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

December 10, 2021 updated by: Instituto Ecuatoriano de Enfermedades Digestivas

Endoscopic Ultrasound (EUS) Assessment of Normal Mediastinal and Abdominal Organ/Anatomic Strictures Using a Novel Developed Artificial Intelligence Model

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.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

Endoscopic ultrasound (EUS) is a high-skilled procedure with a limited number of facilities available for training. Therefore, a high number of procedures is necessary to achieve competency. However, the agreement between observers varies widely. Artificial intelligence (AI) aided recognition and characterization of anatomical structures may improve the training process while improving the agreement between observers. However, developed EUS-AI models have been explicitly trained or only with disease samples or for detecting abdominal anatomical features.

In other fields as Radiation Oncology, developed AI models have been widely used. They must recognize in unison healthy and disease strictures throughout any part of the human body during the contouring. It avoids unnecessary irradiation of normal tissue. EUS-AI models not trained with healthy samples can cause an increase in false-positive cases during real-life practice. It implies potential overdiagnosis of abnormal/disease strictures. EUS-AI models not trained with samples outside

Using an automated machine learning software, Robles-Medranda et al. have previously developed a convolutional neuronal networks (CNN) AI model that recognizes the anatomical structures during linear and radial EUS evaluations (AI Works, MD Consulting group, Ecuador). To the best of our knowledge, this EUS-AI model is the first trained with EUS videos from patients without pathologies and, thus, with normal mediastinal and abdominal organ/anatomic strictures. In this second stage, we pursue to design an external validation of our developed AI model, considering an endoscopist expert as the gold standard.

Study Type

Observational

Enrollment (Anticipated)

60

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

  • Ecuador
    • Guayas
      • Guayaquil, Guayas, Ecuador, 090505
        • Recruiting
        • Ecuadorian Institute of Digestive Diseases
        • Contact:
        • Principal Investigator:
          • Carlos Robles-Medranda, MD FASGE
        • Sub-Investigator:
          • Martha Arevalo-Mora, MD
        • Sub-Investigator:
          • Daniel Calle, MD MSc
        • Sub-Investigator:
          • Miguel Puga-Tejada, MD MSc
        • Sub-Investigator:
          • Raquel Del Valle Zavala, MD
        • Sub-Investigator:
          • Juan Alcivar-Vasquez, MD Msc

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

18 years to 79 years (Adult, Older Adult)

Accepts Healthy Volunteers

No

Genders Eligible for Study

All

Sampling Method

Probability Sample

Study Population

Adult patients with normal mediastinal and abdominal organ/anatomic strictures after imaging test and EUS assessment due to chronic dyspepsia.

Description

Inclusion Criteria:

  • Patients with no history of the thorax and abdominal abnormalities confirmed through an imaging test requested for healthcare purposes in the last twelve months (e.g., thorax X-ray and abdominal ultrasound or thorax and abdominal CT)
  • Patients who undergo EUS assessment due to chronic dyspepsia.

Exclusion Criteria:

  • Morphological alteration on at least one mediastinal and abdominal organ/anatomic strictures documented through any imaging test or EUS.
  • Uncontrolled coagulopathy, kidney/liver failure, or any comorbidity with a meaningful impact on cardiac risk assessment (NHYA III/IV);
  • Refuse to participate in the study or to sign corresponding informed consent.

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

  • Observational Models: Case-Only
  • Time Perspectives: Cross-Sectional

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Patients with normal mediastinal and abdominal organ/anatomic strictures
Adult patients with normal mediastinal and abdominal organ/anatomic strictures after imaging test and EUS assessment due to chronic dyspepsia.
Diagnostic test: Identification or discharge visualization of mediastinal and abdominal organ/anatomic strictures through Endoscopic ultrasound (EUS) videos by an expert endoscopist
An expert endoscopist will select a dataset of mediastinal and abdominal EUS videos (one per patient). An expert endoscopist will identify or discharge visualization of the following organs correctly: aorta, vertebral spine, aortic arch, trachea, AP window, left kidney, liver, spleen, pancreas body, pancreas tail, coeliac trunk, splenic artery, splenic vein, inferior vena cava, adrenal gland, right kidney, gallbladder, common bile duct, ampulla of Vater, portal vein.
Diagnostic test: Recognition of mediastinal and abdominal organ/anatomic strictures through Endoscopic ultrasound (EUS) videos using artificial intelligence (AI)
Using the same previous dataset of mediastinal and abdominal EUS videos, the EUS-AI model will recognize the following organs: aorta, vertebral spine, aortic arch, trachea, AP window, left kidney, liver, spleen, pancreas body, pancreas tail, coeliac trunk, splenic artery, splenic vein, inferior vena cava, adrenal gland, right kidney, gallbladder, common bile duct, ampulla of Vater, portal vein. Considering each patient (and not data frame videos) as the study unit, a contingency table per each mediastinal and abdominal organ/anatomic stricture will be designed.

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Overall accuracy of Endoscopic ultrasound (EUS) artificial intelligence (AI) model for identifying normal mediastinal and abdominal organ/anatomic strictures
Time Frame: Three months

Overall accuracy features will be calculated: sensitivity, specificity, positive predictive value, negative predictive value, diagnostic accuracy, and observed agreement. In addition, there will be defined the following probabilities:

  • True-positive (TP): mediastinal/abdominal organ/anatomic stricture recognized by the EUS-AI model. The expert endoscopist previously correctly identified it.
  • False-positive (FP): mediastinal/abdominal organ/anatomic stricture recognized by the EUS-AI model. The expert endoscopist previously correctly discharged its visualization.
  • False-negative (FN): mediastinal/abdominal organ/anatomic stricture not recognized by the EUS-AI model. The expert endoscopist previously correctly identified it.
  • True-negative (TN): mediastinal/abdominal organ/anatomic stricture not recognized by the EUS-AI model. The expert endoscopist previously correctly discharged its visualization.
Three months

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Instituto Ecuatoriano de Enfermedades Digestivas

Investigators

  • Principal Investigator: Carlos Robles-Medranda, Ecuadorian Institute of Digestive Diseases

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

October 1, 2021

Primary Completion (Anticipated)

March 30, 2022

Study Completion (Anticipated)

June 30, 2022

Study Registration Dates

First Submitted

November 26, 2021

First Submitted That Met QC Criteria

December 8, 2021

First Posted (Actual)

December 9, 2021

Study Record Updates

Last Update Posted (Actual)

December 30, 2021

Last Update Submitted That Met QC Criteria

December 10, 2021

Last Verified

December 1, 2021

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • IECED-26112021

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

No

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

product manufactured in and exported from the U.S.

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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