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

TL;DR

This paper introduces an AI-driven system that guides intra-cardiac echocardiography catheter manipulation in real-time, improving navigation accuracy and consistency during heart imaging procedures through a continuous closed-loop feedback mechanism.

Contribution

The study presents a novel AI-based view guidance system that models spatial relationships and operates in a closed-loop to assist operators without requiring specialized expertise.

Findings

Achieved 89% success rate in simulation tests with real clinical data.

Validated continuous guidance through semi-simulation human-in-the-loop experiments.

Demonstrated potential to improve ICE imaging procedure efficiency and accuracy.

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.