Super-selective arterial clamping, targeting second-order renal artery branches, is a refined nephron-sparing technique that minimizes ischemic injury during partial nephrectomy. Preoperative visualization of tumor-specific vascular anatomy is crucial for safely implementing this approach. We report a case where artificial intelligence (AI)-enhanced 3D reconstruction was used to facilitate surgical planning for robotic partial nephrectomy in a patient with a complex renal tumor.

A 3D model of the renal tumor, renal arterial anatomy down to second-order branches, collecting system, and parenchyma was created using Synapse 3D software with AI-assisted segmentation. The model enabled the identification of the tumor-feeding artery and guided a super-selective clamping strategy during robotic-assisted partial nephrectomy.

The surgery was completed successfully with a total operative time of 150 minutes and a warm ischemia time of 20 minutes. The estimated blood loss was 50 mL. No intraoperative or postoperative complications were observed. The patient was discharged on postoperative day three. Renal function was preserved, with serum creatinine remaining stable (from 0.64 to 0.66 mg/dL). Final pathology confirmed negative surgical margins.

This case demonstrates the feasibility and clinical value of integrating AI-enhanced 3D modeling into preoperative planning for super-selective arterial clamping in partial nephrectomy. The technique offers improved surgical precision while preserving renal function in complex tumor cases.