Evidence map›Paper›PMID 34805583›Full record

ReviewTranslational gastroenterology and hepatology2021

Enhancing polyp detection: technological advances in colonoscopy imaging.

Antonio Lee, Nicholas Tutticci

Abstract readReview
In one paragraph

Review in Translational gastroenterology and hepatology, 2021. 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
–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

4 citing papers in PubMed.

  1. Trial
  2. Trial
  3. Review
  4. History of colonoscopy and technological advances: a narrative review.Translational gastroenterology and hepatology · 2023
    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

2 authors.

Antonio LeeEndoscopy Unit, Queen Elizabeth II Jubilee Hospital, Brisbane, Australia.
Nicholas TutticciEndoscopy Unit, Queen Elizabeth II Jubilee Hospital, Brisbane, Australia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The detection and removal of polyps at colonoscopy is core to the current colorectal cancer (CRC) prevention strategy. However, colonoscopy is flawed with a well described miss rate and variability in detection rates associated with incomplete protection from CRC. Consequently, there is significant interest in techniques and technologies which increase polyp detection with the aim to remedy colonoscopy's ills. Technologic advances in colonoscope imaging are numerous and include; increased definition of imaging, widening field of view, virtual technologies to supplant conventional chromocolonoscopy (CC) and now computer assisted detection. However, despite nearly two decades of technologic advances, data on gains in detection from individual technologies have been modest at best and heterogenous and conflicted as a rule. This state of detection technology science is exacerbated by use of relatively blunt metrics of improvement without consensus, the myopic search for gains over single generations of technology improvement and an unhealthy focus on adenomatous lesions. Yet there remains cause for optimism as detection gains from new technology, while small, may still improve CRC prevention. The technologies are also readily available in current generation colonoscopes and have roles beyond simply detection such as lesion characterization, further improving their worth. Coupled with the imminent expansion of computer assisted detection the detection future from colonoscope imaging advances looks bright. This review aims to cover the major imaging advances and evidence for improvement in polyp detection.

Indexed as

advancesAIColonoscopydetectionhigh definition (HD)imagingpolypvirtual chromoendoscopy

Identifiers

PMID34805583
PMCPMC8573375

What OpenQuestion holds

Textmetadata
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.