Artificial Intelligence-Guided Detection of Anatomical Markers to Enhance Safety in Third-Space Endoscopic Procedures
试验速览
- 阶段
- 不适用
- 状态
- 招募中
- 发起方
- 入组人数
- 20
- 试验地点
- 1
- 主要终点
- Vessel Detection Rate (VDR)
研究概览
简要总结
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.
详细描述
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:
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Device Feasibility
- 盲法
- None
入排标准
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Patients diagnosed with Achalasia Cardia or neoplasms.
排除标准
- •Patients with conditions deemed unsuitable for third space endoscopy procedures (e.g.: Candidiasis)
研究组 & 干预措施
With Artificial Intelligence
Endoscopist will see the AI generated segmentation mask
干预措施: AI generated segmentation mask for sub-mucosal blood vessels (Device)
Without Artificial Intelligence
Endoscopist will not see the AI generated segmentation mask
结局指标
主要结局
Vessel Detection Rate (VDR)
时间窗: 3 months
the proportion of vessels identified by the endoscopist
次要结局
- Vessel Detection Time (VDT)(3 months)
研究者
Abhishek Tyagi
Principal AI Engineer
Asian Institute of Gastroenterology, India