Towards full integration of explainable artificial intelligence in colon capsule endoscopy’s pathway
摘要
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). This is due to several factors, such as low quality bowel cleansing, logistical challenges around both delivery and collection of the capsule, and most importantly, the tedious manual assessment of images after retrieval. Our study, built on the “Danish CareForColon2015 trial (cfc2015)” 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 family of algorithms based on explainable deep neural networks (DNN) that detect polyps within a sequence of images, feed only those images containing polyps into two parallel independent networks to characterize, and estimate the size of important findings. Our recognition DNN to detect colorectal polyps was trained and validated (