Artificial Intelligence for Colonoscopy: Past, Present, and Future

Wallapak Tavanapong, Jung Hwan Oh, Michael A. Riegler, Mohammed Khaleel, Bhuvan Mittal, Piet C. De Groen

Research output: Contribution to journalArticlepeer-review

28 Scopus citations

Abstract

During the past decades, many automated image analysis methods have been developed for colonoscopy. Real-time implementation of the most promising methods during colonoscopy has been tested in clinical trials, including several recent multi-center studies. All trials have shown results that may contribute to prevention of colorectal cancer. We summarize the past and present development of colonoscopy video analysis methods, focusing on two categories of artificial intelligence (AI) technologies used in clinical trials. These are (1) analysis and feedback for improving colonoscopy quality and (2) detection of abnormalities. Our survey includes methods that use traditional machine learning algorithms on carefully designed hand-crafted features as well as recent deep-learning methods. Lastly, we present the gap between current state-of-the-art technology and desirable clinical features and conclude with future directions of endoscopic AI technology development that will bridge the current gap.

Original languageEnglish (US)
Pages (from-to)3950-3965
Number of pages16
JournalIEEE Journal of Biomedical and Health Informatics
Volume26
Issue number8
DOIs
StatePublished - Aug 1 2022

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

Keywords

  • Artificial Intelligence
  • Bioinformatics
  • Clinical trials
  • Colon
  • Colonoscopy
  • Endoscopes
  • Inspection
  • Machine Learning
  • Medical Image Analysis
  • Real-time Systems
  • Spirals

PubMed: MeSH publication types

  • Journal Article
  • Research Support, N.I.H., Extramural

Fingerprint

Dive into the research topics of 'Artificial Intelligence for Colonoscopy: Past, Present, and Future'. Together they form a unique fingerprint.

Cite this