Artificial Intelligence-Guided Detection of Blood Vessels to Enhance Safety in Third-Space Endoscopic Procedures

April 14, 2026 updated by: Abhishek Tyagi, Asian Institute of Gastroenterology, India

Artificial Intelligence-Guided Detection of Anatomical Markers to Enhance Safety in Third-Space Endoscopic Procedures

This prospective study aims to evaluate the performance of a novel Artificial Intelligence (AI) clinical decision support tool during third space endoscopic procedures, such as Endoscopic Submucosal Dissection (ESD) and Peroral Endoscopic Myotomy (POEM).

While these procedures are effective for treating gastrointestinal neoplasms and motility disorders, they carry risks of intraprocedural bleeding and perforation if submucosal blood vessels are not correctly identified and coagulated. Building on previous retrospective validation, this study will assess whether a real-time artificial intelligence model can assist endoscopists in detecting and delineating blood vessels more accurately and faster during live human procedures.

Study Overview

Status

Recruiting

Conditions

Intervention / Treatment

Detailed Description

Background and Rationale

Third-space endoscopy procedures are technically demanding. The primary challenge lies in the inadvertent transection of submucosal vessels, which leads to bleeding that obscures the surgical field and increases the risk of perforation. Currently, vessel identification is entirely operator-dependent.

Our team has developed a deep-learning based artificial intelligence model trained on 250,000 annotated images from 150 POEM procedures. This model is optimized for minimal latency, allowing for real-time visual overlays (delineation) of blood vessels on the endoscopic monitor.

Study Objectives The primary objective is to evaluate the Vessel Detection Rate (VDR)-the proportion of vessels identified by the endoscopist when assisted by the AI compared to standard practice.

The study will also investigate:

Vessel Detection Time (VDT): The latency between a vessel appearing in the field of view and its identification.

Study Design & Workflow:

In this prospective study, the AI system will be integrated into the Olympus EVIS X1 series endoscopy stack. As the endoscopist navigates the submucosal space, the AI will provide real-time visual segmentation masks highlighting vessels. The performance will be recorded and compared against a post-procedure review by independent experts to calculate sensitivity and detection speed.

Study Type

Interventional

Enrollment (Estimated)

20

Phase

  • Not Applicable

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 Contact Backup

Study Locations

  • India
    • Telangana
      • Hyderabad, Telangana, India, 500032
        • Recruiting
        • Asian Institute of Gastroenterology
        • Contact:
        • Principal Investigator:
          • Abhishek Tyagi, M.S.

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

  • Child
  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Description

Inclusion Criteria:

  • Patients diagnosed with Achalasia Cardia or neoplasms.

Exclusion Criteria:

  • Patients with conditions deemed unsuitable for third space endoscopy procedures (e.g.: Candidiasis)

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

  • Primary Purpose: Device Feasibility
  • Allocation: Randomized
  • Interventional Model: Parallel Assignment
  • Masking: None (Open Label)

Arms and Interventions

Participant Group / Arm
Intervention / Treatment
Active Comparator: With Artificial Intelligence
Endoscopist will see the AI generated segmentation mask
Device: AI generated segmentation mask for sub-mucosal blood vessels
Real time AI generated segmentation mask or delineation contours for sub-mucosal blood vessels visible on the endoscopy monitor.
No Intervention: Without Artificial Intelligence
Endoscopist will not see the AI generated segmentation mask

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Vessel Detection Rate (VDR)
Time Frame: 3 months
the proportion of vessels identified by the endoscopist
3 months

Secondary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Vessel Detection Time (VDT)
Time Frame: 3 months
The latency between a vessel appearing in the field of view and its identification.
3 months

Collaborators and Investigators

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

Sponsor

Asian Institute of Gastroenterology, India

Investigators

  • Principal Investigator: Abhishek Tyagi, Asian Intitute of Gastroenterology

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)

February 10, 2026

Primary Completion (Estimated)

April 30, 2026

Study Completion (Estimated)

April 30, 2026

Study Registration Dates

First Submitted

January 27, 2026

First Submitted That Met QC Criteria

February 3, 2026

First Posted (Actual)

February 10, 2026

Study Record Updates

Last Update Posted (Actual)

April 15, 2026

Last Update Submitted That Met QC Criteria

April 14, 2026

Last Verified

February 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • AITSE-1

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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