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Summary

This summary is machine-generated.

Cardiovascular diseases cause many deaths. This study models catheterization hysteresis using a neural network, enabling autonomous robotic surgery and reducing surgeon workload.

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Area of Science:

Robotics
Biomedical Engineering
Artificial Intelligence

Background:

Cardiovascular diseases are a leading cause of global mortality.
Robotic catheterization offers potential for treating vascular conditions like aneurysms and atherosclerosis.
Current robotic systems struggle with autonomous control due to catheter hysteresis.

Purpose of the Study:

To develop a model for parameterizing hysteresis in robotic catheterization.
To enable autonomous control of endovascular tools by understanding hysteretic behaviors.
To reduce surgeon immersion and improve efficiency in vascular interventions.

Main Methods:

An autoregressive nonlinear neural network was adapted to model hysteresis.
Five key factors contributing to hysteresis were identified, estimated, and analyzed.
The model was validated using hysteresis data from a 2-DOF robotic system and an unactuated catheter.

Main Results:

The neural network model accurately parameterized vital causal factors of hysteresis.
Hysteretic behaviors of the endovascular tool were successfully modeled.
Validation with a vascular phantom demonstrated accurate hysteresis profile description.

Conclusions:

The developed neural network model effectively captures and predicts hysteresis in robotic catheterization.
This modeling is crucial for achieving autonomous control in vascular surgery.
The findings pave the way for more efficient and less invasive robotic vascular interventions.