Rahul Ghosh

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About

Rahul Ghosh is a computational researcher at the intersection of medical imaging, geometric deep learning, and neurointerventional robotics. His primary research focuses on developing AI-driven visual perception systems for endovascular procedures, particularly the automated segmentation and tip detection of catheters and guidewires in cerebral angiography. Ghosh’s most cited work introduces a topology-aware geometric deep learning framework that enables robots to “see” intravascular devices in real-time—a critical step toward autonomous or teleoperated neurointervention. This paper has garnered 19 citations since 2023, reflecting its immediate impact on the emerging field of AI-augmented endovascular surgery. By addressing the fundamental challenge of device visibility in x-ray fluoroscopy, Ghosh’s contributions bridge computer vision and clinical robotics, offering a foundation for safer, more precise catheter navigation. His work is notable for combining topological data analysis with deep learning, a novel approach that enhances segmentation accuracy in complex anatomical environments. As a rising voice in medical AI, Ghosh’s research promises to accelerate the development of intelligent robotic systems that can assist neurointerventionalists, ultimately improving outcomes for patients with cerebrovascular disease.