Human-machine collaboration: bringing artificial intelligence into colonoscopy

Colorectal cancer (CRC) is the third most common malignancy worldwide. Colonoscopy offers protection against the development of CRC by detection and resection of neoplastic lesions. Unfortunately, the procedure remains highly operator dependent. A meta-analysis of tandem colonoscopy studies revealed a pooled miss rate of 22% for polyps of any size.1 Postcolonoscopy CRCs are associated with low adenoma detection rates (ADR) and incompletely resected or missed lesions are recognised as key contributory factors.2 International efforts to improve quality must be commended, particularly those led by the Joint Advisory Group on Gastrointestinal Endoscopy in the UK, where individual colonoscopy performance is assessed by quality assurance using key performance indicators. Despite these efforts further significant improvement is needed. Numerous strategies have been used in attempts to improve ADRs including educational interventions, enhanced imaging techniques and mechanical devices to improve mucosal exposure. Computer-aided detection and diagnosis (CAD) systems using advanced artificial intelligence (AI) techniques represent an emerging technology that will likely lead to a paradigm shift in the field.3 Machine learning is a …