Artificial intelligence in endoscopy: The challenges and future directions

Abstract

Artificial intelligence based approaches, in particular deep learning, have achieved state-of-the-art performance in medical fields with increasing number of software systems being approved by both Europe and United States. This paper reviews their applications to early detection of oesophageal cancers with a focus on their advantages and pitfalls. The paper concludes with future recommendations towards the development of a real-time, clinical implementable, interpretable and robust diagnosis support systems.

Keywords: Deep learning; Oesophageal cancer; Early detection; Squamous cell cancer; Barrett’s oesophagus

Core Tip: Precancerous changes in the lining of the oesophagus are easily missed during endoscopy as these lesions usually grow flat with only subtle change in colour, surface pattern and microvessel structure. Many factors impair the quality of endoscopy and subsequently the early detection of oesophageal cancer. Artificial intelligence (AI) solutions provide independence from the skills and experience of the operator in lesion recognition. Recent developments have introduced promising AI systems that will support the clinician in recognising, delineating and classifying precancerous and early cancerous changes during the endoscopy of the oesophagus in real-time.