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.
Patients diagnosed with Achalasia Cardia or neoplasms.
Patients with conditions deemed unsuitable for third space endoscopy procedures (e.g.: Candidiasis)
Parallel
AI generated segmentation mask for sub-mucosal blood vessels
DeviceReal time AI generated segmentation mask or delineation contours for sub-mucosal blood vessels visible on the endoscopy monitor.
Vessel Detection Rate (VDR)
the proportion of vessels identified by the endoscopist
Vessel Detection Time (VDT)
The latency between a vessel appearing in the field of view and its identification.
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