AI-Based Dissection Trajectory Prediction System, Esophageal Squamous Cell Carcinoma (ESCC), High-Grade Intraepithelial Neoplasia
Conditions
Keywords
Artificial intelligence, R0 resection, Procedural efficiency, NASA-TLX, Randomized controlled trial, Dissection trajectory prediction, Endoscopic submucosal dissection
Brief 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.
Interventions
DEVICEAI-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.
DEVICEConventional 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.
Sponsors
Qilu Hospital of Shandong University
Study design
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
TREATMENT
Masking
DOUBLE (Subject, Caregiver)
Eligibility
Sex/Gender
ALL
Age
18 Years to 80 Years
Healthy volunteers
No
Inclusion criteria
* Chinese patients aged 18-80 years. Lesions meeting ESD indications for esophageal, gastric, or colorectal early cancer or high-grade intraepithelial neoplasia, as defined by: Non-invasive tumors regardless of size; or Differentiated-type intramucosal carcinoma without ulceration, regardless of size; or Differentiated-type intramucosal carcinoma with ulceration and a diameter ≤3 cm; or Undifferentiated-type intramucosal carcinoma without ulceration and a diameter ≤2 cm. Planned to undergo ESD treatment. No prior treatment for the lesion (including ESD, surgery, radiotherapy, chemotherapy, etc.). Platelet count \>100 × 10⁹/L and PT-INR \<1.5, with antiplatelet agents (aspirin, clopidogrel, etc.) discontinued for at least 5 days. American Society of Anesthesiologists (ASA) physical status grade I or II. Voluntarily signed informed consent.
Exclusion criteria
* Patients currently undergoing dialysis. Patients with severe cardiopulmonary disease or other severe comorbidities that may increase the risk of the ESD procedure. Pregnant or breastfeeding women.
Design outcomes
Primary
| Measure | Time frame |
|---|---|
| NASA-TLX Workload Score | Immediately after the ESD procedure |
| Mean Single Dissection Time | During the ESD procedure |
Secondary
| Measure | Time frame |
|---|---|
| R0 Resection Rate | At the time of pathological examination |
| Muscularis Propria Injury Rate | During the ESD procedure |
| Intraoperative Bleeding Episodes | During the ESD procedure |
| Intraoperative Perforation Rate | During the ESD procedure |
| Delayed Bleeding Rate | Within 14 days after procedure |
| Delayed Perforation Rate | Within 14 days after procedure |
| Total Procedure Time | During the ESD procedure |
Contacts
CONTACTZhen Li
Outcome results
None listed