Evidence map›Paper›PMID 38418774›Full record

SynthesisIndian journal of gastroenterology : official journal of the Indian Society of Gastroenterology2024

Artificial intelligence in endoscopy related to inflammatory bowel disease: A systematic review.

Partha Pal, Kanapuram Pooja, Zaheer Nabi, Rajesh Gupta, Manu Tandan, Guduru Venkat Rao, Nageshwar Reddy

Abstract readSystematic Review
PubMed Publisher
In one paragraph

Synthesis in Indian journal of gastroenterology : official journal of the Indian Society of Gastroenterology, 2024. 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
8.8field-weighted citation impact, top 2% 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, 19 citations in OpenAlex.

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

7 authors at 1 institution in 1 country.

Partha PalMedical Gastroenterology, Asian Institute of Gastroenterology, Somajiguda, Hyderabad, 500 082, India. partha0123456789@gmail.com.ORCID 0000-0002-7090-9004
Kanapuram PoojaMedical Gastroenterology, Asian Institute of Gastroenterology, Somajiguda, Hyderabad, 500 082, India.
Zaheer NabiMedical Gastroenterology, Asian Institute of Gastroenterology, Somajiguda, Hyderabad, 500 082, India.
Rajesh GuptaMedical Gastroenterology, Asian Institute of Gastroenterology, Somajiguda, Hyderabad, 500 082, India.
Manu TandanMedical Gastroenterology, Asian Institute of Gastroenterology, Somajiguda, Hyderabad, 500 082, India.
Guduru Venkat RaoSurgical Gastroenterology, Asian Institute of Gastroenterology, Hyderabad 500 082, India.
Nageshwar ReddyMedical Gastroenterology, Asian Institute of Gastroenterology, Somajiguda, Hyderabad, 500 082, India.
Asian Institute of Gastroenterology · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

BACKGROUND AND

objectivesIn spite of rapid growth of artificial intelligence (AI) in digestive endoscopy in lesion detection and characterization, the role of AI in inflammatory bowel disease (IBD) endoscopy is not clearly defined. We aimed at systematically reviewing the role of AI in IBD endoscopy and identifying future research areas.

methodsWe searched the PubMed and Embase database using keywords ("artificial intelligence" OR "machine learning" OR "computer-aided" OR "convolutional neural network") AND ("inflammatory bowel disease" OR "ulcerative colitis" OR "Crohn's") AND ("endoscopy" or "colonoscopy" or "capsule endoscopy" or "device assisted enteroscopy") between 1975 and September 2023 and identified 62 original articles for detailed review. Review articles, consensus guidelines, case reports/series, editorials, letter to the editor, non-peer-reviewed pre-prints and conference abstracts were excluded. The quality of the included studies was assessed using the MI-CLAIM checklist.

resultsThe accuracy of AI models (25 studies) to assess ulcerative colitis (UC) endoscopic activity ranged between 86.54% and 94.5%. AI-assisted capsule endoscopy reading (12 studies) substantially reduced analyzable images and reading time with excellent accuracy (90.5% to 99.9%). AI-assisted analysis of colonoscopic images can help differentiate IBD from non-IBD, UC from non-UC and UC from Crohn's disease (CD) (three studies) with 72.1%, 98.3% and > 90% accuracy, respectively. AI models based on non-invasive clinical and radiologic parameters could predict endoscopic activity (three studies). AI-assisted virtual chromoendoscopy (four studies) could predict histologic remission and long-term outcomes. Computer-assisted detection (CADe) of dysplasia (two studies) is feasible along with AI-based differentiation of high from low-grade IBD neoplasia (79% accuracy). AI is effective in linking electronic medical record data (two studies) with colonoscopic videos to facilitate widespread machine learning.

conclusionAI-assisted IBD endoscopy has the potential to impact clinical management by automated detection and characterization of endoscopic lesions. Large, multi-center, prospective studies and commercially available IBD-specific endoscopic AI algorithms are warranted.

Indexed as

Capsule EndoscopyColitis, UlcerativeCrohn DiseaseInflammatory Bowel DiseasesArtificial IntelligenceColonoscopyHumansProspective StudiesArtificial intelligenceConvolutional neural networkCrohn’s diseaseEndoscopyInflammatory bowel diseaseMachine learningUlcerative colitis

Identifiers

PMID38418774
OpenAlexW4392243792

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.