Role of artificial intelligence in multidisciplinary imaging diagnosis of gastrointestinal diseases

Abstract

The use of artificial intelligence-based tools is regarded as a promising approach to increase clinical efficiency in diagnostic imaging, improve the interpretability of results, and support decision-making for the detection and prevention of diseases. Radiology, endoscopy and pathology images are suitable for deep-learning analysis, potentially changing the way care is delivered in gastroenterology. The aim of this review is to examine the key aspects of different neural network architectures used for the evaluation of gastrointestinal conditions, by discussing how different models behave in critical tasks, such as lesion detection or characterization (i.e. the distinction between benign and malignant lesions of the esophagus, the stomach and the colon). To this end, we provide an overview on recent achievements and future prospects in deep learning methods applied to the analysis of radiology, endoscopy and histologic whole-slide images of the gastrointestinal tract.

Keywords: Artificial intelligence; Machine learning; Radiology; Endoscopy; Pathology; Gastroenterology

Core Tip: Artificial intelligence in general, and machine learning (ML) in particular, have great potential as supporting tools for physicians in the evaluation of neoplastic diseases and other conditions of the gastrointestinal tract. Radiology, endoscopy and pathology images can be read and interpreted using ML approaches in a wide variety of clinical scenarios. These include detection, classification and automatic segmentation of tumor lesions, tumor grading, patient stratification and prediction of treatment response.