Clinical Application of an AI-based Dissection Trajectory Prediction System (ADTPS) in Endoscopic Submucosal Dissection

Condition(s) studied

Investigational drug(s) / intervention(s)

AI-based Dissection Trajectory Prediction System (ADTPS): The ADTPS is an artificial intelligence software system that analyzes endoscopic images in real time during ESD. It automatically identifies lesion boundaries and generates a recommended dissection trajectory overlaid on the endoscopic view. The endoscopist follows the AI-generated trajectory to perform submucosal dissection. The system provides visual guidance only and does not alter the standard surgical workflow.

Conventional ESD Procedure: The same endoscopic hardware platform is used, but the AI-based Dissection Trajectory Prediction System (ADTPS) is turned off. The endoscopist performs the ESD procedure based solely on clinical judgment and personal experience, following standard conventional ESD steps including marking, injection, circumferential incision, and submucosal dissection.

Study summary

In this prospective paired diagnostic study and single-center, randomized controlled trial, patients with early esophageal squamous neoplasia or high-grade intraepithelial neoplasia meeting the inclusion and exclusion criteria will be enrolled in a paired diagnostic cohort (60 patients) and subsequently randomly assigned (1:1) to receive endoscopic submucosal dissection (ESD) with AI-based Dissection Trajectory Prediction System (ADTPS) guidance or conventional ESD (without AI). Clinical data and operator workload scores (NASA-TLX) are collected during the procedure, and postoperative follow-up assessments are performed at days 1, 3, 7, and 14. The study aims to analyze the impact of ADTPS on the mean single-dissection time and operator workload in patients undergoing ESD by comparing the efficacy differences between the experimental and control groups. Additionally, the study investigates the effects of ADTPS on other postoperative complications including R0 resection rate, muscularis propria injury, intraoperative bleeding, perforation (acute and delayed), and total procedure time; conducts a comparative analysis of the safety and efficiency of AI-assisted versus conventional ESD; and develops effective clinical strategies for optimizing dissection trajectory and reducing complications in endoscopic submucosal dissection.

Eligibility

Primary outcome measure(s)

• NASA-TLX Workload Score — Immediately after the ESD procedure
• Mean Single Dissection Time — During the ESD procedure

Trial sites (1)

More Qilu Hospital of Shandong University trials in China

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