Using artificial intelligence to improve adequacy of inspection in gastrointestinal endoscopy

Research output: Contribution to journalReview articlepeer-review

Abstract

Adequacy of manual endoscopic inspection of the upper and lower gastrointestinal mucosa is operator-dependent: it is common knowledge that the likelihood of finding lesions and the degree of cancer prevention of manual endoscopic procedures is dependent on the skillset and effort by endoscopists. Recent developments in artificial intelligence allow measurement of skillset and effort during actual endoscopic procedures. Many endoscopic features, representing skillset and effort, such as clarity of image, absence of stool, looking sideways, performing retroflexion and obtaining all possible mucosal views, can be classified or counted, and the results presented in real-time to endoscopists and stored at the end of the procedure as an automated, objective report with representative images documenting adequacy of inspection. Real-time feedback provides endoscopists the option, when measurements suggest inadequate or incomplete inspection, to change technique or repeat and expand inspection. However, responding to real-time feedback and obtaining best possible measurements or all possible mucosal views do not equate to careful inspection; endoscopists may be focused on obtaining best measurements instead of inspecting the mucosa. Therefore, prospective studies with long-term follow-up will be required to determine whether artificial intelligence driven real-time feedback will lead not only to better intra-procedural measurements but also to improved patient outcome, for example, a decrease in gastrointestinal cancer incidence and mortality.

Original languageEnglish (US)
Pages (from-to)71-79
Number of pages9
JournalTechniques and Innovations in Gastrointestinal Endoscopy
Volume22
Issue number2
DOIs
StatePublished - Apr 2020

Bibliographical note

Funding Information:
Financial support: This work was supported by grant DK106130 from the NIDDK and the University of Minnesota.Conflicts of interest: Piet de Groen has a financial interest in EndoMetric INC, a company that analyzes colonoscopy video streams for features of quality.

Funding Information:
Financial support: This work was supported by grant DK106130 from the NIDDK and the University of Minnesota .

Publisher Copyright:
© 2019 Elsevier Inc.

Keywords

  • Artificial intelligence
  • Colonoscopy
  • Deep learning
  • Endoscopy
  • Mucosal inspection
  • Quality
  • Real-time feedback

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