NCT07399652

Brief Summary

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.

Trial Health

57
Monitor

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Trial has exceeded expected completion date
Enrollment
20

participants targeted

Target at below P25 for not_applicable

Timeline
Completed

Started Feb 2026

Shorter than P25 for not_applicable

Geographic Reach
1 country

1 active site

Status
recruiting

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

First Submitted

Initial submission to the registry

January 27, 2026

Completed
14 days until next milestone

First Posted

Study publicly available on registry

February 10, 2026

Completed
Same day until next milestone

Study Start

First participant enrolled

February 10, 2026

Completed
3 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

April 30, 2026

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

April 30, 2026

Completed
Last Updated

April 15, 2026

Status Verified

February 1, 2026

Enrollment Period

3 months

First QC Date

January 27, 2026

Last Update Submit

April 14, 2026

Conditions

Achalasia CardiaTumor

Keywords

Artificial IntelligenceThird Space EndoscopyPOEMESDSTEREMR

Outcome Measures

Primary Outcomes (1)

  • Vessel Detection Rate (VDR)

    the proportion of vessels identified by the endoscopist

    3 months

Secondary Outcomes (1)

  • Vessel Detection Time (VDT)

    3 months

Study Arms (2)

With Artificial Intelligence

ACTIVE COMPARATOR

Endoscopist will see the AI generated segmentation mask

Device: AI generated segmentation mask for sub-mucosal blood vessels

Without Artificial Intelligence

NO INTERVENTION

Endoscopist will not see the AI generated segmentation mask

Interventions

AI generated segmentation mask for sub-mucosal blood vesselsDEVICE

Real time AI generated segmentation mask or delineation contours for sub-mucosal blood vessels visible on the endoscopy monitor.

With Artificial Intelligence

Eligibility Criteria

Sexall
Healthy VolunteersNo
Age GroupsChild (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

  • Patients diagnosed with Achalasia Cardia or neoplasms.

You may not qualify if:

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

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Asian Institute of Gastroenterology

Hyderabad, Telangana, 500032, India

RECRUITING

Related Publications (2)

  • Scheppach MW, Mendel R, Muzalyova A, Rauber D, Probst A, Nagl S, Rommele C, Yip HC, Lau LHS, Golder SK, Schmidt A, Kouladouros K, Abdelhafez M, Walter BM, Meinikheim M, Chiu PWY, Palm C, Messmann H, Ebigbo A. Use of artificial intelligence in submucosal vessel detection during third-space endoscopy. Endoscopy. 2025 Jul;57(7):760-766. doi: 10.1055/a-2534-1164. Epub 2025 Feb 5.

    PMID: 39909396RESULT

  • Ebigbo A, Mendel R, Scheppach MW, Probst A, Shahidi N, Prinz F, Fleischmann C, Rommele C, Goelder SK, Braun G, Rauber D, Rueckert T, de Souza LA Jr, Papa J, Byrne M, Palm C, Messmann H. Vessel and tissue recognition during third-space endoscopy using a deep learning algorithm. Gut. 2022 Dec;71(12):2388-2390. doi: 10.1136/gutjnl-2021-326470. Epub 2022 Sep 15.

    PMID: 36109151RESULT

MeSH Terms

Conditions

Esophageal AchalasiaNeoplasms

Condition Hierarchy (Ancestors)

Esophageal Motility DisordersDeglutition DisordersEsophageal DiseasesGastrointestinal DiseasesDigestive System Diseases

Study Officials

  • Abhishek Tyagi

    Asian Intitute of Gastroenterology

    PRINCIPAL INVESTIGATOR

Central Study Contacts

Abhishek Tyagi, M.S.

CONTACT

919989154556mr.tyagi@gmail.com

Mohan Ramchandani, M.D.

CONTACT

919701335444ramchandanimohan@gmail.com

Study Design

Study Type
interventional
Phase
not applicable
Allocation
RANDOMIZED
Masking
NONE
Purpose
DEVICE FEASIBILITY
Intervention Model
PARALLEL
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Principal AI Engineer

Study Record Dates

First Submitted

January 27, 2026

First Posted

February 10, 2026

Study Start

February 10, 2026

Primary Completion

April 30, 2026

Study Completion

April 30, 2026

Last Updated

April 15, 2026

Record last verified: 2026-02

Data Sharing

IPD Sharing
Will not share

Locations

IN(1)