Published online Jul 28, 2020. doi: 10.35712/aig.v1.i1.1
Peer-review started: July 1, 2020
First decision: July 15, 2020
Revised: July 18, 2020
Accepted: July 21, 2020
Article in press: July 21, 2020
Published online: July 28, 2020
Processing time: 25 Days and 14.1 Hours
Artificial intelligence (AI) has grown tremendously in the last decades and is undoubtedly the future era in medicine. Concerning digestive diseases, applications of AI include clinical gastroenterology, gastrointestinal endoscopy and imaging, and not least pathological diagnosis. Several gastrointestinal pathologies require histological confirmation for a positive diagnosis. Among them, celiac disease (CD) diagnosis has been in the spotlight over time, but controversy is still ongoing with regard to the so-called celiac-type histology. Despite efforts to improve histological diagnosis in CD, there are still several issues and pitfalls associated with duodenal histology reading. Several papers have assessed the accuracy of AI techniques in detecting CD on duodenal biopsy images and have shown high diagnostic performance over standard histology reading. We discuss the role of computer-assisted histology in improving the assessment of mucosal architectural injury and inflammation in CD patients, both for diagnosis and follow-up.
Core tip: Histology in celiac disease (CD) diagnosis is hampered by several pitfalls, from low adherence to biopsy sampling recommendations and reporting of results to significant inter-observer variability. A quantitative, computer-assisted histological assessment of mucosal biopsies could overcome many of the current limitations of conventional histology. We herein discuss the current evidence on artificial intelligence-based histology in CD diagnosis and its role in improving histological measurements in CD.