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NCT05151939

Endoscopic Ultrasound (EUS) Artificial Intelligence Model for Normal Mediastinal and Abdominal Strictures Assessment

Status unknown Last updated 30 December 2021
What this trial tests

trial testing Identification or discharge visualization of mediastinal and abdominal organ/anatomic strictures through Endoscopic ultrasound (EUS) videos by an expert endoscopist in Abdomen in 60 participants. Status unknown.

Timeline
1 October 2021
Primary endpoint
30 March 2022
30 June 2022

Quick facts

Lead sponsorInstituto Ecuatoriano de Enfermedades Digestivas
StatusStatus unknown
Study typeOBSERVATIONAL
Enrollment60
Start date1 October 2021
Primary completion30 March 2022
Estimated completion30 June 2022
Sites1 location across Ecuador

Drugs / interventions tested

Conditions studied

Sponsor

Instituto Ecuatoriano de Enfermedades Digestivas — full company profile →

Who can join

Adults 18 to 79, any sex, with Abdomen or Mediastinum. Patients with the condition only — healthy volunteers not accepted.

Sponsor's own description

Therefore, a high number of procedures is necessary to achieve EUS competency, but interobserver agreement still varies widely. Artificial intelligence (AI) aided recognition of anatomical structures may improve the training process and inter-observer agreement. Robles-Medranda et al. developed an AI model that recognizes normal anatomical structures during linear and radial EUS evaluations. We pursue to design an external validation of our developed AI model, considering an endoscopist expert as the gold standard.

Publications & conference data

2 peer-reviewed publications reference this trial (live from Europe PMC):

  1. Research progress and hotspot of the artificial intelligence application in the ultrasound during 2011-2021: A bibliometric analysis.
    Xia D, Chen G, Wu K, Yu M, et al · · 2022 · cited 7× · PMID 36187670 · DOI 10.3389/fpubh.2022.990708
  2. Artificial intelligence in abdominal and pelvic ultrasound imaging: current applications.
    Cai L, Pfob A. · · 2025 · cited 6× · PMID 39487919 · DOI 10.1007/s00261-024-04640-x

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