Clinical trial · Interventional
Artificial Intelligence-Guided Detection of Blood Vessels to Enhance Safety in Third-Space Endoscopic Procedures
Artificial Intelligence-Guided Detection of Anatomical Markers to Enhance Safety in Third-Space Endoscopic Procedures
- Source
- ClinicalTrials.gov
- Retrieved
- Sep 26, 2026
- Layer
- normalized (units and labels harmonized; values unchanged)
- Run
- ING-CLINICALTRIALS-20260926-000001
Summary
Brief summary (as posted)
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.
Conditions
Conditions (2)
Free-text conditions as registered, with the CancerIndex entity they were reconciled to and the match type.
| Condition (as posted) | Mapped entity | Match | Confidence |
|---|---|---|---|
| Achalasia Cardia | — | UNRESOLVED | — |
| Tumor | Neoplasm | ALIAS | 0.90 |
Interventions
Interventions (1)
| Intervention | Type | Mapped drug | Match |
|---|---|---|---|
| AI generated segmentation mask for sub-mucosal blood vessels | Device | — | UNRESOLVED |
Design
Arms and outcomes
Arms (2)
- type
- ACTIVE_COMPARATOR
- label
- With Artificial Intelligence
- description
- Endoscopist will see the AI generated segmentation mask
- interventionNames
- Device: AI generated segmentation mask for sub-mucosal blood vessels
- type
- NO_INTERVENTION
- label
- Without Artificial Intelligence
- description
- Endoscopist will not see the AI generated segmentation mask
Primary outcomes (1)
- measure
- Vessel Detection Rate (VDR)
- timeFrame
- 3 months
- description
- the proportion of vessels identified by the endoscopist
Secondary outcomes (1)
Eligibility
Eligibility (as posted)
- Sex
- All
Show eligibility criteria text
Inclusion Criteria: * Patients diagnosed with Achalasia Cardia or neoplasms. Exclusion Criteria: * Patients with conditions deemed unsuitable for third space endoscopy procedures (e.g.: Candidiasis)
References
Publications (2)
- RESULTScheppach 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
- RESULTEbigbo 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