Real-time characterization of colorectal polyps using artificial intelligence: A prospective pilot study comparing two computer-aided diagnosis systems and one expert endoscopist

Aims Artificial intelligence (AI) has great potential in gastrointestinal endoscopy. Aim was to evaluate real-time diagnostic performances of our Artificial Intelligence for ColoRectal Polyps (AI4CRP) computer-aided diagnosis system for optical diagnosis of diminutive colorectal polyps (CRPs) and compare it with CAD EYE and an expert endoscopist.

Methods AI4CRP was developed using convolutional neural networks and previously trained and tested. In this prospective real-time pilot study, AI4CRP was compared with CAD EYE© (Fujifilm, Tokyo, Japan) and one expert endoscopist unaware of AI-output. Blue light imaging was used for characterization and histopathology as gold standard. CRPs were characterized as hyperplastic (hyperplastic polyps) or neoplastic (adenomas, sessile serrated lesions[SSLs]) by AI4CRP and the endoscopist, and as hyperplastic (hyperplastic polyps, SSLs) or neoplastic (adenomas) by CAD EYE. CAD EYE’s inconclusive diagnoses were excluded. Enabling self-critical AI4CRP, post-hoc analysis excluded low confidence scores.

Results Real-time testing included 30 patients with 51 CRPs (32 adenomas, 6 SSLs, 12 hyperplastic polyps). AI4CRP had a diagnostic accuracy of 80.4%, sensitivity of 82.1%, and specificity of 75.0%. For self-critical AI4CRP (n=37) the diagnostic accuracy was 89.2%, sensitivity 89.7%, and specificity 87.5%. CAD EYE (n=49) had a diagnostic accuracy of 83.7%, sensitivity of 74.2%, and specificity of 100.0%. For the expert endoscopist the diagnostic accuracy was 88.2%, sensitivity 94.9%, and specificity 66.7%.