Optical Polyp Diagnosis in the Era or Artificial Intelligence

Abstract

The development of new image enhancement modalities and improved endoscopic imaging quality have not led to increased adoption of resect-and-discard in routine practice. Studies have shown that endoscopists have the capacity to achieve quality thresholds to perform optical diagnosis, however, this has not led to acceptance of optical diagnosis as a replacement for pathology for diminutive (1-5mm) polyps. In recent years, Artificial Intelligence (AI)-based Computer Assisted Characterisation (CADx) of diminutive polyps has recently emerged as a strategy that could potentially represent a breakthrough technology to enable widespread adoption of resect-and-discard. Recent evidence suggests that pathology-based diagnosis is suboptimal, as polyp non-retrieval, fragmentation, sectioning errors, incorrect diagnosis as ‘normal mucosa’, and inter-pathologist variability limit the efficacy of pathology for the diagnosis of 1-5mm polyps. New paradigms in performing polyp diagnosis with or without AI have emerged to compete with pathology in terms of efficacy. Strategies, such as Autonomous AI, AI-assisted human diagnosis, AI-unassisted human diagnosis, and combined strategies have been proposed as potential paradigms for resect-and-discard, although further research is still required to determine the optimal strategy. Implementation studies with high patient acceptance, where polyps are truly being discarded without histologic diagnosis are paving the way towards normalizing resect-and-discard in routine clinical practice. Ultimately the largest challenges for CADx remain liability perceptions from endoscopists. The potential benefits of AI-based resect-and-discard are many, with very little potential harm. Real world implementation studies are therefore required to pave the way for the acceptability of such strategies in routine practice.