Evidence map›Paper›PMID 34447874›Full record

ArticleEndoscopy international open2021

Artificial intelligence and colon capsule endoscopy: automatic detection of blood in colon capsule endoscopy using a convolutional neural network.

Miguel Mascarenhas Saraiva, João P S Ferreira, Hélder Cardoso, João Afonso, Tiago Ribeiro, Patrícia Andrade, Marco P L Parente, Renato N Jorge, Guilherme Macedo

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In one paragraph

Article in Endoscopy international open, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
10citing papers in PubMed, 1 pooled it
–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

10 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Review
  4. Review
  5. Review
  6. Article
  7. Review
  8. Review
  9. Advances in colon capsule endoscopy: a review of current applications and challenges.Frontiers in gastroenterology (Lausanne, Switzerland) · 2023
    Review
  10. Review
4 · The record

Corrections and comments

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

9 authors.

Miguel Mascarenhas SaraivaDepartment of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, Porto, Portugal.
João P S FerreiraDepartment of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal.
Hélder CardosoDepartment of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, Porto, Portugal.
João AfonsoDepartment of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, Porto, Portugal.
Tiago RibeiroDepartment of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, Porto, Portugal.
Patrícia AndradeDepartment of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, Porto, Portugal.
Marco P L ParenteDepartment of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal.
Renato N JorgeDepartment of Mechanical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal.
Guilherme MacedoDepartment of Gastroenterology, São João University Hospital, Alameda Professor Hernâni Monteiro, Porto, Portugal.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Colon capsule endoscopy (CCE) is a minimally invasive alternative to conventional colonoscopy. Most studies on CCE focus on colorectal neoplasia detection. The development of automated tools may address some of the limitations of this diagnostic tool and widen its indications for different clinical settings. We developed an artificial intelligence model based on a convolutional neural network (CNN) for the automatic detection of blood content in CCE images. Training and validation datasets were constructed for the development and testing of the CNN. The CNN detected blood with a sensitivity, specificity, and positive and negative predictive values of 99.8 %, 93.2 %, 93.8 %, and 99.8 %, respectively. The area under the receiver operating characteristic curve for blood detection was 1.00. We developed a deep learning algorithm capable of accurately detecting blood or hematic residues within the lumen of the colon based on colon CCE images.

Identifiers

PMID34447874
PMCPMC8383083

What OpenQuestion holds

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Registered trials

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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.