Evolution of autonomous and semi‐autonomous robotic surgical systems: a review of the literature

Moustris, George P.; Hiridis, S. C.; Deliparaschos, Kyriakos M.; Konstantinidis, Konstantinos

doi:10.1002/rcs.408

Cited by 259 publications

(153 citation statements)

References 138 publications

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“…[39][40][41] By placing SFE directly on a surgical tool, the poses of SFEs (camera) can be obtained from the robot system. Unlike previous studies that were solely based on images, this 3-D model can be generated in a more efficient and accurate way by simplifying the procedure of bundle adjustment.…”

Section: Three-dimensional Virtual Panoramic Views From Endoscopic Videomentioning

confidence: 99%

Accurate three-dimensional virtual reconstruction of surgical field using calibrated trajectories of an image-guided medical robot

Gong

Hannaford

et al. 2014

J. Med. Imag

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Abstract. Brain tumor margin removal is challenging because diseased tissue is often visually indistinguishable from healthy tissue. Leaving residual tumor leads to decreased survival, and removing normal tissue causes lifelong neurological deficits. Thus, a surgical robotics system with a high degree of dexterity, accurate navigation, and highly precise resection is an ideal candidate for image-guided removal of fluorescently labeled brain tumor cells. To image, we developed a scanning fiber endoscope (SFE) which acquires concurrent reflectance and fluorescence wide-field images at a high resolution. This miniature flexible endoscope was affixed to the arm of a RAVEN II surgical robot providing programmable motion with feedback control using stereo-pair surveillance cameras. To verify the accuracy of the three-dimensional (3-D) reconstructed surgical field, a multimodal physical-sized model of debulked brain tumor was used to obtain the 3-D locations of residual tumor for robotic path planning to remove fluorescent cells. Such reconstruction is repeated intraoperatively during margin clean-up so the algorithm efficiency and accuracy are important to the robotically assisted surgery. Experimental results indicate that the time for creating this 3-D surface can be reduced to one-third by using known trajectories of a robot arm, and the error from the reconstructed phantom is within 0.67 mm in average compared to the model design.

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Section: Three-dimensional Virtual Panoramic Views From Endoscopic Videomentioning

confidence: 99%

Accurate three-dimensional virtual reconstruction of surgical field using calibrated trajectories of an image-guided medical robot

Gong

Hannaford

et al. 2014

J. Med. Imag

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“…RSAs are being used for many tumorectomy interventions within the abdominal and thoracic cavities such as prostectomy and hysterectomy [2,25] as described in reviews of recent developments in semi-autonomous and autonomous execution of surgical procedures by Moustris et al [20] and Kranzfelder et al [14]. Automated Suturing: Automation of suturing has been studied in the context of hierarchical models for multi-step task planning [11], multilateral manipulation of needle and suture [34], and interaction with deformable tissue [8,9].…”

Section: Background and Related Workmentioning

confidence: 99%

Automating multi-throw multilateral surgical suturing with a mechanical needle guide and sequential convex optimization

Sen

Garg

Gealy

et al. 2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

151

101

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Abstract-For supervised automation of multi-throw suturing in Robot-Assisted Minimally Invasive Surgery, we present a novel mechanical needle guide and a framework for optimizing needle size, trajectory, and control parameters using sequential convex programming. The Suture Needle Angular Positioner (SNAP) results in a 3x error reduction in the needle pose estimate in comparison with the standard actuator. We evaluate the algorithm and SNAP on a da Vinci Research Kit using tissue phantoms and compare completion time with that of humans from the JIGSAWS dataset [5]. Initial results suggest that the dVRK can perform suturing at 30% of human speed while completing 86% suture throws attempted. Videos and data are available at: berkeleyautomation.github.io/amts

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“…Robotic surgical systems have been used in a wide variety of surgical interventions [1,5,22,33,37]. In this work, we use the da Vinci Research Kit (DVRK) [15,16], a research platform built from mechanical components from the firstgeneration of the da Vinci surgical system [4].…”

Section: Related Workmentioning

confidence: 99%

Learning by observation for surgical subtasks: Multilateral cutting of 3D viscoelastic and 2D Orthotropic Tissue Phantoms

Murali

Sen

Kehoe

et al. 2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

161

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Abstract-Automating repetitive surgical subtasks such as suturing, cutting and debridement can reduce surgeon fatigue and procedure times and facilitate supervised tele-surgery. Programming is difficult because human tissue is deformable and highly specular. Using the da Vinci Research Kit (DVRK) robotic surgical assistant, we explore a "Learning By Observation" (LBO) approach where we identify, segment, and parameterize sub-trajectories ("surgemes") and sensor conditions to build a finite state machine (FSM) for each subtask. The robot then executes the FSM repeatedly to tune parameters and if necessary update the FSM structure. We evaluate the approach on two surgical subtasks: debridement of 3D Viscoelastic Tissue Phantoms (3d-DVTP), in which small target fragments are removed from a 3D viscoelastic tissue phantom, and Pattern Cutting of 2D Orthotropic Tissue Phantoms (2d-PCOTP), a step in the standard Fundamentals of Laparoscopic Surgery training suite, in which a specified circular area must be cut from a sheet of orthotropic tissue phantom. We describe the approach and physical experiments, which yielded a success rate of 96% for 50 trials of the 3d-DVTP subtask and 70% for 20 trials of the 2d-PCOTP subtask.

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Evolution of autonomous and semi‐autonomous robotic surgical systems: a review of the literature

Abstract: Autonomous and semi-autonomous systems are beginning to emerge in various interventions, automating important steps of the operation. These systems are expected to become standard modality and revolutionize the face of surgery.

Cited by 259 publications

References 138 publications

Accurate three-dimensional virtual reconstruction of surgical field using calibrated trajectories of an image-guided medical robot

Accurate three-dimensional virtual reconstruction of surgical field using calibrated trajectories of an image-guided medical robot

Automating multi-throw multilateral surgical suturing with a mechanical needle guide and sequential convex optimization

Learning by observation for surgical subtasks: Multilateral cutting of 3D viscoelastic and 2D Orthotropic Tissue Phantoms

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