Artificial Intelligence-Guided Detection of Blood Vessels to Enhance Safety in Third-Space Endoscopic Procedures
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: AI generated segmentation mask for sub-mucosal blood vessels.
- Who it may be relevant to
- Registry conditions: Achalasia Cardia, Tumor. Basic parameters: No limits · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- India
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Artificial Intelligence-Guided Detection of Anatomical Markers to Enhance Safety in Third-Space Endoscopic Procedures
Overview
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.
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.
Interventions
- 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.
Primary outcome measures
- Vessel Detection Rate (VDR) [Time frame: 3 months]
Secondary outcome measures (1)
- Vessel Detection Time (VDT) [Time frame: 3 months]
Eligibility criteria
Inclusion criteria
- Patients diagnosed with Achalasia Cardia or neoplasms.
Exclusion criteria
- Patients with conditions deemed unsuitable for third space endoscopy procedures (e.g.: Candidiasis)
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Allocation
- Randomized
- Model
- Parallel assignment
- Masking
- Open label
- Primary purpose
- Device feasibility
Study locations
India · 1 center
- Asian Institute of Gastroenterology — Hyderabad
Publications
- 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 39909396
- 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 36109151
Identifiers
NCT: NCT07399652 · AITSE-1