A Tension Sensor Array for Cable-Driven Surgical Robots

Tendon-sheath structures are commonly utilized to drive surgical robots due to their compact size, flexibility, and straightforward controllability. However, long-distance cable tension estimation poses a significant challenge due to its frictional characteristics affected by complicated factors. This paper proposes a miniature tension sensor array for an endoscopic cable-driven parallel robot, aiming to integrate sensors into the distal end of long and flexible surgical instruments to sense cable tension and alleviate friction between the tendon and sheath. The sensor array, mounted at the distal end of the robot, boasts the advantages of a small size (16 mm outer diameter) and reduced frictional impact. A force compensation strategy was presented and verified on a platform with a single cable and subsequently implemented on the robot. The robot demonstrated good performance in a series of palpation tests, exhibiting a 0.173 N average error in force estimation and a 0.213 N root-mean-square error. In blind tests, all ten participants were able to differentiate between silicone pads with varying hardness through force feedback provided by a haptic device.

The current state of autonomous suturing: a systematic review

BACKGROUND

Robotic technology is an important tool in surgical innovation, with robots increasingly being used in the clinical setting. Robots can be used to enhance accuracy, perform remote actions, or to automate tasks. One such surgical task is suturing, a repetitive, fundamental component of surgery that can be tedious and time consuming. Suturing is a promising automation target because of its ubiquity, repetitive nature, and defined constraints. This systematic review examines research to date on autonomous suturing.

METHODS

A systematic review of the literature focused on autonomous suturing was conducted in accordance with PRISMA guidelines.

RESULTS

6850 articles were identified by searching PubMed, Embase, Compendex, and Inspec. Duplicates and non-English articles were removed. 4389 articles were screened and 4305 were excluded. Of the 84 remaining, 43 articles did not meet criteria, leaving 41 articles for final review. Among these, 34 (81%) were published after 2014. 31 (76%) were published in an engineering journal9 in a robotics journal, and 1 in a medical journal. The great majority of articles (33, 80%) did not have a specific clinical specialty focus, whereas 6 (15%) were focused on applications in MIS/laparoscopic surgery and 2 (5%) on applications in ophthalmology. Several suturing subtasks were identified, including knot tying, suture passing/needle insertion, needle passing, needle and suture grasping, needle tracking/kinesthesia, suture thread detection, suture needle shape production, instrument assignment, and suture accuracy. 14 articles were considered multi-component because they referred to several previously mentioned subtasks.

CONCLUSION

In this systematic review exploring research to date on autonomous suturing, 41 articles demonstrated significant progress in robotic suturing. This summary revealed significant heterogeneity of work, with authors focused on different aspects of suturing and a multitude of engineering problems. The review demonstrates increasing academic and commercial interest in surgical automation, with significant technological advances toward feasibility.

Performance and Capability Assessment in Surgical Subtask Automation

Robot-Assisted Minimally Invasive Surgery (RAMIS) has reshaped the standard clinical practice during the past two decades. Many believe that the next big step in the advancement of RAMIS will be partial autonomy, which may reduce the fatigue and the cognitive load on the surgeon by performing the monotonous, time-consuming subtasks of the surgical procedure autonomously. Although serious research efforts are paid to this area worldwide, standard evaluation methods, metrics, or benchmarking techniques are still not formed. This article aims to fill the void in the research domain of surgical subtask automation by proposing standard methodologies for performance evaluation. For that purpose, a novel characterization model is presented for surgical automation. The current metrics for performance evaluation and comparison are overviewed and analyzed, and a workflow model is presented that can help researchers to identify and apply their choice of metrics. Existing systems and setups that serve or could serve as benchmarks are also introduced and the need for standard benchmarks in the field is articulated. Finally, the matter of Human-Machine Interface (HMI) quality, robustness, and the related legal and ethical issues are presented.

Autonomous robotic laparoscopic surgery for intestinal anastomosis

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.

Hyper-Redundant Manipulator Capable of Adjusting Its Non-Uniform Curvature with Discrete Stiffness Distribution

Hyper-redundant manipulators are widely used in minimally invasive surgery because they can navigate through narrow passages in passive compliance with the human body. Although their stability and dexterity have been significantly improved over the years, we need manipulators that can bend with appropriate curvatures and adapt to complex environments. This paper proposes a design principle for a manipulator capable of adjusting its non-uniform curvature and predicting the bending shape. Rigid segments were serially stacked, and elastic fixtures in the form of flat springs were arranged between hinged-slide joint segments. A manipulator with a diameter of 4.5 mm and a length of 28 mm had been fabricated. A model was established to predict the bending shape through minimum potential energy theory, kinematics, and measured stiffnesses of the flat springs. A comparison of the simulation and experimental results indicated an average position error of 3.82% of the endpoints when compared to the total length. With this modification, the manipulator is expected to be widely used in various fields such as small endoscope systems and single-port robot systems.