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Robot-assisted flexible needle insertion using universal distributional deep reinforcement learning.

Xiaoyu Tan¹, Yonggu Lee², Chin-Boon Chng²

¹Department of Mechanical Engineering, National University of Singapore, Singapore, 117575, Singapore. xiaoyu_tan@u.nus.edu.

International Journal of Computer Assisted Radiology and Surgery

|November 27, 2019

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This study introduces a novel deep reinforcement learning framework for robot-assisted flexible needle insertion, enhancing surgical accuracy and robustness in procedures like biopsy and ablation.

Area of Science:

Robotics
Medical Technology
Artificial Intelligence

Background:

Flexible needle insertion is crucial for minimally invasive surgery, offering reduced trauma and faster recovery.
Challenges include unpredictable needle-tissue interactions, impacting robotic insertion precision and reliability.

Purpose of the Study:

To develop a robust path planning framework for robot-assisted flexible needle insertion.
To enhance the precision and reliability of procedures like biopsy and radio-frequency ablation.

Main Methods:

Utilized multi-goal deep reinforcement learning with universal distributional Q-learning (UDQL).
Implemented risk management through Q-value distribution visualization.
Enhanced generalization and re-planning capabilities via universal value function approximation.

Keywords:

Deep learning Deep reinforcement learning Needle steering Tool–tissue interaction Uncertainty

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Main Results:

Demonstrated secure flexible needle steering with high accuracy and robustness in simulations and phantom experiments.
Showcased improved robustness through distribution information aiding clinical decision-making.

Conclusions:

The proposed framework enables multi-target needle insertion from a single point with superior accuracy and robustness.
Outperforms previous methods in continuous state space models for flexible needle steering.