ArticleiGIE : innovation, investigation and insights2023
Interobserver agreement between an artificial intelligence algorithm and colon capsule endoscopy readers on bowel-cleansing quality.
Article in iGIE : innovation, investigation and insights, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
4 citing papers in PubMed.
- Re-investigation rate following colon capsule endoscopy stratified by faecal haemoglobin concentration in a screening population.Therapeutic advances in gastroenterology · 2026Article
- Article
- Towards full integration of explainable artificial intelligence in colon capsule endoscopy's pathway.Scientific reports · 2025Article
- Bowel cleansing quality evaluation in colon capsule endoscopy: what is the reference standard?Therapeutic advances in gastroenterology · 2024Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background and Aims: Colon capsule endoscopy (CCE) faces substantial challenges, one of which is achieving adequate colon cleansing. Furthermore, the interobserver agreement on bowel-cleansing quality varies. To address this issue, we developed an artificial intelligence algorithm (AIA) to evaluate bowel-cleansing quality. The aim of this study was to estimate the interobserver agreement on bowel cleansing between a group of experienced CCE readers and an AIA and to examine whether percentiles of the overall bowel-cleansing quality are a suitable way of reporting the results generated by the AIA. Methods: Bowel-cleansing quality in 842 CCE investigations was scored on both a 2- and 4-point grading scale for the entire colon and by segment by experienced CCE readers and the AIA. For the algorithm, a score was given based on the mean score, median, upper and lower quartiles, and second and 98th percentiles. The level of agreement was evaluated using Cohen's κ. Results: The interobserver agreement between the CCE readers and AIA on bowel-cleansing quality was minimal to none for the overall bowel evaluation, by segment, and on the 2- and 4- point grading scale regardless of the threshold for the AIA score. Conclusions: We found minimal agreement on evaluation of bowel-cleansing quality in CCE between CCE readers and the AIA. Mean or percentiles of the AIA grading did not seem suitable for AI-generated bowel-cleansing evaluation.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.