Evidence map›Paper›PMID 38764765›Full record

ArticleWorld journal of gastroenterology2024

Artificial intelligence in detection of small bowel lesions and their bleeding risk: A new step forward.

Silvia Cocca, Giuseppina Pontillo, Giuseppe Grande, Rita Conigliaro

Abstract readLetter
PubMed Full text at PMC Publisher
In one paragraph

Article in World journal of gastroenterology, 2024. 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.

  1. 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

4 authors.

Silvia CoccaGastroenterology and Endoscopy Unit, Azienda Ospedaliero Universitaria Policlinico di Modena, Modena 41121, Italy. silvia.cocca@gmail.com.
Giuseppina PontilloGastroenterology and Endoscopy Unit, Presidio Ospedaliero San Giuseppe Moscati (Aversa, CE) - ASL Caserta, Caserta 81100, Italy.
Giuseppe GrandeDepartment of Gastroenterology and Digestive Endoscopy, Azienda Ospedaliero Universitaria di Modena, Modena 41121, Italy.
Rita ConigliaroGastroenterology and Endoscopy Unit, Azienda Ospedaliero Universitaria Policlinico di Modena, Modena 41121, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The present letter to the editor is related to the study with the title "Automatic detection of small bowel (SB) lesions with different bleeding risk based on deep learning models". Capsule endoscopy (CE) is the main tool to assess SB diseases but it is a time-consuming procedure with a significant error rate. The development of artificial intelligence (AI) in CE could simplify physicians' tasks. The novel deep learning model by Zhang

Indexed as

Artificial IntelligenceCapsule EndoscopyDeep LearningGastrointestinal HemorrhageIntestine, SmallHumansRisk AssessmentArtificial intelligenceBleeding riskCapsule endoscopySmall bowelVascular lesions

Identifiers

PMID38764765
PMCPMC11099393

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

None linked

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.