CCRA: A colon cleanliness rating algorithm based on colonoscopy video analysis.

Yu Bo, Shao Wei, Yao Dengju, Wang Yunhao, Zhang Heyi

Abstract read

In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

Multivariate Gaussian Bayes classifier with limited data for segmentation of clean and contaminated regions in the small bowel capsule endoscopy images.PloS one · 2025
Article

4 · The record

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PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Yu BoSchool of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China.

Shao WeiSchool of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China.

Yao DengjuSchool of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China.

Wang YunhaoSchool of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China.

Zhang HeyiSchool of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: A Colon Cleanliness Rating Algorithm (CCRA) based on colonoscopy image analysis is proposed in this paper, in order to solve the problem that the results of Colon Cleanliness (or Bowel Preparation Quality) rating caused by manual inspection are inconsistent. Methods: Firstly, CCRA intercepts images from the colonoscopy video. Secondly, each colonoscopy image's stool area is segmented by U-Net to obtain the 2-classification segmentation results. Finally, the colon cleanliness is obtained by comparing the average area of the stool area with the standard proportion. Results: After testing, the pixel accuracy of the U-Net model is 97.02 %, IoU is 83.67 %, accuracy is 92.17 %, recall is 90.21 %, F1-Score is 90.95 %. The accuracy of CCRA is 92.45 %-99.275. Conclusion: The experimental results show that the CCRA proposed in this paper can quickly and accurately output the colon cleanliness rating of patients without manpower.

Indexed as

Bowel preparation qualityColon cleanlinessDeep learningU-net

Identifiers

PMID38034702

DOI10.1016/j.heliyon.2023.e22662

PMCPMC10687275

What OpenQuestion holds

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LicenceCC BY-NC-ND

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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.