Session: 16-01-01: Government Agency Student Poster Competition

Paper Number: 150253

150253 - Development of a Novel Robotic System for Semi-Autonomous Vascular Anastomosis 

Vascular anastomosis is a procedure where two blood vessels are connected together, typically using suture, and is critical in transplant and reconstructive surgeries, with millions of cases annually in the United States. Microvascular anastomosis, involving vessels 1mm in diameter or smaller, is particularly challenging, requiring specialized surgical training and exhibiting failure rates of up to 10%. The complexity and required expertise limit the accessibility of this procedure to patients who live near highly trained surgeons. Our research aims to address these limitations by developing a robotic system for performing microvascular anastomosis, potentially improving patient outcomes and increasing access to high-quality surgery.

As a foundational step, we developed a robotic system tailored for 5mm diameter vessels, utilizing imaging and actuation modalities that can be miniaturized to accommodate 1mm diameter vessels. This system comprises a robotic manipulator, a robotic suturing tool, an optical coherence tomography (OCT) imaging fiber, a Kuka Med robotic manipulator, and an Omnivision micro-camera.

The contributions of this paper include:

Integration of a robotic vessel manipulator into a semi-autonomous system for vessel handling, featuring a nitinol gripper for precise repositioning during suturing.

Creation of a unique dataset for missed suture detection, accompanied by a trained ResNet model.

Development of an OCT-based tissue edge detection system.

Formulation of an innovative workflow for performing semi-autonomous anastomosis.

The robotic manipulator ensures vessel repositioning within +/- 2.8 degrees, enhancing the accuracy of suturing. The Kuka Med robot and the attached robotic suturing tool, adapted from previous work on the Smart Tissue Autonomous Robot (STAR), are instrumental in suture placement. The OCT sensor detects vessel edges with a 90% success rate in scans, using template matching to distinguish between nitinol, tissue, and air. The micro-camera captures pre- and post-suturing images, which are analyzed by the ResNet50 model, achieving a binary classification accuracy of 96.5% on training data, 90.7% on validation data, and 87.0% on testing data.

The system is integrated using the Robot Operating System (ROS) and our semi-autonomous suturing routine. Validation was conducted through an ex-vivo study comparing the robot's performance to that of three surgeons. Each performed five anastomoses on porcine femoral arteries, evaluated for leak pressure, lumen reduction, bite depth variance, suture spacing variance, and time per stitch. The robot successfully completed all anastomoses, with 90% of sutures placed with no human intervention. Performance metrics indicated the robot's equivalence to the surgeons in all categories except time per stitch, where it was slower.

These results validate our system's capability to perform anastomosis and support our hypothesis that a robotic system can enhance microvascular anastomosis outcomes and accessibility. Future work will focus on miniaturizing the system for 1mm diameter vessels, improving accuracy, and reducing dependency on surgeons.

Presenting Author: Jesse Haworth Johns Hopkins University

Presenting Author Biography: Jesse Haworth is a PhD student at Johns Hopkins University, working under the guidance of Professors Axel Krieger and Russell Taylor in the field of medical robotics. Jesse's research focuses on developing autonomous robotic systems for performing microvascular anastomosis, a critical procedure in transplant and reconstructive surgeries. Additionally, Jesse collaborates with Professor Russell Taylor on virtual fixtures using cooperative robotics. With a background in medical device engineering, Jesse has experience in prototyping, developing, and bringing innovative medical technologies to market. Passionate about combining engineering and medicine, Jesse aims to contribute significantly to the field of surgical robotics, enhancing both patient care and the capabilities of medical professionals.

Authors:

Jesse Haworth Johns Hopkins University
Rishi Biswas Johns Hopkins University
Michael Kam Johns Hopkins University
Justin Opfermann Johns Hopkins Univesity
Yaning Wang Johns Hopkins University
Desire Pantalone University Hospital Careggi in Florence
Francis Creighton Johns Hopkins Medicine
Robin Yang Johns Hopkins Medicine
Jin Kang Johns Hopkins Univeristy
Axel Krieger Johns Hopkins Univeristy