Background and Aims
A reliable assessment of bowel preparation is important to ensure high-quality colonoscopy.
Current bowel preparation scoring systems are limited by interobserver variability.
This study aimed to demonstrate objective assessment of bowel preparation adequacy
using an artificial intelligence (AI)/convolutional neural network (CNN) algorithm
developed from colonoscopy videos.
Methods
Two CNNs were developed using a training set of 73,304 images from 200 colonoscopies.
First, a binary CNN was developed and trained to distinguish video frames that were
appropriate versus inappropriate for scoring with the Boston Bowel Preparation Scale
(BBPS). A second multiclass CNN was developed and trained on 26,950 appropriate frames
that were expertly annotated with BBPS segment scores (0-3). We validated the algorithm
using 252 10-second video clips that were assigned BBPS segment scores by 2 experts.
The algorithm provided mean BBPS scores based on the algorithm (AI-BBPS) by calculating
mean BBPS based on each frame’s scoring. We maximized the algorithm’s performance
by choosing a dichotomized AI-BBPS score that closely matched dichotomized BBPS scores
(ie, adequate vs inadequate). We tested the mean BBPS score based on the algorithm
AI-BBPS against human rating using 30 independent 10-second video clips (test set
1) and 10 full withdrawal colonoscopy videos (test set 2).
Results
In the validation set, the algorithm demonstrated an area under the curve of .918
and accuracy of 85.3% for detection of inadequate bowel cleanliness. In test set 1,
sensitivity for inadequate bowel preparation was 100% and agreement between raters
and AI was 76.7% to 83.3%. In test set 2, sensitivity for inadequate bowel preparation
for each segment was 100% and agreement between raters and AI was 68.9% to 89.7%.
Agreement between raters alone versus raters and AI were similar (κ = .694 and .649,
respectively).
Conclusions
The algorithm assessment of bowel cleanliness as measured with the BBPS showed good
performance and agreement with experts including full withdrawal colonoscopies.
Graphical abstract
Graphical Abstract
Abbreviations:
AI (artificial intelligence), AI-BBPS (mean BBPS score based on the algorithm), AUC (area under the receiver operating characteristic curve), BBPS (Boston bowel preparation scale), CNN (convolutional neural network), dAI-BBPS (dichotomized AI-BBPS)To read this article in full you will need to make a payment
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Article info
Publication history
Published online: December 08, 2021
Accepted:
November 22,
2021
Received:
October 18,
2021
Footnotes
DISCLOSURE: The following authors disclosed financial relationships: A. H. Calderwood: Advisor for Dark Canyon Labs. W. Karnes: Cofounder and chief medical officer of Docbot. J. Requa: Employee of Docbot. B. C. Jacobson: Consultant for Motus GI. M. B. Wallace: Consultant for Verily; research support from Cosmo and Medtronic. All other authors disclosed no financial relationships.
If you would like to chat with an author of this article, you may contact Dr Wallace at [email protected]
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