Robots Outperform Expert Surgeons on Open Bowel Surgery in Pigs

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Robot outperformed expert surgeons and current robot-assisted surgical techniques in open bowel surgery in pigs. (stock image) Credit: © ralamst / Fotolia

Robot outperformed expert surgeons and current robot-assisted surgical techniques in open bowel surgery in pigs. (stock image) Credit: © ralamst / Fotolia

Putting surgery one step closer into the realm of self-driving cars and intelligent machines, researchers show for the first time that a supervised autonomous robot can successfully perform soft tissue surgery. The robot outperformed expert surgeons and current robot-assisted surgical techniques in open bowel surgery in pigs. By taking human intervention out of the equation, autonomous robots could potentially reduce complications and improve the safety and efficacy of soft tissue surgeries, about 45 million of which are performed in the U.S. each year.

Robot-assisted surgery currently relies on the surgeon to manually control it, and outcomes can vary depending on the individual’s training and experience. Efforts in automating surgery have made headway for hard tissues, such as in bone cutting, but have proven challenging for soft tissues, which are malleable and mobile and, thus, more unpredictable.

Automating soft tissue surgery. (A) The STAR system integrated NIRF and 3D plenoptic vision, force sensing, submillimeter positioning, and actuated surgical tools. (B) Surgical site detail during linear suturing task showing a longitudinally cut porcine intestine suspended by five stay sutures. (C) The tissue was first marked by syringe with biocompatible NIRF markers to track motions through blood and tissue occlusions. A 3D surface point cloud was then acquired with the plenoptic camera. The NIRF markers (now white) were projected onto the point cloud after offline registration of the 2D NIR image and the 3D point cloud data. The suture plan was computed as a sequence of coordinates and suture types (yellow knots and blue running sutures) to achieve leak-free anastomosis by overlapping stitch compression zones. (D) Point clouds of initial (blue) and deformed (red) tissue. STAR performance was then evaluated under random ex vivo deformations. Three deformations were induced on NIRF markers 2, 3, and 4 with an additional bowel rotation at the second deformation. m, marker. (E) Representative average marker deformation results from (D). Data are averages ± SD (n = 5 trials). (Additional combinations of induced deformations are in fig. S2.) (F) The 3D point cloud during end-to-end anastomosis before (blue) and after (red) deformations. The tissue was tracked by the combined NIRF and 3D plenoptic vision system. (G) The theory behind spacing of running suture is adapted from (21). The spacing was informed by the tissue thickness to ensure that the suture spacing S was smaller than bite size H. The zones of compression for each stitch, illustrated in pink, overlapped for leak-free anastomosis. A suture plan was initially created by interpolating between the tracked positions of NIRF markers and updated as the tissue deformed. On the right, the suture plan is overlaid on the bowel point cloud. Points outlined in green are knot locations. The plan for end-to-end anastomosis used two sutures. First, a running suture (red) was applied on the back wall (from right to left) after tying the first knot (green). At the left corner, the needle pierced the tissue from inside, which was later used to tie the final knot. Second, another running suture (blue) was applied on the back wall (left to right) after tying the second knot. Similarly, at the corner, the tissue was pierced from inside to outside. (H) Once the right corner was completed, the tissue was flipped manually, such that the edges of the front walls were close to each other. The suture plan calculated the running suture positions (blue) to continue from the right corner with the same suture.

Automating soft tissue surgery. (A) The STAR system integrated NIRF and 3D plenoptic vision, force sensing, submillimeter positioning, and actuated surgical tools. (B) Surgical site detail during linear suturing task showing a longitudinally cut porcine intestine suspended by five stay sutures. (C) The tissue was first marked by syringe with biocompatible NIRF markers to track motions through blood and tissue occlusions. A 3D surface point cloud was then acquired with the plenoptic camera. The NIRF markers (now white) were projected onto the point cloud after offline registration of the 2D NIR image and the 3D point cloud data. The suture plan was computed as a sequence of coordinates and suture types (yellow knots and blue running sutures) to achieve leak-free anastomosis by overlapping stitch compression zones. (D) Point clouds of initial (blue) and deformed (red) tissue. STAR performance was then evaluated under random ex vivo deformations. Three deformations were induced on NIRF markers 2, 3, and 4 with an additional bowel rotation at the second deformation. m, marker. (E) Representative average marker deformation results from (D). Data are averages ± SD (n = 5 trials). (Additional combinations of induced deformations are in fig. S2.) (F) The 3D point cloud during end-to-end anastomosis before (blue) and after (red) deformations. The tissue was tracked by the combined NIRF and 3D plenoptic vision system. (G) The theory behind spacing of running suture is adapted from (21). The spacing was informed by the tissue thickness to ensure that the suture spacing S was smaller than bite size H. The zones of compression for each stitch, illustrated in pink, overlapped for leak-free anastomosis. A suture plan was initially created by interpolating between the tracked positions of NIRF markers and updated as the tissue deformed. On the right, the suture plan is overlaid on the bowel point cloud. Points outlined in green are knot locations. The plan for end-to-end anastomosis used two sutures. First, a running suture (red) was applied on the back wall (from right to left) after tying the first knot (green). At the left corner, the needle pierced the tissue from inside, which was later used to tie the final knot. Second, another running suture (blue) was applied on the back wall (left to right) after tying the second knot. Similarly, at the corner, the tissue was pierced from inside to outside. (H) Once the right corner was completed, the tissue was flipped manually, such that the edges of the front walls were close to each other. The suture plan calculated the running suture positions (blue) to continue from the right corner with the same suture.

Azad Shademan and colleagues designed and programmed Smart Tissue Autonomous Robot (STAR) to perform complex surgical tasks. Equipped with a robotic arm and surgical tools, STAR combines smart imaging technologies and fluorescent markers to navigate and adapt to the complexities of soft tissue. The researchers tested their robot against manual surgery by expert surgeons, laparoscopy, and robot-assisted surgery with the da Vinci Surgical System.

Under supervision, STAR proved superior to all approaches in suturing and reconnecting bowel segments, known as intestinal anastomosis, both ex vivo and in vivo in pigs. The animals survived the operation with no complications. With further development, autonomous robotic surgery may one day take human error out of the operating room, improving care for patients undergoing bowel surgery, tumor removal, and other soft tissue surgery. http://www.eurekalert.org/pub_releases/2016-05/aaft-rsj050216.php   http://stm.sciencemag.org/content/8/337/337ra64.full