[K25] Translational XR and AI Technologies with Dynamic Soft Robotic Hearts for Immersive Clinical Training and Future Intraoperative Guidance in Interventional Cardiology
Ente: National Heart Lung and Blood Institute
Scadenza: 2030-07-31
Importo max: 100.805 EUR
Paese: US
Descrizione
Project Summary
This K25 project aims to revolutionize structural heart intervention training and procedural guidance by
developing an advanced extended reality (XR) platform integrated with AI, soft robotics, and fluoroscopy-
compatible systems to address critical clinical challenges in procedures like atrial septal defect (ASD) closure,
left atrial appendage (LAA) closure, and transcatheter mitral valve repair (TMVR). Current 2D fluoroscopy lacks
depth perception, increases radiation exposure, and poses nephrotoxicity risks from contrast agents,
compromising patient safety. The proposed research builds on prior work developing a real-time 3D catheter
tracking XR system with submillimeter accuracy, introducing a clinically impactful solution to enhance
procedural precision and realism. Structured in two phases, the project addresses these limitations through
innovative technology.
Phase I (Aims 1 & 2) develops an off-cathlab, radiation-free training system. Aim 1 creates an XR
platform with a flexible haptic device for realistic catheter manipulation within patient-specific 3D cardiac
models, simulating anatomical resistance to reduce training-related radiation exposure. Aim 2 quantifies user
learning behaviors and psychomotor strategies using motion tracking, catheter trajectories, and physiological
data (e.g., GSR, HR, HRV), comparing XR to 2D training to optimize skill acquisition and reduce cognitive load,
critical for clinical proficiency.
Phase II (Aims 3 & 4) advances to an on-cathlab, fluoroscopy-integrated system. Aim 3 designs a soft
robotic heart (SRH) model using compliant materials with pneumatic actuation to replicate cardiac dynamics,
integrated with AI-driven catheter pose estimation from biplane and monoplane fluoroscopy for realistic cath-
lab training. Aim 4 evaluates clinical readiness with ~25 interventional cardiologists and fellows, assessing
navigation accuracy, task efficiency, and perceived realism to align with clinical workflows.
The platform leverages my engineering expertise in soft robotics, AI, and XR to improve clinical outcomes
in interventional cardiology by enhancing spatial awareness, reducing reliance on fluoroscopy, and minimizing
procedural complications. By integrating haptic feedback, AI-enhanced imaging, and dynamic SRH models, the
research aims to lower learning curves, enhance skill retention, and improve procedural safety. The anticipated
clinical impact includes reduced complication rates, enhanced patient safety, and improved outcomes in
structural heart interventions. This work establishes a scalable, clinically adaptable training ecosystem, paving
the way for future intraoperative XR guidance systems and advancing smart minimally invasive technologies
(SMIT) for cardiovascular care.
Istituzione: WEILL MEDICAL COLL OF CORNELL UNIV
PI: Mohsen Annabestani
Progetto: 1K25HL186244-01
Settori: National Heart Lung and Blood Institute
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