The i2Snake Robotic Platform for Endoscopic Surgery.

Endoscopic procedures have transformed minimally invasive surgery as they allow the examination and intervention on a patient's anatomy through natural orifices, without the need for external incisions. However, the complexity of anatomical pathways and the limited dexterity of existing instruments, limit such procedures mainly to diagnosis and biopsies. This paper proposes a new robotic platform: the Intuitive imaging sensing navigated and kinematically enhanced ([Formula: see text]) robot that aims to improve the field of endoscopic surgery. The proposed robotic platform includes a snake-like robotic endoscope equipped with a camera, a light-source and two robotic instruments, supported with a robotic arm for global positioning and for insertion of the [Formula: see text] and a master interface for master-slave teleoperation. The proposed robotic platform design focuses on ergonomics and intuitive control. The control workflow was first validated in simulation and then implemented on the robotic platform. The results are consistent with the simulation and show the clear clinical potential of the system. Limitations such as tendon backlash and elongation over time will be further investigated by means of combined hardware and software solutions. In conclusion, the proposed system contributes to the field of endoscopic surgical robots and could allow to perform more complex endoscopic surgical procedures while reducing patient trauma and recovery time.

Gaze gesture based human robot interaction for laparoscopic surgery.

While minimally invasive surgery offers great benefits in terms of reduced patient trauma, bleeding, as well as faster recovery time, it still presents surgeons with major ergonomic challenges. Laparoscopic surgery requires the surgeon to bimanually control surgical instruments during the operation. A dedicated assistant is thus required to manoeuvre the camera, which is often difficult to synchronise with the surgeon's movements. This article introduces a robotic system in which a rigid endoscope held by a robotic arm is controlled via the surgeon's eye movement, thus forgoing the need for a camera assistant. Gaze gestures detected via a series of eye movements are used to convey the surgeon's intention to initiate gaze contingent camera control. Hidden Markov Models (HMMs) are used for real-time gaze gesture recognition, allowing the robotic camera to pan, tilt, and zoom, whilst immune to aberrant or unintentional eye movements. A novel online calibration method for the gaze tracker is proposed, which overcomes calibration drift and simplifies its clinical application. This robotic system has been validated by comprehensive user trials and a detailed analysis performed on usability metrics to assess the performance of the system. The results demonstrate that the surgeons can perform their tasks quicker and more efficiently when compared to the use of a camera assistant or foot switches.

A machine learning approach for real-time modelling of tissue deformation in image-guided neurosurgery.

OBJECTIVES: Accurate reconstruction and visualisation of soft tissue deformation in real time is crucial in image-guided surgery, particularly in augmented reality (AR) applications. Current deformation models are characterised by a trade-off between accuracy and computational speed. We propose an approach to derive a patient-specific deformation model for brain pathologies by combining the results of pre-computed finite element method (FEM) simulations with machine learning algorithms. The models can be computed instantaneously and offer an accuracy comparable to FEM models. METHOD: A brain tumour is used as the subject of the deformation model. Load-driven FEM simulations are performed on a tetrahedral brain mesh afflicted by a tumour. Forces of varying magnitudes, positions, and inclination angles are applied onto the brain's surface. Two machine learning algorithms-artificial neural networks (ANNs) and support vector regression (SVR)-are employed to derive a model that can predict the resulting deformation for each node in the tumour's mesh. RESULTS: The tumour deformation can be predicted in real time given relevant information about the geometry of the anatomy and the load, all of which can be measured instantly during a surgical operation. The models can predict the position of the nodes with errors below 0.3mm, beyond the general threshold of surgical accuracy and suitable for high fidelity AR systems. The SVR models perform better than the ANN's, with positional errors for SVR models reaching under 0.2mm. CONCLUSIONS: The results represent an improvement over existing deformation models for real time applications, providing smaller errors and high patient-specificity. The proposed approach addresses the current needs of image-guided surgical systems and has the potential to be employed to model the deformation of any type of soft tissue.

Vision-based deformation recovery for intraoperative force estimation of tool-tissue interaction for neurosurgery.

PURPOSE: In microsurgery, accurate recovery of the deformation of the surgical environment is important for mitigating the risk of inadvertent tissue damage and avoiding instrument maneuvers that may cause injury. The analysis of intraoperative microscopic data can allow the estimation of tissue deformation and provide to the surgeon useful feedback on the instrument forces exerted on the tissue. In practice, vision-based recovery of tissue deformation during tool-tissue interaction can be challenging due to tissue elasticity and unpredictable motion. METHODS: The aim of this work is to propose an approach for deformation recovery based on quasi-dense 3D stereo reconstruction. The proposed framework incorporates a new stereo correspondence method for estimating the underlying 3D structure. Probabilistic tracking and surface mapping are used to estimate 3D point correspondences across time and recover localized tissue deformations in the surgical site. RESULTS: We demonstrate the application of this method to estimating forces exerted on tissue surfaces. A clinically relevant experimental setup was used to validate the proposed framework on phantom data. The quantitative and qualitative performance evaluation results show that the proposed 3D stereo reconstruction and deformation recovery methods achieve submillimeter accuracy. The force-displacement model also provides accurate estimates of the exerted forces. CONCLUSIONS: A novel approach for tissue deformation recovery has been proposed based on reliable quasi-dense stereo correspondences. The proposed framework does not rely on additional equipment, allowing seamless integration with the existing surgical workflow. The performance evaluation analysis shows the potential clinical value of the technique.

Making the Leap: the Translation of Innovative Surgical Devices From the Laboratory to the Operating Room.

OBJECTIVE: To determine the rate and extent of translation of innovative surgical devices from the laboratory to first-in-human studies, and to evaluate the factors influencing such translation. SUMMARY BACKGROUND DATA: Innovative surgical devices have preceded many of the major advances in surgical practice. However, the process by which devices arising from academia find their way to translation remains poorly understood. METHODS: All biomedical engineering journals, and the 5 basic science journals with the highest impact factor, were searched between January 1993 and January 2000 using the Boolean search term "surgery OR surgeon OR surgical". Articles were included if they described the development of a new device and a surgical application was described. A recursive search of all citations to the article was performed using the Web of Science (Thompson-Reuters, New York, NY) to identify any associated first-in-human studies published by January 2015. Kaplan-Meier curves were constructed for the time to first-in-human studies. Factors influencing translation were evaluated using log-rank and Cox proportional hazards models. RESULTS: A total of 8297 articles were screened, and 205 publications describing unique devices were identified. The probability of a first-in-human at 10 years was 9.8%. Clinical involvement was a significant predictor of a first-in-human study (P = 0.02); devices developed with early clinical collaboration were over 6 times more likely to be translated than those without [RR 6.5 (95% confidence interval 0.9-48)]. CONCLUSIONS: These findings support initiatives to increase clinical translation through improved interactions between basic, translational, and clinical researchers.