  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.
