Optical diagnosis of colorectal polyps using convolutional neural networks

World J Gastroenterol. 2021 Sep 21;27(35):5908-5918. doi: 10.3748/wjg.v27.i35.5908.


Colonoscopy remains the gold standard investigation for colorectal cancer screening as it offers the opportunity to both detect and resect pre-malignant and neoplastic polyps. Although technologies for image-enhanced endoscopy are widely available, optical diagnosis has not been incorporated into routine clinical practice, mainly due to significant inter-operator variability. In recent years, there has been a growing number of studies demonstrating the potential of convolutional neural networks (CNN) to enhance optical diagnosis of polyps. Data suggest that the use of CNNs might mitigate the inter-operator variability amongst endoscopists, potentially enabling a "resect and discard" or "leave in" strategy to be adopted in real-time. This would have significant financial benefits for healthcare systems, avoid unnecessary polypectomies of non-neoplastic polyps and improve the efficiency of colonoscopy. Here, we review advances in CNN for the optical diagnosis of colorectal polyps, current limitations and future directions.

Keywords: Artificial intelligence; Colorectal polyps; Computer aided diagnosis; Convolutional neural networks; Deep learning; Optical diagnosis.

Publication types

  • Review

MeSH terms

  • Colonic Polyps* / diagnostic imaging
  • Colonic Polyps* / surgery
  • Colonoscopy
  • Colorectal Neoplasms* / diagnostic imaging
  • Early Detection of Cancer
  • Humans
  • Neural Networks, Computer