Augmented Dexterity for Surgical Suturing: STITCH Performs Needle Insertion, Thread Coordination, and Handoffs

Alapfogalmak

STITCH, an augmented dexterity pipeline, performs Suture Throws Including Thread Coordination and Handoffs using novel perception and control techniques.

Kivonat

The STITCH pipeline automates the surgical suturing task by iteratively performing needle insertion, thread sweeping, needle extraction, suture cinching, needle handover, and needle pose correction with failure recovery policies.
The key components of the STITCH pipeline are:

6D Needle Pose Estimation Module: STITCH uses a combination of deep learning, analytical, and sampling-based approaches to accurately estimate the 6D pose of the surgical needle. This enables closed-loop control of the needle motions.

Augmented Dexterity Suturing Motion Controller: STITCH coordinates the various sub-tasks of the suturing process, including needle insertion, thread sweeping, needle extraction, suture cinching, needle handover, and needle pose correction. Novel motion primitives are introduced to improve reliability.

Motion Failure Recovery: STITCH includes recovery mechanisms to handle failures during needle extraction and handover, retrying the motions up to 5 times before declaring failure.

In physical experiments across 15 trials, STITCH achieved an average of 2.93 successful sutures without human intervention and 4.47 successful sutures with human intervention. The pipeline demonstrates the potential of augmented dexterity to enhance surgical capabilities.

Statisztikák

STITCH achieves an average single-suture success rate of 69.39% and a mean sutures-to-failure of 2.93 over 15 trials.
With human intervention, STITCH achieves a single-suture success rate of 83.33% and a mean sutures-to-failure of 4.47.

Idézetek

"STITCH iteratively performs needle insertion, thread sweeping, needle extraction, suture cinching, needle handover, and needle pose correction with failure recovery policies."
"We introduce a novel visual 6D needle pose estimation framework using a stereo camera pair and new suturing motion primitives."

Főbb Kivonatok

STITCH

by Kush Hari,Ha... : arxiv.org 04-09-2024

https://arxiv.org/pdf/2404.05151.pdf

Mélyebb kérdések

How can the STITCH pipeline be extended to handle more complex surgical scenarios, such as deformable tissues or varying lighting conditions?

To extend the STITCH pipeline for more complex surgical scenarios, several enhancements can be implemented:

Deformable Tissues:

Integrate real-time tissue deformation feedback into the perception system to adapt to changes in tissue shape during surgery.
Implement force sensing capabilities to provide haptic feedback to the robot, allowing it to adjust its movements based on tissue resistance.
Incorporate machine learning algorithms to predict tissue behavior and optimize suturing motions accordingly.

Varying Lighting Conditions:

Develop robust lighting compensation algorithms to ensure consistent image quality for accurate perception despite changes in lighting.
Utilize multispectral imaging techniques to capture a broader range of light wavelengths, reducing the impact of varying lighting conditions.
Implement adaptive exposure control mechanisms to adjust camera settings dynamically based on the lighting environment.

Integration of AI:

Introduce reinforcement learning algorithms to enable the robot to adapt its suturing strategy based on real-time feedback and environmental changes.
Incorporate deep learning models for image enhancement and segmentation to improve needle tracking and pose estimation under challenging lighting conditions.
Implement self-supervised learning techniques to enhance the robot's ability to generalize across different tissue types and lighting scenarios.

By incorporating these advancements, the STITCH pipeline can become more versatile and capable of handling the complexities associated with deformable tissues and varying lighting conditions in surgical settings.

What are the potential ethical and regulatory considerations for increasing the autonomy of surgical robots in the operating room?

Patient Safety:

Ensuring that autonomous surgical robots meet stringent safety standards to minimize the risk of errors or adverse events during procedures.
Implementing fail-safe mechanisms and emergency stop protocols to enable human intervention in case of unexpected robot behavior.

Liability and Accountability:

Clarifying legal responsibilities in cases of robot malfunctions or errors, including determining liability between the manufacturer, healthcare provider, and medical staff.
Establishing clear guidelines for informed consent from patients regarding the use of autonomous robots in surgical procedures.

Data Privacy and Security:

Safeguarding patient data collected and processed by autonomous surgical systems to prevent unauthorized access or breaches.
Ensuring compliance with data protection regulations such as HIPAA to protect patient confidentiality and privacy.

Transparency and Explainability:

Requiring transparency in the decision-making processes of autonomous robots to enable clinicians to understand and trust the actions taken by the system.
Implementing mechanisms for explaining the rationale behind the robot's decisions to facilitate human oversight and intervention when necessary.

Regulatory Approval:

Obtaining regulatory approval from agencies such as the FDA for the use of autonomous surgical robots, demonstrating their safety, efficacy, and reliability through rigorous testing and validation.

Addressing these ethical and regulatory considerations is crucial to the responsible deployment of autonomous surgical robots in the operating room, ensuring patient safety, data security, and legal compliance.

How could the insights from the STITCH pipeline be applied to enhance human-robot collaboration in other domains beyond surgery?

Manufacturing:

Implementing similar perception and control techniques to enable robots to collaborate with human workers in assembly and manufacturing processes.
Enhancing handover and coordination capabilities to improve efficiency and safety in shared workspace scenarios.

Logistics and Warehousing:

Applying the motion primitives and interactive perception concepts from STITCH to optimize pick-and-place tasks in logistics and warehouse operations.
Facilitating seamless collaboration between robots and human operators for order fulfillment and inventory management.

Construction:

Leveraging the insights from the STITCH pipeline to develop robotic systems that can assist construction workers in tasks such as bricklaying, welding, and painting.
Enhancing robot autonomy and adaptability to work alongside human workers in dynamic and unstructured environments.

Search and Rescue:

Utilizing the perception and motion control techniques from STITCH to enhance robotic systems for search and rescue missions in hazardous or disaster scenarios.
Improving human-robot coordination for locating and extracting survivors in complex and unpredictable environments.

By applying the principles and methodologies from the STITCH pipeline to other domains, it is possible to enhance human-robot collaboration, increase productivity, and improve safety across a wide range of industries beyond surgery.