Autonomous robotic laparoscopic surgery for intestinal anastomosis

Abstract

Autonomous robotic surgery has the potential to provide efficacy, safety, and consistency independent of individual surgeon's skill and experience. Autonomous anastomosis is a challenging soft-tissue surgery task because it requires intricate imaging, tissue tracking, and surgical planning techniques, as well as a precise execution via highly adaptable control strategies often in unstructured and deformable environments. In the laparoscopic setting, such surgeries are even more challenging because of the need for high maneuverability and repeatability under motion and vision constraints. Here we describe an enhanced autonomous strategy for laparoscopic soft tissue surgery and demonstrate robotic laparoscopic small bowel anastomosis in phantom and in vivo intestinal tissues. This enhanced autonomous strategy allows the operator to select among autonomously generated surgical plans and the robot executes a wide range of tasks independently. We then use our enhanced autonomous strategy to perform in vivo autonomous robotic laparoscopic surgery for intestinal anastomosis on porcine models over a 1-week survival period. We compared the anastomosis quality criteria-including needle placement corrections, suture spacing, suture bite size, completion time, lumen patency, and leak pressure-of the developed autonomous system, manual laparoscopic surgery, and robot-assisted surgery (RAS). Data from a phantom model indicate that our system outperforms expert surgeons' manual technique and RAS technique in terms of consistency and accuracy. This was also replicated in the in vivo model. 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.

Fig. 1.. Enhanced autonomous laparoscopic soft tissue surgery.

(A) The components of the Smart Tissue Autonomous Robot (STAR) system including medical robotic arms, actuated surgical tools, and dual-channel NIR and 3D structured light endoscopic imaging system. (B) Control architecture of the enhanced autonomous control strategy for STAR.

Fig. 2.. Tissue motion tracking.

(A) The CNN-based breathing motion tracker. (B) Examples of the vertical motion of NIR marker during in vivo tests. (C) Robustness test configurations for the phantom conditions. (D) The accuracy test results for the breathing motion tracker via optical flow with fixed threshold (OF), optical flow with adjustable threshold (OA), and the CNN-based method (CNN).

Fig. 3.. The results of phantom end-to-end anastomosis via LAP (n = 4), RAS (n = 4), and STAR (n = 5).

(A) Suture hesitancy events per stitch (additional suturing attempts per stitch). (B) Task completion time. (C) Suture spacing. (D) consistency of suture spacing via the coefficient of variance (COV). (E) Suture bite depth. (F) Consistency of bite depth via the coefficient of variance (COV). (G) Representative examples of the phantom end-to-end anastomosis test via LAP, RAS, and STAR including three-dimensional flow fields within each sample.

Fig. 4.. Segmentation assisted landmark detection.

(A) Overall data processing scheme based on cascaded U-nets, (B) Final results on the whole testing set. Blue shows the intestine segmentation results, and pink shows the landmark heatmap results.

Fig. 5.. The results of in vivo experiments.

(A) Representative histology examples from each anastomosis tissue operated on with STAR (n = 4), and manual laparoscopic control test (n = 1). The approximate location of each anastomosis is indicated with an arrow. Dashed boxes near each anastomosis represent the location of the magnified images. (B) PMN cell as a surrogate measure of inflammation for each sample. (C) Representative examples of the anastomosis collected at necropsy for STAR and control tests.