Achalasia CardiaNCT07399652
Artificial Intelligence-Guided Detection of Blood Vessels to Enhance Safety in Third-Space Endoscopic Procedures
A study testing AI generated segmentation mask for sub-mucosal blood vessels in people with Achalasia Cardia and Tumor, run by Asian Institute of Gastroenterology, India at 1 site in India.
Trial 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). Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Achalasia Cardia, Tumor
- What is being tested?
- AI generated segmentation mask for sub-mucosal blood vessels
- Type of treatment
- Medical device
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 40 participants
- Where
- 1 siteIndia
- Study dates
- 10 Feb 2026to 30 Apr 2026
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Who may be able to join
- Patients diagnosed with Achalasia Cardia or neoplasms.
Who cannot join
- Patients with conditions deemed unsuitable for third space endoscopy procedures (e.g.: Candidiasis)
Trial locations and contacts
1 site in 1 country.
About this study
Official title: 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.
Read the 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.
- Conditions
- Achalasia Cardia, Tumor
- Being tested
- AI generated segmentation mask for sub-mucosal blood vessels
- Keywords
- Artificial Intelligence, Third Space Endoscopy, POEM, ESD, STER, EMR
- Medical subject headings
- Esophageal Achalasia, Neoplasms
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
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.
| Study group | Role | What they receive |
|---|---|---|
| With Artificial Intelligence | Active comparator | Endoscopist will see the AI generated segmentation mask Receives: Device: AI generated segmentation mask for sub-mucosal blood vessels |
| Without Artificial Intelligence | No intervention | Endoscopist will not see the AI generated segmentation mask |
What is being measured
The primary outcome is the main question the study is designed to answer.
Vessel Detection Rate (VDR)
the proportion of vessels identified by the endoscopist
Measured: 3 months
Secondary outcomes (1)
Vessel Detection Time (VDT)
The latency between a vessel appearing in the field of view and its identification.
Measured: 3 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted10 Feb 2026
- Study start10 Feb 2026
- Primary completion30 Apr 2026
- Study completion30 Apr 2026
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Achalasia CardiaStudies of the same condition, open and under way.
- More trials run by Asian Institute of Gastroenterology, IndiaThe sponsor's other registered studies.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Device feasibility
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Model description
- 20 interventions studied with AI for device feasibility purpose 20 interventions studied without AI for comparison Alternating allocation - If arm had been randomized and by chance an operator's unassisted cases landed early and assisted cases late, any improvement that was really just practice across the series would be credited to the AI. Alternating (starting unassisted, then AI) interleaves the two arms across each operator's own learning curve, so a within-operator time trend is balanced across arms rather than confounded with arm.
- Masking
- None (open label)
- Enrolment
- 40 participants (actual)
Sponsor and investigators
- Lead sponsor
- Asian Institute of Gastroenterology, India
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Abhishek Tyagi, Asian Institute of Gastroenterology, India
- Investigators
- Abhishek Tyagi, Principal investigator, Asian Intitute of Gastroenterology
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07399652
- Sponsor's protocol ID
- AITSE-1
- Status last verified
- Sep 2026
- First submitted
- 27 Jan 2026
- Last updated
- 22 Sep 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
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.
PubMed 39909396Ebigbo 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.
PubMed 36109151
Study at a glance
From the registry record, last updated by the sponsor on 22 Sep 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Artificial Intelligence-Guided Detection of Anatomical Markers to Enhance Safety in Third-Space Endoscopic Procedures
- ClinicalTrials.gov ID
- NCT07399652
- Acronym / sponsor ID
- AITSE-1
- Conditions
- Achalasia Cardia, Tumor
- Keywords
- Artificial Intelligence, Third Space Endoscopy, POEM, ESD, STER, EMR
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 40 participants (actual)
- Lead sponsor
- Asian Institute of Gastroenterology, IndiaUniversity, hospital or other
- Study start
- 10 Feb 2026 (actual)
- Primary completion
- 30 Apr 2026 (actual)
- Study completion
- 30 Apr 2026 (actual)
- First posted
- 10 Feb 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Device feasibility
- Allocation
- Non-randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
- Model description
- 20 interventions studied with AI for device feasibility purpose 20 interventions studied without AI for comparison Alternating allocation - If arm had been randomized and by chance an operator's unassisted cases landed early and assisted cases late, any improvement that was really just practice across the series would be credited to the AI. Alternating (starting unassisted, then AI) interleaves the two arms across each operator's own learning curve, so a within-operator time trend is balanced across arms rather than confounded with arm.
| Arm | Type | Receives |
|---|---|---|
| With Artificial Intelligence | Active comparator | Device: AI generated segmentation mask for sub-mucosal blood vesselsEndoscopist will see the AI generated segmentation mask |
| Without Artificial Intelligence | No intervention | –Endoscopist will not see the AI generated segmentation mask |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (1 listed)
- Patients diagnosed with Achalasia Cardia or neoplasms.
Key exclusion criteria (1 listed)
- Patients with conditions deemed unsuitable for third space endoscopy procedures (e.g.: Candidiasis)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AI generated segmentation mask for sub-mucosal blood vesselsReal time AI generated segmentation mask or delineation contours for sub-mucosal blood vessels visible on the endoscopy monitor. | Device | With Artificial Intelligence |
Primary outcome
Vessel Detection Rate (VDR)
Time frame: 3 months
the proportion of vessels identified by the endoscopist
1 secondary outcome
Vessel Detection Time (VDT)
Time frame: 3 months
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 22 Sep 2026.
- Results publication: PubMed 39909396Scheppach 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.
- Results publication: PubMed 36109151Ebigbo 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.
- Individual participant dataSharing planned: No
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Achalasia
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Abhishek Tyagi principal investigator
Asian Intitute of Gastroenterology
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AI generated segmentation mask for sub-mucosal blood vessels in people with Achalasia Cardia and Tumor, run by Asian Institute of Gastroenterology, India at 1 site in India.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 22 Sep 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in India. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Vessel Detection Rate (VDR) (time frame: 3 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07399652, which also lists 2 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 22 Sep 2026.