Evaluation by a Machine Learning System of Two Preparations for Small Bowel Capsule Endoscopy: The BUBS (Burst Unpleasant Bubbles with Simethicone) Study

Abstract

Background: Bubbles often mask the mucosa during capsule endoscopy (CE). Clinical scores assessing the cleanliness and the amount of bubbles in the small bowel (SB) are poorly reproducible unlike machine learning (ML) solutions. We aimed to measure the amount of bubbles with ML algorithms in SB CE recordings, and compare two polyethylene glycol (PEG)-based preparations, with and without simethicone, in patients with obscure gastro-intestinal bleeding (OGIB). Patients & Methods: All consecutive outpatients with OGIB from a tertiary care center received a PEG-based preparation, without or with simethicone, in two different periods. The primary outcome was a difference in the proportions (%) of frames with abundant bubbles (>10%) along the full-length video sequences between the two periods. SB CE recordings were analyzed by a validated computed algorithm based on a grey-level of co-occurrence matrix (GLCM), to assess the abundance of bubbles in each frame. Results: In total, 105 third generation SB CE recordings were analyzed (48 without simethicone and 57 with simethicone-added preparations). A significant association was shown between the use of a simethicone-added preparation and a lower abundance of bubbles along the SB (p = 0.04). A significantly lower proportion of “abundant in bubbles” frames was observed in the fourth quartile (30.5% vs. 20.6%, p = 0.02). There was no significant impact of the use of simethicone in terms of diagnostic yield, SB transit time and completion rate. Conclusion: An accurate and reproducible computed algorithm demonstrated significant decrease in the abundance of bubbles along SB CE recordings, with a marked effect in the last quartile, in patients for whom simethicone had been added in PEG-based preparations, compared to those without simethicone.

From Data to Insights: How Is AI Revolutionizing Small-Bowel Endoscopy?

“…Although there was no significant impact on the diagnostic and transit time, there was a marked reduction in the abundance of bubbles over the SB, specifically in the distal ileum. This research was significant as it enhanced the potential role of AI in establishing the gold standard for preparation in SB CE [71].…”

Identification of Circular Patterns in Capsule Endoscopy Bubble Frames

“…Thus, they may be one of the agents containing important information that should be detected from WCE video frames to enhance the diagnostic process or assessing cleanliness and digestion processes. [ 13 14 15 ]…”

“…Assessing and analyzing bubbles in the small bowel by humans is subjective and unreliable because of inaccessibility, but computer vision-based algorithms can enhance accuracy and objectivity in measuring bubbles in WCE images. [ 13 ] There are several approaches for identification these agents: morphological approaches, texture-based approaches, and color-based approaches. The morphological approaches aim the unique structure of bubbles in the WCE images.…”