Evidence map›Paper›PMID 33384546›Full record

ReviewWorld journal of gastroenterology2020

Artificial intelligence-aided colonoscopy: Recent developments and future perspectives.

Giulio Antonelli, Paraskevas Gkolfakis, Georgios Tziatzios, Ioannis S Papanikolaou, Konstantinos Triantafyllou, Cesare Hassan

Open access · hybridAbstract readReview
PubMed Full text at PMC Publisher
In one paragraph

Review in World journal of gastroenterology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
1.3field-weighted citation impact, top 17% 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

4 citing papers in PubMed, 18 citations in OpenAlex.

  1. Review
  2. Review
  3. Review
  4. 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

6 authors at 2 institutions in 2 countries.

Giulio AntonelliGastroenterology Unit, Nuovo Regina Margherita Hospital, Rome 00153, Italy.
Paraskevas GkolfakisDepartment of Gastroenterology Hepatopancreatology and Digestive Oncology, Erasme University Hospital, Université Libre de Bruxelles, Brussels 1070, Belgium. pgkolfakis@med.uoa.gr.
Georgios TziatziosHepatogastroenterology Unit, Second Department of Internal Medicine - Propaedeutic, Medical School, National and Kapodistrian University of Athens, ''Attikon" University General Hospital, Athens 12462, Greece.
Ioannis S PapanikolaouHepatogastroenterology Unit, Second Department of Internal Medicine - Propaedeutic, Medical School, National and Kapodistrian University of Athens, ''Attikon" University General Hospital, Athens 12462, Greece.
Konstantinos TriantafyllouHepatogastroenterology Unit, Second Department of Internal Medicine - Propaedeutic, Medical School, National and Kapodistrian University of Athens, ''Attikon" University General Hospital, Athens 12462, Greece.
Cesare HassanGastroenterology Unit, Nuovo Regina Margherita Hospital, Rome 00153, Italy.
National and Kapodistrian University of Athens · GROspedale Regina Margherita · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Artificial intelligence (AI) systems, especially after the successful application of Convolutional Neural Networks, are revolutionizing modern medicine. Gastrointestinal Endoscopy has shown to be a fertile terrain for the development of AI systems aiming to aid endoscopists in various aspects of their daily activity. Lesion detection can be one of the two main aspects in which AI can increase diagnostic yield and abilities of endoscopists. In colonoscopy, it is well known that a substantial rate of missed neoplasia is still present, representing the major cause of interval cancer. In addition, an extremely high variability in adenoma detection rate, the main key quality indicator in colonoscopy, has been extensively reported. The other domain in which AI is believed to have a considerable impact on everyday clinical practice is lesion characterization and aid in "optical diagnosis". By predicting

Indexed as

AdenomaColonic PolypsColorectal NeoplasmsArtificial IntelligenceColonoscopyHumansAdenomaArtificial intelligenceCharacterizationColonoscopyDetectionPolyp

Identifiers

PMID33384546
PMCPMC7754556
OpenAlexW3118222515

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.