Artificial Intelligence vs Endoscopist Identification in EUS Normal Anatomy

Comparative Evaluation of Artificial Intelligence and Endoscopists´ Accuracy in Endoscopic Ultrasound for Identifying Normal Anatomical Structures: A Multi-institutional, Cross-sectional Study

Status

Completed

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT06279546

Enrollment

Registered

2024-02-28

Start date

2023-05-01

Completion date

2024-01-26

Last updated

2024-02-28

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Gastrointestinal Diseases

Keywords

EUS, AI, Gastrointestinal

Brief summary

Endoscopic ultrasound (EUS) visual impression is operator-dependant and can hinder diagnostic accuracy, especially in less experienced endoscopists. The implementation of artificial intelligence can potentially mitigate operator dependency and interpretation variability, helping or improving the overall accuracy. The investigators therefore aim to compare diagnostic accuracy between artificial intelligence (AI)-based model and the endoscopists when identifying normal anatomical structures in EUS-procedures.

Detailed description

EUS is an operator dependent procedure where accuracy depends on experience and skills. Nowadays, EUS-training can be achieved by a formal fellowship training in a center for 6-24 months or an informal training through didactic sessions with a short hands-on experience. However, parameters for a correct and complete learning experience measurement are yet to be defined. The implementation of artificial intelligence on EUS can potentially mitigate the operator-dependent variable and improve diagnostic accuracy. Therefore, detection of normal anatomical structures on a separate basis using an AI-based model, expert and non-expert endoscopists to determine where the AI would be most helpful. The investigators aim to compare the diagnostic accuracy of the AI-based model with the endoscopists identification of normal anatomical structures in EUS procedures.

Interventions

DIAGNOSTIC_TESTDetection of structures

Pre-recorded videos, cropped according to the different windows (mediastinal, gastric, duodenal) will be analyzed by the AIWorks-EUS model and endoscopists on different times for recognition of the different normal anatomical structures.

Study design

Observational model

COHORT

Time perspective

CROSS_SECTIONAL

Eligibility

Sex/Gender

ALL

Age

18 Years to 99 Years

Healthy volunteers

Inclusion criteria

* Expert gastrointestinal EUS-endoscopists. * Non-expert gastrointestinal endoscopists training for EUS. * Patients with chronic dyspepsia without other findings. * Patients with previous CT images or upper digestive endoscopy reporting no other findings. * Patients requiring EUS for surveillance due to family history of pancreatic cancer without findings on MRI.

Exclusion criteria

* Internet connection less than 100 MBs per second. * Patients with abnormal structures or with visible lesions.

Design outcomes

Primary

Measure	Time frame	Description
Diagnostic accuracy	5 months	The true positive, true negative, false positive and false negative based on detection of anatomical structures according to the an external expert endoscopist as gold-standard.

Secondary

Measure	Time frame	Description
Interobserver agreement	5 months	Comparison of diagnostic accuracies between Artificial intelligence (AI)-based model and both groups (expert and non-expert endoscopists) using Fleiss Kappa.

Countries

Ecuador

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026