Time-saving polyp detection in colon capsule endoscopy: evaluation of a novel software algorithm

Artificial intelligence to improve polyp detection and screening time in colon capsule endoscopy

Colon Capsule Endoscopy (CCE) is a minimally invasive procedure which is increasingly being used as an alternative to conventional colonoscopy. Videos recorded by the capsule cameras are long and require one or more experts' time to review and identify polyps or other potential intestinal problems that can lead to major health issues. We developed and tested a multi-platform web application, AI-Tool, which embeds a Convolution Neural Network (CNN) to help CCE reviewers. With the help of artificial intelligence, AI-Tool is able to detect images with high probability of containing a polyp and prioritize them during the reviewing process. With the collaboration of 3 experts that reviewed 18 videos, we compared the classical linear review method using RAPID Reader Software v9.0 and the new software we present. Applying the new strategy, reviewing time was reduced by a factor of 6 and polyp detection sensitivity was increased from 81.08 to 87.80%.

How should we do colon capsule endoscopy reading: a practical guide

In this article, we aim to provide general principles as well as personal views for colonic capsule endoscopy. To allow an in-depth understanding of the recommendations, we also present basic technological characteristics and specifications, with emphasis on the current as well as the previous version of colonic capsule endoscopy and relevant software. To date, there is no scientific proof to support the optimal way of reading a colonic capsule endoscopy video, or any standards or guidelines exist. Hence, any advice is a mixture of recommendations by the capsule manufacturer and experts’ opinion. Furthermore, there is a paucity of data regarding the use of term(s) (pre-reader/reader-validator) in colonic capsule endoscopy. We also include a couple of handy tables in order to get info at a glance.