Evidence map›Paper›PMID 38136403›Full record

ReviewCancers2023

The Future of Minimally Invasive Capsule Panendoscopy: Robotic Precision, Wireless Imaging and AI-Driven Insights.

Miguel Mascarenhas, Miguel Martins, João Afonso, Tiago Ribeiro, Pedro Cardoso, Francisco Mendes, Patrícia Andrade, Helder Cardoso, João Ferreira, Guilherme Macedo

Abstract readReview
In one paragraph

Review in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Article
  5. Article
  6. Article
  7. Review
  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

10 authors.

Miguel MascarenhasPrecision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal.ORCID 0000-0002-0340-0830
Miguel MartinsPrecision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal.ORCID 0000-0002-0484-4804
João AfonsoPrecision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal.
Tiago RibeiroPrecision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal.
Pedro CardosoPrecision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal.ORCID 0000-0001-9427-5635
Francisco MendesPrecision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal.ORCID 0000-0002-5890-7049
Patrícia AndradePrecision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal.
Helder CardosoPrecision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal.
João FerreiraDepartment of Mechanic Engineering, Faculty of Engineering, University of Porto, 4200-065 Porto, Portugal.
Guilherme MacedoPrecision Medicine Unit, Department of Gastroenterology, São João University Hospital, 4200-427 Porto, Portugal.ORCID 0000-0002-9387-9872

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the early 2000s, the introduction of single-camera wireless capsule endoscopy (CE) redefined small bowel study. Progress continued with the development of double-camera devices, first for the colon and rectum, and then, for panenteric assessment. Advancements continued with magnetic capsule endoscopy (MCE), particularly when assisted by a robotic arm, designed to enhance gastric evaluation. Indeed, as CE provides full visualization of the entire gastrointestinal (GI) tract, a minimally invasive capsule panendoscopy (CPE) could be a feasible alternative, despite its time-consuming nature and learning curve, assuming appropriate bowel cleansing has been carried out. Recent progress in artificial intelligence (AI), particularly in the development of convolutional neural networks (CNN) for CE auxiliary reading (detecting and diagnosing), may provide the missing link in fulfilling the goal of establishing the use of panendoscopy, although prospective studies are still needed to validate these models in actual clinical scenarios. Recent CE advancements will be discussed, focusing on the current evidence on CNN developments, and their real-life implementation potential and associated ethical challenges.

Indexed as

artificial intelligencebioethicscapsule endoscopygreen endoscopypanendoscopy

Identifiers

PMID38136403
PMCPMC10742312

What Socratic 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 Socratic graph.