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About this paper
Harry Robertshaw, Lennart Karstensen, Benjamin Jackson, Hadi Sadati, Kawal Rhode, Sebastien Ourselin, Alejandro Granados, Thomas C Booth
This systematic review analyzes the current state of Artificial Intelligence in autonomous navigation for endovascular interventions, identifying that the field is predominantly at an experimental proof-of-concept stage (TRL 3) and relies heavily on reinforcement learning. It highlights the critical need for standardized experimental designs and clinically translatable tracking methods to facilitate future progression.
Key insights
- AI-driven autonomous navigation in endovascular interventions is currently at a Technology Readiness Level (TRL) of 3, signifying an experimental proof-of-concept phase with no clinical trials reported.
- Reinforcement Learning (RL) is the predominant AI methodology (64% of reviewed studies), often combined with Learning from Demonstration (LfD), with half of the systems achieving Conditional Autonomy (Level 3).
- A major limitation is the lack of standardized experimental designs, inconsistent performance metrics, and significant translational hurdles for current tracking and manipulation methods in clinical environments.
Summary by alphaXiv.
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