Artificial Intelligence for Colonoscopy: Past, Present, and Future

Wallapak Tavanapong, Junghwan Oh, Michael Riegler, Mohammed I. Khaleel, Bhuvan Mitta, Piet C. De Groen

Research output: Contribution to journalArticlepeer-review

Abstract

During the past decades, many automated image analysis methods have been developed for colonoscopy. Realtime 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)
JournalIEEE Journal of Biomedical and Health Informatics
VolumePP
DOIs
StateAccepted/In press - 2022

Bibliographical note

Publisher Copyright:
Author

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

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