Towards Full Integration of Artificial Intelligence in Colon Capsule Endoscopy's Pathway
This work addresses the need for more efficient and accurate early diagnosis of colorectal diseases in clinical practice, representing an incremental step towards full AI integration in CCE.
The study tackled the gap between colon capsule endoscopy (CCE) and optical colonoscopy by integrating AI for autonomous detection, localization, and characterization of colorectal polyps, achieving high performance metrics such as 99.9% sensitivity for detection and 82% sensitivity for classification.
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.