Artificial Intelligence vs Endoscopist Identification in EUS Normal Anatomy

Gastrointestinal Diseases Clinical Trial

Official title:

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

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT06279546
• Other study ID #: IECED-12345
• Status: Completed
• Start date: May 1, 2023
• Est. completion date: January 26, 2024

Study information

• Verified date: February 2024
• Source: Instituto Ecuatoriano de Enfermedades Digestivas
• Contact: n/a
• Is FDA regulated: No
• Study type: Observational [Patient Registry]

Clinical Trial 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.

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.

Recruitment information / eligibility

• Status: Completed
• Enrollment: 30
• Est. completion date: January 26, 2024
• Est. primary completion date: October 1, 2023
• Accepts healthy volunteers: No
• Gender: All
• Age group: 18 Years to 99 Years

Eligibility

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.

Related Conditions & MeSH terms

• Digestive System Diseases
• Gastrointestinal Diseases

Intervention

Diagnostic Test:
• Detection 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.

Locations

Country	Name	City	State
Ecuador	IECED	Guayaquil	Guayas

Sponsors (12)

Lead Sponsor

Collaborator

Instituto Ecuatoriano de Enfermedades Digestivas

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

Country where clinical trial is conducted

Ecuador, 

References & Publications (4)

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. — View 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. — View 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. — View 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. — View Citation

Outcome

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