AI-driven View Guidance System in Intra-cardiac Echocardiography Imaging

2024-09-25

arXiv.org

AI Summary

To address suboptimal image quality and inaccurate catheter positioning caused by limited experience of novice operators during intracardiac echocardiography (ICE), this study proposes a closed-loop human–machine collaborative navigation system. The method introduces a novel relative pose model formulated in a unified spatial coordinate system, integrating real-time ICE image geometric modeling, pose estimation, and predictive closed-loop control to deliver interpretable, stepwise catheter manipulation guidance aligned with clinically defined imaging views. By mapping discrete semantic commands to continuous physical actuation, the system enables non-expert physicians to autonomously achieve precise target viewpoints. In 6,532 simulated trials, the system achieved an 89% success rate in view transitions. A semi-physical human-in-the-loop experiment further validated its clinical feasibility and operational efficacy.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Intra-cardiac echocardiography (ICE) is a crucial imaging modality used in electrophysiology (EP) and structural heart disease (SHD) interventions, providing realtime, high-resolution views from within the heart. Despite its advantages, effective manipulation of the ICE catheter requires significant expertise, which can lead to inconsistent outcomes, especially among less experienced operators. To address this challenge, we propose an AIdriven view guidance system that operates in a continuous closed-loop with human-in-the-loop feedback, designed to assist users in navigating ICE imaging without requiring specialized knowledge. Specifically, our method models the relative position and orientation vectors between arbitrary views and clinically defined ICE views in a spatial coordinate system. It guides users on how to manipulate the ICE catheter to transition from the current view to the desired view over time. By operating in a closedloop configuration, the system continuously predicts and updates the necessary catheter manipulations, ensuring seamless integration into existing clinical workflows. The effectiveness of the proposed system is demonstrated through a simulation-based performance evaluation using real clinical data, achieving an 89% success rate with 6,532 test cases. Additionally, a semi-simulation experiment with human-in-the-loop testing validated the feasibility of continuous yet discrete guidance. These results underscore the potential of the proposed method to enhance the accuracy and efficiency of ICE imaging procedures.

Problem

• Intracardiac Echocardiography (ICE)
• Image Quality
• Operator Experience

Innovation

• Real-time Guidance
• Intracardiac Echocardiography (ICE)
• Artificial Intelligence System