ArticleEndoscopy international open2018
Development of a computed cleansing score to assess quality of bowel preparation in colon capsule endoscopy.
Article in Endoscopy international open, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed, 3 citations in OpenAlex.
- Expected Values of Artificial Intelligence in Colon Capsule Endoscopy: Artificial Intelligence in Capsule Endoscopy Consortium Position Statement.Endoscopy international open · 2026Article
- AI-Assisted Evaluation of Colon Cleanliness in Capsule Endoscopy Videos.Diagnostics (Basel, Switzerland) · 2025Article
- Article
- Bowel cleansing quality evaluation in colon capsule endoscopy: what is the reference standard?Therapeutic advances in gastroenterology · 2024Article
- Clinicians' Guide to Artificial Intelligence in Colon Capsule Endoscopy-Technology Made Simple.Diagnostics (Basel, Switzerland) · 2023Review
- Artificial Intelligence in Colon Capsule Endoscopy-A Systematic Review.Diagnostics (Basel, Switzerland) · 2022Review
- New Generation Express View: An Artificial Intelligence Software Effectively Reduces Capsule Endoscopy Reading Times.Diagnostics (Basel, Switzerland) · 2022Article
- Lavage, Simethicone, and Prokinetics-What to Swallow with a Video Capsule.Diagnostics (Basel, Switzerland) · 2021Review
- Role of Artificial Intelligence in Video Capsule Endoscopy.Diagnostics (Basel, Switzerland) · 2021Review
- Colon capsule endoscopy is a viable alternative to colonoscopy for the investigation of intermediate- and low-risk patients with gastrointestinal symptoms: results of a pilot study.Endoscopy international open · 2021Article
- Scoring Systems for Clinical Colon Capsule Endoscopy-All You Need to Know.Journal of clinical medicine · 2021Review
- CAD-CAP: a 25,000-image database serving the development of artificial intelligence for capsule endoscopy.Endoscopy international open · 2020Article
- Image database, AI and capsule endoscopy; the bets are on.Endoscopy international open · 2020Article
Corrections and comments
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Authors and funding
12 authors at 3 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND AND STUDY
aimsColon capsule endoscopy (CCE) does not possess an objective and reliable scoring system to assess the quality of visualization of the colon mucosa. The aim of this study was to establish a colonic computed assessment of cleansing (CAC) score able to discriminate "adequately cleansed" from "inadequately cleansed" CCE still frames. PATIENTS AND
methodsTwelve normal and complete CCEs, using the Pillcam Colon 2 system (Medtronic, Minnesota, United States), were prospectively selected amongst a database. A CAC score, defined as the ratio of color intensities red over green (R/G ratio), and red over brown (R/(R + G) ratio) was calculated for each extracted colonic frame. After sorting and random selection, two sets of still frames representative of the range of these ratios were obtained. These images were analyzed twice in random order by two experienced CCE readers who were blinded to the CAC scores. A receiver operating characteristic (ROC) curve was forged for both types of ratios and a threshold established, yielding the highest diagnostic performance in terms of adequate cleansing assessment.
resultsFour-hundred-and-eight frames were extracted. Regarding the R/G ratio, a threshold value of 1.55 was calculated, with a sensitivity of 86.5 % and a specificity of 77.7 %. Regarding the R/(R + G) ratio, a threshold value of 0.58 was calculated with a sensitivity of 95.5 % and a specificity of 62.9 %.
conclusionThe two proposed CAC scores based on the ratio of color intensities come with high sensitivities for discriminating between "adequately cleansed" and "inadequately cleansed" CCE still frames, but they lack specificity. Further refinement, with implementation of additional image parameters, is warranted.
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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.