A Computational Framework for Complementary Situational Awareness (CSA) in Surgical Assistant Robots

Chalasani, Preetham; Deguet, Anton; Kazanzides, Peter; Taylor, Russell H.

doi:10.1109/irc.2018.00011

Cited by 14 publications

(6 citation statements)

References 30 publications

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“…The final category of publications relates to technologies that can enhance the surgeon's awareness [31], [211] and perception from a visual point of view; for example, using eye-gaze trackers [76], [78] to personalize a surgeon's experience or combining different imaging techniques, such as ultrasounds [72], [181], to provide more intraoperative visual information but also from tactile sensing [120]- [122], [160], [251]. A significant research effort also targeted improving the teleoperation capabilities of the dVRK, taking into account time delay [23], possible master-slave misalignment [64], constrained workspace [35], shared control [41], or incorporating virtual environment guidance [55].…”

Section: Surgical Workflow Optimizationmentioning

confidence: 99%

Accelerating Surgical Robotics Research: A Review of 10 Years With the da Vinci Research Kit

D'Ettorre

Mariani

Stilli

et al. 2021

IEEE Robot. Automat. Mag.

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This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination.R obotic-assisted surgery is now well established in clinical practice and has become the gold-standard clinical treatment option for several clinical indications. The field of robotic-assisted surgery is expected to grow substantially in the next decade, with a range of new robotic devices emerging to address unmet clinical needs across different specialties. A vibrant surgical robotics research community is pivotal for conceptualizing such new systems as well as for developing and training the engineers and scientists to translate them into practice. The da Vinci Research Kit (dVRK), an academic and industry collaborative effort to repurpose decommissioned da Vinci surgical systems [Intuitive Surgical Inc. (ISI), California, USA] as a research platform for surgical robotics research, has been a key initiative for addressing a barrier to entry for new research groups in surgical robotics. In this article, we present an extensive review of the publications that have been facilitated by the dVRK over the past decade. We classify research efforts into different categories and outline some of the major challenges and needs for the robotics community to maintain and build upon this initiative.

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Section: Surgical Workflow Optimizationmentioning

confidence: 99%

Accelerating Surgical Robotics Research: A Review of 10 Years With the da Vinci Research Kit

D'Ettorre

Mariani

Stilli

et al. 2021

IEEE Robot. Automat. Mag.

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“…The elasticity and damping coefficients of the haptics feedback are set to 50 N/m and 5 N m/s , respectively. The software framework is built upon the CSA library developed at Johns Hopkins University [28]. To validate the effectiveness of the proposed frameworks, three experiments were conducted, as discussed below.…”

Section: B Teleoperated Frameworkmentioning

confidence: 99%

Hybrid Robot-Assisted Frameworks for Endomicroscopy Scanning in Retinal Surgeries

Shahbazi

Patel

et al. 2020

IEEE Trans. Med. Robot. Bionics

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High-resolution real-time imaging at cellular level in retinal surgeries is very challenging due to extremely confined space within the eyeball and lack of appropriate modalities. Probe-based confocal laser endomicroscopy (pCLE) system, which has a small footprint and provides highly-magnified images, can be a potential imaging modality for improved diagnosis. The ability to visualize in cellular-level the retinal pigment epithelium and the chorodial blood vessels underneath can provide useful information for surgical outcomes in conditions such as retinal detachment. However, the adoption of pCLE is limited due to narrow field of view and micron-level range of focus. The physiological tremor of surgeons' hand also deteriorate the image quality considerably and leads to poor imaging results.In this paper, a novel image-based hybrid motion control approach is proposed to mitigate challenges of using pCLE in retinal surgeries. The proposed framework enables shared control of the pCLE probe by a surgeon to scan the tissue precisely without hand tremors and an auto-focus image-based control algorithm that optimizes quality of pCLE images. The control strategy is deployed on two semi-autonomous frameworks -cooperative and teleoperated. Both frameworks consist of the Steady-Hand Eye Robot (SHER), whose end-effector holds the pCLE probe. The teleoperated framework also uses the da Vinci Research Kit (dVRK), which enables the user to remotely control the pCLE probe. The frameworks have been evaluated through experiments and a series of user studies involving 14 participants. Statistical analyses have been conducted and it is shown that the proposed hybrid approach results in higher image quality, smoother motion, and reduced workload in a statistically significant manner.

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“…Artificial intelligence (AI) applications in RAS are emerging research topics, such as autonomy [6], surgeon skill evaluation [7], workflow analysis [8], etc. The use of AI in robotic surgical systems helps to enable passive virtual fixtures [9], presentation of advisory information [10], and the automation of surgical tasks [11], This paper was recommended for publication by Editor Pietro Valdastri upon evaluation of the Associate Editor and Reviewers' comments. This work was supported by Intuitive Surgical Inc.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Hierarchical Surgical State Estimation During Robot-Assisted Surgery Through Deep Neural Networks

Qin

Allan

Burdick³

et al. 2021

IEEE Robot. Autom. Lett.

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Many operations in robot-assisted surgery (RAS) can be viewed in a hierarchical manner. Each surgical task is represented by a superstate, which can be decomposed into finer-grained states. The estimation of these discrete states at different levels of temporal granularity provides a temporal perception of the current surgical scene during RAS, which is a crucial step towards many automated surgeon-assisting functionalities. We propose Hierarchical Estimation of Surgical States through Deep Neural Networks (HESS-DNN), a deep learningbased system that concurrently estimates the current super-and fine-grained states. HESS-DNN incorporates endoscopic vision, robot kinematics, and system events data from the da Vinci ® Xi surgical system. HESS-DNN is evaluated on a real-world robotic inguinal hernia repair surgery dataset: HERNIA-20, and achieves accurate state estimates of both surgical superstate and the corresponding fine-grained surgical state. We show that HESS-DNN improves state-of-the-art fine-grained state estimation across the entire HERNIA-20 RAS procedure through its hierarchical design. We also analyze the relative contributions of each input data type and HESS-DNN's design to surgical (super)state estimation accuracy.

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A Computational Framework for Complementary Situational Awareness (CSA) in Surgical Assistant Robots

Cited by 14 publications

References 30 publications

Accelerating Surgical Robotics Research: A Review of 10 Years With the da Vinci Research Kit

Accelerating Surgical Robotics Research: A Review of 10 Years With the da Vinci Research Kit

Hybrid Robot-Assisted Frameworks for Endomicroscopy Scanning in Retinal Surgeries

Autonomous Hierarchical Surgical State Estimation During Robot-Assisted Surgery Through Deep Neural Networks

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