Sanjeev Narasimhan

Papers

Total Citations

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About

Sanjeev Narasimhan is at the forefront of integrating artificial intelligence with robotic-assisted surgery, focusing on computer vision and deep learning to automate surgical analysis and improve patient outcomes. His primary research areas include surgical phase recognition, 3D tooltip tracking, and automated skill assessment—all critical for advancing the safety, efficiency, and training in minimally invasive procedures. His most cited work, "Surgical Phase Recognition in Inguinal Hernia Repair" (20 citations), establishes an AI-based baseline for analyzing workflow from 209 robotic-assisted surgery videos, demonstrating how deep learning can automatically identify surgical phases. In "Monocular 3D Tooltip Tracking in Robotic Surgery," he tackles the complex challenge of extracting precise 3D tool movements from single-camera video, a key step toward real-time feedback and enhanced surgical safety. His recent study on automatic assessment of robotic suturing (3 citations) shows how computer vision models can evaluate trainee proficiency, promising to transform surgical residency training. With a growing citation footprint, Narasimhan’s work is shaping the future of data-driven, AI-enhanced surgical practice.