A. Krieger

M. E. Smith, N. Yilmaz, T. Watts et al.

Existing tracheal tumor resection methods often lack the precision required for effective airway clearance, and robotic advancements offer new potential for autonomous resection. We present a vision-guided, autonomous approach for palliative resection of tracheal tumors. This system models the tracheal surface with a fifth-degree polynomial to plan tool trajectories, while a custom Faster R-CNN segmentation pipeline identifies the trachea and tumor boundaries. The electrocautery tool angle is optimized using handheld surgical demonstrations, and trajectories are planned to maintain a 1 mm safety clearance from the tracheal surface. We validated the workflow successfully in five consecutive experiments on ex-vivo animal tissue models, successfully clearing the airway obstruction without trachea perforation in all cases (with more than 90% volumetric tumor removal). These results support the feasibility of an autonomous resection platform, paving the way for future developments in minimally-invasive autonomous resection.

J. Yu, H. Yi, Y. Wang et al.

Deep Anterior Lamellar Keratoplasty (DALK) is a partial-thickness corneal transplant procedure used to treat corneal stromal diseases. A crucial step in this procedure is the precise separation of the deep stroma from Descemet's membrane (DM) using the Big Bubble technique. To simplify the tasks of needle insertion and pneumo-dissection in this technique, we previously developed an Optical Coherence Tomography (OCT)-guided, eye-mountable robot that uses real-time tracking of corneal layers from M-mode OCT signals for control. However, signal noise and instability during manipulation of the OCT fiber sensor-integrated needle have hindered the performance of conventional deep-learning segmentation methods, resulting in rough and inaccurate detection of corneal layers. To address these challenges, we have developed a topology-based deep-learning segmentation method that integrates a topological loss function with a modified network architecture. This approach effectively reduces the effects of noise and improves segmentation speed, precision, and stability. Validation using in vivo, ex vivo, and hybrid rabbit eye datasets demonstrates that our method outperforms traditional loss-based techniques, providing fast, accurate, and robust segmentation of the epithelium and DM to guide surgery.

H. Saeidi, J. D. Opfermann, M. Kam et al.

Autonomous robotic surgery has the potential to provide efficacy, safety, and consistency independent of individual surgeons skill and experience. Autonomous soft-tissue surgery in unstructured and deformable environments is especially challenging as it necessitates intricate imaging, tissue tracking and surgical planning techniques, as well as a precise execution via highly adaptable control strategies. In the laparoscopic setting, soft-tissue surgery is even more challenging due to the need for high maneuverability and repeatability under motion and vision constraints. We demonstrate the first robotic laparoscopic soft tissue surgery with a level of autonomy of 3 out of 5, which allows the operator to select among autonomously generated surgical plans while the robot executes a wide range of tasks independently. We also demonstrate the first in vivo autonomous robotic laparoscopic surgery via intestinal anastomosis on porcine models. We compared the criteria including needle placement corrections, suture spacing, suture bite size, completion time, lumen patency, and leak pressure between the developed system, manual laparoscopic surgery, and robot-assisted surgery (RAS). The ex vivo results indicate that our system outperforms expert surgeons and RAS techniques in terms of consistency and accuracy, and it leads to a remarkable anastomosis quality in living pigs. These results demonstrate that surgical robots exhibiting high levels of autonomy have the potential to improve consistency, patient outcomes, and access to a standard surgical technique.