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Area of Science:

  • Gastroenterology
  • Medical Imaging
  • Artificial Intelligence in Medicine

Background:

  • Adequate colon cleansing is crucial for colon capsule endoscopy (CCE) effectiveness.
  • Interobserver agreement on bowel-cleansing quality in CCE is inconsistent.
  • An artificial intelligence algorithm (AIA) was developed to objectively assess bowel-cleansing quality.

Purpose of the Study:

  • To evaluate the interobserver agreement between experienced CCE readers and an AIA in assessing bowel-cleansing quality.
  • To determine if percentiles of overall bowel-cleansing quality are suitable for reporting AIA results.

Main Methods:

  • Bowel-cleansing quality was scored using 2- and 4-point scales for 842 CCE investigations.
  • Experienced readers and the AIA evaluated the entire colon and specific segments.
  • Agreement was assessed using Cohen's kappa, with AIA scores based on mean, median, quartiles, and percentiles.

Main Results:

  • Minimal to no interobserver agreement was found between CCE readers and the AIA for overall and segmental bowel-cleansing quality.
  • This lack of agreement persisted across both 2- and 4-point grading scales.
  • The AIA's performance was inconsistent regardless of the scoring threshold used.

Conclusions:

  • There is minimal agreement in evaluating CCE bowel-cleansing quality between human readers and the developed AIA.
  • Current AIA grading methods, including mean scores or percentiles, are not appropriate for reporting bowel-cleansing quality in CCE.
  • Further development is needed for AI tools to reliably assess CCE bowel preparation.