Overview
The goal of this study is to develop a real-time artificial intelligence-driven 3D kidney model to assist robotic or laparoscopic partial nephrectomy:
• Can this AI-powered model optimize the workflow of partial nephrectomy and enhance surgical benefits?
Description
This study aims to evaluate the feasibility of the AI-based real-time image-guided kidney model system in optimizing partial nephrectomy workflows. Patients scheduled for laparoscopic or robotic-assisted partial nephrectomy will be randomized to receive either AI-assisted surgical navigation (utilizing intraoperative 3D model overlay with automated registration) or conventional approaches. Comparative metrics will include ischemia time, margin positivity rate, and operative efficiency indices. Findings will inform iterative refinement of the system architecture based on clinical performance feedback.
Eligibility
Inclusion Criteria:
- Ages 18-80 years, regardless of gender
- Written informed consent obtained from the patient or legally authorized representative after full protocol disclosure
- Preoperative imaging (CT/MRI) confirming clinical stage T1a or select T1b renal tumors suitable for partial nephrectomy (R.E.N.A.L. nephrometry score ≤10)
- Localized renal tumors without lymph node/distant metastasis per NCCN Guidelines® (v2023)
- Elective minimally invasive partial nephrectomy (laparoscopic/robotic) after comprehensive surgical counseling
Exclusion Criteria:
- Multifocal renal tumors (bilateral or unilateral)
- Prior systemic anticancer therapy (targeted agents/immunotherapy/chemotherapy) within 6 months
- Absolute surgical contraindications (e.g., ASA class ≥IV, uncontrolled coagulopathy)
- Intraoperative conversion to radical nephrectomy or open approach
- Postoperative adjuvant therapy during protocol-defined follow-up (12 months)
- Major comorbidities (e.g., NYHA class III/IV heart failure, eGFR <30 mL/min/1.73m²) affecting outcome assessment
- Concurrent enrollment in interventional clinical trials
- Investigator-determined ineligibility based on risk-benefit analysis