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Abstract #4658

Accuracy requirements for an automated deep-learning-based slice prescription for cardiac MRI

Margarita Gorodezky1, Sandeep Kaushik1, Martin Janich1, and Gaspar Delso2
1GE Healthcare, Munich, Germany, 2GE Healthcare, Barcelona, Spain

Synopsis

Keywords: Acquisition Methods, Machine Learning/Artificial Intelligence, Automated plane prescription, workflow

Motivation: Automated plane prescription tools can make cardiac MRI more accessible, but their accuracy needs to be validated.

Goal(s): We aim to determine the accuracy requirements for an AI-driven automated prescription tool.

Approach: To determine the accuracy requirements for an AI-driven automated prescription tool we compare landmarks set by the tools to those set manually by operators with different levels of experience.

Results: The prototype can match the average performance of an operator group, outperforming the less experienced individuals.

Impact: To be reliable the performance of automated prescription tools needs to be established. Here an AI-driven automated prescription tool for cardiac MRI planes could match the average performance of an operator group, outperforming the less experienced individuals.

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Keywords