Optical diagnosis of colorectal polyps using convolutional neural networks

Abstract

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; Deep learning; Convolutional neural networks; Computer aided diagnosis; Optical diagnosis; Colorectal polyps

Core Tip: A convolutional neural network (CNN) is a specific type of artificial intelligence deep learning. These networks may play an important role in the coming years in assisting endoscopists to optically diagnose colorectal polyps. CNNs can mitigate the inter-operator variability amongst endoscopists, potentially enabling a “resect and discard” or “leave in” strategy to be adopted. This would improve the efficiency of colonoscopy, reduce healthcare costs and reduce adverse events for patients by avoiding unnecessary resections of non-neoplastic polyps. In this article, we expand on the most relevant studies in this field and discuss limitations and future directions that will determine fulfilment of the potential of CNN in the optical diagnosis of colorectal polyps.