  Colonoscopy is the standard of care technique for detecting and removing
polyps for the prevention of colorectal cancer. Nevertheless,
gastroenterologists (GI) routinely miss approximately 25% of polyps during
colonoscopies. These misses are highly operator dependent, influenced by the
physician skills, experience, vigilance, and fatigue. Standard quality metrics,
such as Withdrawal Time or Cecal Intubation Rate, have been shown to be well
correlated with Adenoma Detection Rate (ADR). However, those metrics are
limited in their ability to assess the quality of a specific procedure, and
they do not address quality aspects related to the style or technique of the
examination. In this work we design novel online and offline quality metrics,
based on visual appearance quality criteria learned by an ML model in an
unsupervised way. Furthermore, we evaluate the likelihood of detecting an
existing polyp as a function of quality and use it to demonstrate high
correlation of the proposed metric to polyp detection sensitivity. The proposed
online quality metric can be used to provide real time quality feedback to the
performing GI. By integrating the local metric over the withdrawal phase, we
build a global, offline quality metric, which is shown to be highly correlated
to the standard Polyp Per Colonoscopy (PPC) quality metric.
