Towards Full Integration of Artificial Intelligence in Colon Capsule Endoscopy's Pathway

TL;DR

This study advances colon capsule endoscopy by developing AI algorithms for autonomous detection, characterization, and sizing of colorectal polyps, aiming for full clinical integration.

Contribution

It introduces a comprehensive AI pipeline with high accuracy for polyp detection, classification, and size estimation in colon capsule endoscopy.

Findings

Polyp detection sensitivity of 99.9% and specificity of 99.4%.

Polyps characterization sensitivity of 82% and specificity of 80%.

Size estimation accuracy of 88%.

Abstract

Despite recent surge of interest in deploying colon capsule endoscopy (CCE) for early diagnosis of colorectal diseases, there remains a large gap between the current state of CCE in clinical practice, and the state of its counterpart optical colonoscopy (OC). Our study is aimed at closing this gap, by focusing on the full integration of AI in CCE's pathway, where image processing steps linked to the detection, localization and characterisation of important findings are carried out autonomously using various AI algorithms. We developed a recognition network, that with an impressive sensitivity of 99.9%, a specificity of 99.4%, and a negative predictive value (NPV) of 99.8%, detected colorectal polyps. After recognising a polyp within a sequence of images, only those images containing polyps were fed into two parallel independent networks for characterisation, and estimation of the size of those important findings. The characterisation network reached a sensitivity of 82% and a specificity of 80% in classifying polyps to two groups, namely neoplastic vs. non-neoplastic. The size estimation network reached an accuracy of 88% in correctly segmenting the polyps. By automatically incorporating this crucial information into CCE's pathway, we moved a step closer towards the full integration of AI in CCE's routine clinical practice.