Evidence map›Paper›PMID 38903657›Full record

ArticleCrohn's & colitis 3602024

Radiomics-Based Analysis of Intestinal Ultrasound Images for Inflammatory Bowel Disease: A Feasibility Study.

Phillip Gu, Jui-Hsuan Chang, Dan Carter, Dermot P B McGovern, Jason Moore, Paul Wang, Xiuzhen Huang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Crohn's & colitis 360, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed, 1 pooled it
–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

13 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. The Emerging Role of Ultrasonography in Ulcerative Colitis.Diagnostics (Basel, Switzerland) · 2026
    Review
  3. Review
  4. Artificial intelligence in the management of inflammatory bowel disease: What's next?World journal of gastrointestinal pharmacology and therapeutics · 2026
    Review
  5. Review
  6. Review
  7. Review
  8. Review
  9. Review
  10. Review
  11. Review
  12. Review
  13. 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.

Phillip GuF. Widjaja Inflammatory Bowel Disease Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.ORCID https://orcid.org/0000-0001-8158-6557
Jui-Hsuan ChangDepartment of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Dan CarterDepartment of Gastroenterology, Sheba Medical Center, Tel Aviv, Israel.ORCID https://orcid.org/0000-0001-7979-6803
Dermot P B McGovernF. Widjaja Inflammatory Bowel Disease Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Jason MooreDepartment of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Paul WangDepartment of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.
Xiuzhen HuangDepartment of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The increasing adoption of intestinal ultrasound ( Methods: Retrospectively analyzing IUS images obtained during routine outpatient visits, we developed and tested radiomic-based and CNN-based models to distinguish between normal and abnormal images, with abnormal images defined as bowel wall thickness > 3 mm or bowel hyperemia with modified Limberg score ≥ 1 (both are surrogate markers for inflammation). Model performances were measured by area under the receiver operator curve ( Results: For this feasibility study, 125 images (33% abnormal) were analyzed. A radiomic-based model using XG boost yielded the best classifier model with average test AUC 0.98%, 93.8% sensitivity, 93.8% specificity, and 93.7% accuracy. The CNN-based classification model yielded an average testing AUC of 0.75. Conclusions: Radiomic analysis of IUS images is feasible, and a radiomic-based classification model could accurately differentiate abnormal from normal images. Our findings establish methods to facilitate future radiomic-based IUS studies that can help standardize image interpretation and expand IUS research capabilities.

Indexed as

artificial intelligenceconvolutional neural networkinflammatory bowel diseaseintestinal ultrasoundradiomics

Identifiers

PMID38903657
PMCPMC11187771

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.