NIH Reporter #1K25HL186244-01

Translational XR and AI Technologies with Dynamic Soft Robotic Hearts for Immersive Clinical Training and Future Intraoperative Guidance in Interventional Cardiology

Award to Weill Medical College of Cornell University to develop an advanced XR and AI platform with soft robotic hearts for immersive clinical training and intraoperative guidance in interventional cardiology, aiming to improve procedural safety and outcomes.

Description

This project aims to develop an advanced extended reality (XR) platform integrated with AI, soft robotics, and fluoroscopy-compatible systems to enhance training and procedural guidance for structural heart interventions such as ASD, LAA closure, and TMVR. The platform addresses limitations of 2D fluoroscopy, including radiation exposure, poor depth perception, and contrast-related risks, by providing realistic 3D visualization and soft robotic heart models. The project is structured in two phases: Phase I develops an off-cathlab, radiation-free training system with a flexible haptic device for realistic catheter manipulation within patient-specific 3D cardiac models and quantifies user learning behaviors and psychomotor strategies. Phase II advances to an on-cathlab, fluoroscopy-integrated system with a soft robotic heart model and AI-driven catheter pose estimation for realistic clinical training, including evaluation of clinical readiness with interventional cardiologists. The platform leverages expertise in soft robotics, AI, and XR to improve clinical outcomes by enhancing spatial awareness, reducing fluoroscopy reliance, and minimizing procedural complications, aiming to lower learning curves, improve skill retention, and enhance procedural safety, ultimately reducing complication rates and improving patient outcomes.

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