Kunstig Intelligens-vejledt detektion af blodkar for at forbedre sikkerheden ved tredje-rum endoskopiske procedurer
Kunstig Intelligens-styret detektion af anatomiske markører til forbedret sikkerhed ved tredje-rum endoskopiske procedurer
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.
Studieoversigt
Status
Betingelser
Intervention / Behandling
Detaljeret beskrivelse
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.
Undersøgelsestype
Tilmelding (Faktiske)
Fase
- Ikke anvendelig
Kontakter og lokationer
Studiesteder
-
-
Telangana
-
Hyderabad, Telangana, Indien, 500032
- Asian Institute of Gastroenterology
-
-
Deltagelseskriterier
Berettigelseskriterier
Aldre berettiget til at studere
- Barn
- Voksen
- Ældre voksen
Tager imod sunde frivillige
Køn, der er berettiget til at studere
Beskrivelse
Inclusion Criteria:
- Patients diagnosed with Achalasia Cardia or neoplasms.
Exclusion Criteria:
- Patients with conditions deemed unsuitable for third space endoscopy procedures (e.g.: Candidiasis)
Studieplan
Hvordan er undersøgelsen tilrettelagt?
Design detaljer
- Primært formål: Enhedens gennemførlighed
- Tildeling: Ikke-randomiseret
- Interventionel model: Parallel tildeling
- Maskning: Ingen (Åben etiket)
Våben og indgreb
Deltagergruppe / Arm |
Intervention / Behandling |
|---|---|
|
Aktiv komparator: With Artificial Intelligence
Endoscopist will see the AI generated segmentation mask |
Real time AI generated segmentation mask or delineation contours for sub-mucosal blood vessels visible on the endoscopy monitor. |
|
Ingen indgriben: Without Artificial Intelligence
Endoscopist will not see the AI generated segmentation mask |
Hvad måler undersøgelsen?
Primære resultatmål
Resultatmål |
Foranstaltningsbeskrivelse |
Tidsramme |
|---|---|---|
|
Vessel Detection Rate (VDR)
Tidsramme: 3 months
|
the proportion of vessels identified by the endoscopist |
3 months
|
Sekundære resultatmål
Resultatmål |
Foranstaltningsbeskrivelse |
Tidsramme |
|---|---|---|
|
Vessel Detection Time (VDT)
Tidsramme: 3 months
|
The latency between a vessel appearing in the field of view and its identification. |
3 months
|
Samarbejdspartnere og efterforskere
Efterforskere
- Ledende efterforsker: Abhishek Tyagi, Asian Intitute of Gastroenterology
Publikationer og nyttige links
Generelle publikationer
- 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.
- 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.
Datoer for undersøgelser
Studer store datoer
Studiestart (Faktiske)
Primær færdiggørelse (Faktiske)
Studieafslutning (Faktiske)
Datoer for studieregistrering
Først indsendt
Først indsendt, der opfyldte QC-kriterier
Først opslået (Faktiske)
Opdateringer af undersøgelsesjournaler
Sidste opdatering sendt (Faktiske)
Sidste opdatering indsendt, der opfyldte kvalitetskontrolkriterier
Sidst verificeret
Mere information
Begreber relateret til denne undersøgelse
Nøgleord
Yderligere relevante MeSH-vilkår
Andre undersøgelses-id-numre
- AITSE-1
Plan for individuelle deltagerdata (IPD)
Planlægger du at dele individuelle deltagerdata (IPD)?
Lægemiddel- og udstyrsoplysninger, undersøgelsesdokumenter
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