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Opinion
Human-machine collaboration: bringing artificial intelligence into colonoscopy
  1. Omer F Ahmad1,
  2. Danail Stoyanov1,
  3. Laurence B Lovat1,2
  1. 1 Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, London, UK
  2. 2 Division of Surgery and Interventional Science, University College London, London, UK
  1. Correspondence to Dr Omer F Ahmad, Wellcome/EPSRC Centre for Interventional and Surgical Sciences (WEISS), University College London, London, WC1E 6BT, UK; o.ahmad{at}ucl.ac.uk

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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 …

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Footnotes

  • Contributors All authors (OFA, DS, LBL) contributed equally to the intellectual content, drafting, critical revisions and final approval of the manuscript.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Patient consent Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

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