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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