Enhancing Robotic-Assisted Medical Procedures with Mixed Reality: Intuitive Instrument Planning and Safe Human-Robot Collaboration

핵심 개념

A novel mixed reality framework that enables real-time planning and execution of medical instruments by providing 3D anatomical image overlay, human-robot collision detection, and an intuitive robot programming interface to improve the effectiveness of human-robot interactions in robotic-assisted medical systems.

초록

The proposed framework combines mixed reality (MR) technologies and human-robot mutual perception to enhance the planning and execution of medical instrument placement in robotic-assisted medical systems (RAMS).
The key components of the framework include:

Instrument Placement Planner:

Allows the operator to visualize 3D anatomical images (e.g., MRI) overlaid on the real-world scene and intuitively plan the target placement of medical instruments using a handheld clicker device.
Calculates the optimal pose of the instrument based on the planned target.

Collision Object Converter:

Tracks the operator's motion using a head-mounted display (HMD) and maps it to a virtual avatar.
Converts the avatar into simple geometric shapes to represent the operator as collision objects for the robot's trajectory planning.
Enables the robot to be aware of the operator's location and avoid potential collisions during execution.

Robot Programming Interface:

Integrates the planned instrument pose and the collision objects to generate a collision-free robot trajectory.
Allows the operator to preview the planned trajectory overlaid on the real-world scene and make adjustments if needed.
Executes the instrument placement once the operator confirms the trajectory.

The framework also includes an easy-to-use virtual-to-real calibration method that simplifies the process of aligning virtual content with the physical environment, improving the accuracy of the overlaid visualizations.
The feasibility of the proposed framework is evaluated through two medical use cases: 1) coil placement during transcranial magnetic stimulation (TMS) and 2) drill and injector device positioning during femoroplasty. The results demonstrate the system's potential to enhance the effectiveness and safety of human-robot interactions in various RAMS applications.

통계

The average translation error in the TMS coil placement was 1.88 ± 1.21 mm, and the average rotation error was 0.51 ± 0.51°.
The average translation error in the femoroplasty drill and injector device placement was 1.48 ± 0.53 mm, and the average rotation error was 0.42 ± 0.31°.

인용구

"The integration of anatomy visualization with robot programming interfaces, coupled with human-robot mutual perception (i.e., both the operator and the robot are aware of each other), has not been fully explored in RAMS."
"The framework enables the visualization of anatomical overlay and uses a hand-held device to facilitate the localization of anatomical targets."
"A converter is designed to transform the operator's avatar into detectable objects, allowing the robot to be aware of the operator's location and prevent possible collisions."

핵심 통찰 요약

On the Fly Robotic-Assisted Medical Instrument Planning and Execution Using Mixed Reality

by Letian Ai,Yi... 게시일 arxiv.org 04-10-2024

https://arxiv.org/pdf/2404.05887.pdf

더 깊은 질문

How can the proposed framework be extended to support multi-modal input and feedback for the operator, such as haptic or auditory cues, to further enhance the human-robot collaboration?

To enhance human-robot collaboration, the proposed framework can be extended to incorporate multi-modal input and feedback mechanisms. One way to achieve this is by integrating haptic feedback into the system. By incorporating haptic devices that provide tactile sensations to the operator, such as force feedback gloves or controllers, the operator can receive physical feedback related to the interaction with the robotic system. For example, when planning the trajectory of a medical instrument, the operator can feel resistance or vibrations that indicate potential collisions or alignment issues, improving the overall precision and safety of the procedure.
In addition to haptic feedback, auditory cues can also be integrated into the framework. Auditory feedback can provide real-time information to the operator about the status of the robotic system, alerts for critical events, or guidance on the next steps in the procedure. For instance, auditory cues can indicate when a trajectory has been successfully planned, warn of potential collisions, or provide confirmation of instrument placement.
By combining haptic and auditory cues with the existing visual feedback in the mixed reality environment, the operator can receive comprehensive and intuitive feedback from multiple sensory modalities. This multi-modal approach can enhance the operator's situational awareness, improve communication with the robotic system, and ultimately optimize the human-robot collaboration in medical procedures.

What are the potential challenges and limitations in deploying the framework in a real-world clinical setting, and how can they be addressed?

Deploying the proposed framework in a real-world clinical setting may face several challenges and limitations that need to be addressed for successful implementation:

Regulatory Compliance: Ensuring that the framework complies with regulatory standards and guidelines for medical devices and software in healthcare settings is crucial. This includes obtaining necessary approvals and certifications to guarantee the safety and efficacy of the system.

Data Security and Privacy: Protecting patient data and ensuring the security and privacy of sensitive medical information transmitted and processed by the framework is essential. Implementing robust data encryption, access controls, and compliance with healthcare data regulations can address these concerns.

Integration with Existing Systems: Compatibility with existing medical equipment, software, and workflows in clinical environments is vital. Seamless integration with hospital information systems, electronic health records, and surgical navigation tools should be ensured to facilitate adoption and usability.

Training and User Acceptance: Providing comprehensive training for healthcare professionals on how to use the framework effectively is critical. User acceptance testing and feedback from clinicians can help identify usability issues and refine the system to meet the specific needs of medical practitioners.

Maintenance and Support: Establishing a reliable maintenance and support system to address technical issues, software updates, and hardware maintenance is essential for the continuous operation of the framework in clinical settings.

By addressing these challenges through thorough planning, collaboration with healthcare stakeholders, and adherence to industry standards, the deployment of the framework in real-world clinical settings can be successful.

How might the integration of the proposed framework with other emerging technologies, such as artificial intelligence-powered surgical planning and decision support systems, further improve the overall effectiveness and safety of robotic-assisted medical procedures?

Integrating the proposed framework with artificial intelligence (AI)-powered surgical planning and decision support systems can significantly enhance the effectiveness and safety of robotic-assisted medical procedures in the following ways:

Enhanced Preoperative Planning: AI algorithms can analyze patient data, medical images, and surgical plans to optimize the preoperative planning process. By integrating AI-powered surgical planning tools with the framework, surgeons can benefit from automated assistance in determining optimal instrument trajectories, target locations, and procedural steps.

Real-time Decision Support: AI algorithms can provide real-time decision support during surgical procedures by analyzing data from the robotic system, patient vitals, and imaging feedback. This integration can help surgeons make informed decisions, adjust instrument placements, and respond to unexpected situations promptly, improving surgical outcomes and patient safety.

Predictive Analytics: AI algorithms can leverage historical data and real-time inputs to predict potential complications, optimize instrument placements, and recommend adjustments to the surgical plan. By integrating predictive analytics capabilities with the framework, surgeons can proactively address challenges and minimize risks during robotic-assisted procedures.

Adaptive Control and Learning: AI-powered systems can adapt to the surgeon's preferences, patient-specific anatomical variations, and procedural complexities over time. By incorporating adaptive control and machine learning capabilities into the framework, the robotic system can continuously improve its performance, adjust to changing conditions, and enhance the overall efficiency and safety of medical procedures.

By integrating the proposed framework with AI-powered technologies, healthcare providers can leverage advanced analytics, automation, and intelligent decision-making capabilities to optimize robotic-assisted medical procedures, streamline workflows, and ultimately deliver better patient outcomes.