Towards full integration of explainable artificial intelligence in colon capsule endoscopy’s pathway

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 ([Formula: see text]) and tested ([Formula: see text]) on an unaugmented database of 1751 images containing colorectal polyps and 1672 images of normal mucosa reached an impressive sensitivity of [Formula: see text], a specificity of [Formula: see text], and a negative predictive value (NPV) of [Formula: see text]. The characterisation DNN trained on an unaugmented database of 317 images featuring neoplastic polyps and 162 images of non-neoplastic polyps reached a sensitivity of [Formula: see text] and a specificity of [Formula: see text] in classifying polyps. The size estimation DNN trained on an unaugmented database of 280 images reached an accuracy of [Formula: see text] in correctly segmenting the polyps. By automatically incorporating important information including size, location and pathology of the findings into CCE's pathway, we moved a step closer towards the full integration of explainable AI (XAI) in CCE's routine clinical practice. This translates into a fewer number of unnecessary investigations and resection of diminutive, insignificant colorectal polyps.