Artificial Intelligence vs Endoscopist Identification in EUS Normal Anatomy

NCT06279546 | Completed

• 1 other identifier: IECED-12345
• Study Type: observational
• Target: 30
• Locations: 1 country
• Sites: 1

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.

Conditions

Gastrointestinal Diseases

Keywords

EUS; AI; Gastrointestinal

Outcome Measures

Primary Outcomes (1)

• Diagnostic accuracy

  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.

  5 months

Secondary Outcomes (1)

• Interobserver agreement

  5 months

Study Arms (3)

AI-based model

AIWorks-EUS Convolutional Neural Network version 2 (CNNv2) (mdconsgroup, Guayaquil, Ecuador) applied on pre-recorded videos for the detection of normal anatomical structures.

Diagnostic Test: Detection of structures

Expert endoscopists

Endoscopists with \>190 EUS procedures per year, including 75 pancreatobiliary and mucosal cancer staging procedures each, 40 subepithelial cases; and 50 cases of EUS-fine needle aspiration (FNA) (25 of them being pancreatic cases); following the American Society for Gastrointestinal Endoscopy (ASGE) recommendations.

Diagnostic Test: Detection of structures

Non-expert endoscopists

Endoscopists with \<190 EUS procedures per year, including 75 pancreatobiliary and mucosal cancer staging procedures each, 40 subepithelial cases; and 50 cases of EUS-FNA (25 of them being pancreatic cases); following the American Society for Gastrointestinal Endoscopy (ASGE) recommendations.

Diagnostic Test: Detection of structures

Interventions

Detection of structures DIAGNOSTIC_TEST

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.

AI-based model; Expert endoscopists; Non-expert endoscopists

Eligibility Criteria

• Age: 18 Years - 99 Years
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Expert and non-expert gastrointestinal EUS-endoscopists.

You may qualify if:

• 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.

You may not qualify if:

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

Sponsors & Collaborators

• Instituto Ecuatoriano de Enfermedades Digestivas: lead
• The Methodist Hospital Research Institute: collaborator
• Baylor Saint Luke's Medical Center: collaborator
• Beth Israel Deaconess Medical Center: collaborator
• Barra Life Medical Center, Brazil: collaborator
• Hospital Clinico Universitario de Santiago: collaborator
• Universitair Ziekenhuis Brussel: collaborator
• Hospital Civil de Morelia, Michoacan: collaborator
• ELIAS Emergency University Hospital: collaborator
• Larkin Community Hospital: collaborator
• Carol Davila University of Medicine and Pharmacy: collaborator
• mdconsgroup, Guayaquil, Ecuador: collaborator

Study Sites (1)

IECED

Guayaquil, Guayas, 090505, Ecuador

Location

Related Publications (4)

• Han C, Nie C, Shen X, Xu T, Liu J, Ding Z, Hou X. Exploration of an effective training system for the diagnosis of pancreatobiliary diseases with EUS: A prospective study. Endosc Ultrasound. 2020 Sep-Oct;9(5):308-318. doi: 10.4103/eus.eus_47_20.

  PMID: 32913147 BACKGROUND

• Cho CM. Training in Endoscopy: Endoscopic Ultrasound. Clin Endosc. 2017 Jul;50(4):340-344. doi: 10.5946/ce.2017.067. Epub 2017 Jul 31.

  PMID: 28783919 BACKGROUND

• Finocchiaro M, Cortegoso Valdivia P, Hernansanz A, Marino N, Amram D, Casals A, Menciassi A, Marlicz W, Ciuti G, Koulaouzidis A. Training Simulators for Gastrointestinal Endoscopy: Current and Future Perspectives. Cancers (Basel). 2021 Mar 20;13(6):1427. doi: 10.3390/cancers13061427.

  PMID: 33804773 BACKGROUND

• Robles-Medranda C, Baquerizo-Burgos J, Puga-Tejada M, Del Valle R, Mendez JC, Egas-Izquierdo M, Arevalo-Mora M, Cunto D, Alcivar-Vasquez J, Pitanga-Lukashok H, Tabacelia D. Development of convolutional neural network models that recognize normal anatomic structures during real-time radial-array and linear-array EUS (with videos). Gastrointest Endosc. 2024 Feb;99(2):271-279.e2. doi: 10.1016/j.gie.2023.10.028. Epub 2023 Oct 10.

  PMID: 37827432 BACKGROUND

MeSH Terms

Conditions

Gastrointestinal Diseases

Condition Hierarchy (Ancestors)

Digestive System Diseases

Study Officials

• Carlos Robles-Medranda, MD FASGE

  Instituto Ecuatoriano de Enfermedades Digestivas (IECED)

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: CROSS SECTIONAL
• Target Duration: 7 Days
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Head of the Endoscopy Division

Study Record Dates

• First Submitted: February 16, 2024
• First Posted: February 28, 2024
• Study Start: May 1, 2023
• Primary Completion: October 1, 2023
• Study Completion: January 26, 2024
• Last Updated: February 28, 2024

Record last verified: 2024-02

Data Sharing

• IPD Sharing: Will not share

Locations

EC (1)