical aspects of AI, however, remain confusing especially for non-expert physicians. This physician-engineer co-authored review explains the basic technical aspects of AI and provides a comprehensive overview of recent publications on AI in gastrointestinal endoscopy. Finally, a basic insight is offered into understanding publications on AI in gastrointestinal endoscopy.

Fig. 1

Overview of artificial intelligence (AI), machine learning (ML) and deep learning (DL) .

Fig. 2

Deep learning (DL) based on convolutional neural networks (CNN) showing the input layer with raw data of the image, the hidden layer with a series of convolutions computed for each layer and the classification of the image in the output layer.

Fig. 3

Automatic tumor classification and segmentation on two endoscopic images ( a, c ) are shown by colored contours ( c, d ) overlaid on the original images as so-called heat maps.