Clinical trial NCT06279546: Artificial Intelligence vs Endoscopist Identification in EUS Normal Anatomy
Official title: Comparative Evaluation of Artificial Intelligence and Endoscopists´ Accuracy in Endoscopic Ultrasound for Identifying Normal Anatomical Structures: A Multi-institutional, Cross-sectional Study
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Gastrointestinal Diseases
Keywords: EUS
AI
Gastrointestinal
Lead sponsor: Instituto Ecuatoriano de Enfermedades Digestivas
Sponsor class: OTHER
Collaborators: name: The Methodist Hospital Research Institute
name: Baylor Saint Luke's Medical Center
name: Beth Israel Deaconess Medical Center
name: Barra Life Medical Center, Brazil
name: Hospital Clinico Universitario de Santiago
name: Universitair Ziekenhuis Brussel
name: Hospital Civil de Morelia, Michoacan
name: ELIAS Emergency University Hospital
name: Larkin Community Hospital
name: Carol Davila University of Medicine and Pharmacy
name: mdconsgroup, Guayaquil, Ecuador
First posted: 2024-02-28
Enrollment: 30
Sex: ALL
Minimum age (years): 18
Maximum age (years): 99
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Expert and non-expert gastrointestinal EUS-endoscopists.

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.

Eligibility criteria: 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.

Primary outcomes:
measure: Diagnostic accuracy
timeFrame: 5 months
Secondary outcomes:
measure: Interobserver agreement
timeFrame: 5 months
Other outcomes:
Locations:
city: Guayaquil
country: Ecuador
facility: IECED
state: Guayas
Study officials:
affiliation: Instituto Ecuatoriano de Enfermedades Digestivas (IECED)
name: Carlos Robles-Medranda, MD FASGE
role: PRINCIPAL_INVESTIGATOR
References:
citation: 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.
citation: 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.
citation: 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.
citation: 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.