Evidence map›Paper›PMID 36832189›Full record

ArticleDiagnostics (Basel, Switzerland)2023

The Role of an Artificial Intelligence Method of Improving the Diagnosis of Neoplasms by Colonoscopy.

Ilona Vilkoite, Ivars Tolmanis, Hosams Abu Meri, Inese Polaka, Linda Mezmale, Linda Anarkulova, Marcis Leja, Aivars Lejnieks

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
8citing papers in PubMed, 3 pooled it
3.5field-weighted citation impact, top 7% of its field
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

8 citing papers in PubMed, 3 syntheses or guidelines pooled it, 15 citations in OpenAlex.

  1. Pooled it
  2. Pooled it
  3. Pooled it
  4. Article
  5. Article
  6. Review
  7. Article
  8. Article
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

8 authors at 5 institutions in 1 country.

Ilona VilkoiteHealth Centre 4, LV-1012 Riga, Latvia.
Ivars TolmanisDigestive Diseases Center GASTRO, LV-1079 Riga, Latvia.ORCID 0000-0003-0783-6460
Hosams Abu MeriHealth Centre 4, LV-1012 Riga, Latvia.
Inese PolakaInstitute of Clinical and Preventive Medicine, University of Latvia, LV-1586 Riga, Latvia.ORCID 0000-0002-9892-7765
Linda MezmaleInstitute of Clinical and Preventive Medicine, University of Latvia, LV-1586 Riga, Latvia.ORCID 0000-0001-7765-852X
Linda AnarkulovaInstitute of Clinical and Preventive Medicine, University of Latvia, LV-1586 Riga, Latvia.ORCID 0000-0003-0035-416X
Marcis LejaDigestive Diseases Center GASTRO, LV-1079 Riga, Latvia.ORCID 0000-0002-0319-8855
Aivars LejnieksDepartment of Internal Diseases, Riga Stradins University, LV-1007 Riga, Latvia.ORCID 0000-0002-2097-6698
Riga East University Hospital · LVRiga Stradiņš University · LVLatvian Environment, Geology and Meteorology Centre (Latvia) · LVUniversity of Latvia · LVUniversity of Liepāja · LV

Funding

European Regional Development Fund Nr. 1.1.1.1/20/A/035
6 · The paper itself

Abstract

backgroundColorectal cancer (CRC) is the third most common cancer worldwide. Colonoscopy is the gold standard examination that reduces the morbidity and mortality of CRC. Artificial intelligence (AI) could be useful in reducing the errors of the specialist and in drawing attention to the suspicious area.

methodsA prospective single-center randomized controlled study was conducted in an outpatient endoscopy unit with the aim of evaluating the usefulness of AI-assisted colonoscopy in PDR and ADR during the day time. It is important to understand how already available CADe systems improve the detection of polyps and adenomas in order to make a decision about their routine use in practice. In the period from October 2021 to February 2022, 400 examinations (patients) were included in the study. One hundred and ninety-four patients were examined using the ENDO-AID CADe artificial intelligence device (study group), and 206 patients were examined without the artificial intelligence (control group).

resultsNone of the analyzed indicators (PDR and ADR during morning and afternoon colonoscopies) showed differences between the study and control groups. There was an increase in PDR during afternoon colonoscopies, as well as ADR during morning and afternoon colonoscopies.

conclusionsBased on our results, the use of AI systems in colonoscopies is recommended, especially in circumstances of an increase of examinations. Additional studies with larger groups of patients at night are needed to confirm the already available data.

Indexed as

adenomaartificial intelligencecolonoscopycolorectal cancerpolyps

Identifiers

PMID36832189
PMCPMC9955100
OpenAlexW4320497350

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.