Evidence map›Paper›PMID 37663113›Full record

ArticleWorld journal of gastrointestinal endoscopy2023

Endoscopic ultrasound artificial intelligence-assisted for prediction of gastrointestinal stromal tumors diagnosis: A systematic review and meta-analysis.

Rômulo Sérgio Araújo Gomes, Guilherme Henrique Peixoto de Oliveira, Diogo Turiani Hourneaux de Moura, Ana Paula Samy Tanaka Kotinda, Carolina Ogawa Matsubayashi, Bruno Salomão Hirsch, Matheus de Oliveira Veras, João Guilherme Ribeiro Jordão Sasso, Roberto Paolo Trasolini, Wanderley Marques Bernardo and 1 more

2 registry-linked trialsOpen access · diamondAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in World journal of gastrointestinal endoscopy, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 7 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed, 1 pooled it
2.2field-weighted citation impact, top 13% 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.

NCT07078136 recruitingnot on this mapstarted 2025, after this paper: background citation

Multicenter Observational Study of a Multimodal AI Model Using EUS, White-Light Endoscopy, and Clinical Data for Diagnosis of Upper GI Mesenchymal Tumors and Risk Stratification of Gastric GISTs

TypeobservationalSponsorHuazhong University of Science and TechnologyRan2025 to 2026Enrolled130ConditionsSubmucosal Tumor, Gastrointestinal Stromal Tumor (GIST), Leiomyoma, SchwannomaArmsMultimodal AI Model, Expert Endoscopist Assessment
NCT07106411 not yet recruitingnot on this mapstarted 2025, after this paper: background citation

Contrast-Enhanced Harmonic Endoscopic Ultrasound for the Differential Diagnosis Between Gastrointestinal Stromal Tumors and Leiomyomas: A Multicenter Prospective Self-Controlled Study

TypeobservationalSponsorHuazhong University of Science and TechnologyRan2025 to 2026Enrolled288ConditionsSubmucosal Tumor, Gastrointestinal Stromal Tumor (GIST), LeiomyomaArmsEndoscopic Ultrasound, Contrast-Enhanced Harmonic Endoscopic Ultrasound (CEH-EUS)
3 · Its place in the literature

Who cites it

7 citing papers in PubMed, 1 synthesis or guideline pooled it, 8 citations in OpenAlex.

  1. Pooled it
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  3. Article
  4. Review
  5. Review
  6. Review
  7. 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

11 authors at 2 institutions in 2 countries.

Rômulo Sérgio Araújo GomesDepartment of Gastroenterology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo 05403-010, Brazil.
Guilherme Henrique Peixoto de OliveiraDepartment of Gastroenterology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo 05403-010, Brazil.
Diogo Turiani Hourneaux de MouraDepartment of Gastroenterology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo 05403-010, Brazil.
Ana Paula Samy Tanaka KotindaDepartment of Gastroenterology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo 05403-010, Brazil.
Carolina Ogawa MatsubayashiDepartment of Gastroenterology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo 05403-010, Brazil.
Bruno Salomão HirschDepartment of Gastroenterology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo 05403-010, Brazil.
Matheus de Oliveira VerasDepartment of Gastroenterology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo 05403-010, Brazil.
João Guilherme Ribeiro Jordão SassoDepartment of Gastroenterology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo 05403-010, Brazil. dr.guilhermehpoliveira@gmail.com.
Roberto Paolo TrasoliniDivision of Hepatology and Endoscopy, Department of Gastroenterology, Harvard Medical School, Brigham and Women's Hospital, Boston, MA 02115, United States.
Wanderley Marques BernardoDepartment of Gastroenterology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo 05403-010, Brazil.
Eduardo Guimarães Hourneaux de MouraDepartment of Gastroenterology, Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo, São Paulo 05403-010, Brazil.
Hospital das Clínicas da Faculdade de Medicina da Universidade de São Paulo · BRBrigham and Women's Hospital · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundSubepithelial lesions (SELs) are gastrointestinal tumors with heterogeneous malignant potential. Endoscopic ultrasonography (EUS) is the leading method for evaluation, but without histopathological analysis, precise differentiation of SEL risk is limited. Artificial intelligence (AI) is a promising aid for the diagnosis of gastrointestinal lesions in the absence of histopathology.

aimTo determine the diagnostic accuracy of AI-assisted EUS in diagnosing SELs, especially lesions originating from the muscularis propria layer.

methodsElectronic databases including PubMed, EMBASE, and Cochrane Library were searched. Patients of any sex and > 18 years, with SELs assessed by EUS AI-assisted, with previous histopathological diagnosis, and presented sufficient data values which were extracted to construct a 2 × 2 table. The reference standard was histopathology. The primary outcome was the accuracy of AI for gastrointestinal stromal tumor (GIST). Secondary outcomes were AI-assisted EUS diagnosis for GIST

resultsEight retrospective studies with a total of 2355 patients and 44154 images were included in this meta-analysis. The AI-assisted EUS for GIST diagnosis showed a sensitivity of 92% [95% confidence interval (CI): 0.89-0.95;

conclusionAI-assisted EUS has high diagnostic accuracy for fourth-layer SELs, especially for GIST, demonstrating superiority compared to experienced endoscopists' and improving their diagnostic performance in the absence of invasive procedures.

Indexed as

Artificial intelligenceSubepithelial lesionsUltrasound endoscopy

Identifiers

PMID37663113
PMCPMC10473903
OpenAlexW4385686855

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
Read underepoch 390

Registered trials

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.