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PURPOSE: Planning for bone tumor resection surgery is a technically demanding and time-consuming task, reliant on manual positioning of planar cuts in a virtual space. More elaborate cutting approaches may be possible through the use of surgical robots or patient-specific instruments; however, methods for preparing such a resection plan must be developed. METHODS: This work describes an automated approach for generating conformal bone tumor resection plans, where the resection geometry is defined by the convex hull of the tumor, and a focal point. The resection geometry is optimized using particle swarm, where the volume of healthy bone collaterally resected with the tumor is minimized. The approach was compared to manually prepared planar resection plans from an experienced surgeon for 20 tumor cases. RESULTS: It was found that algorithm-generated hull-type resections greatly reduced the volume of collaterally resected healthy bone. The hull-type resections resulted in statistically significant improvements compared to the manual approach (paired t test, p < 0.001). CONCLUSIONS: The described approach has potential to improve patient outcomes by reducing the volume of healthy bone collaterally resected with the tumor and preserving nearby critical anatomy.

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Robotic patients show great potential for helping to improve medical palpation training, as they can provide feedback that cannot be obtained in a real patient. They provide information about internal organ deformation that can significantly enhance palpation training by giving medical trainees visual insight based on the pressure they apply for palpation. This can be achieved by using computational models of abdomen mechanics. However, such models are computationally expensive, and thus unable to provide real-time predictions. In this work, we proposed an innovative surrogate model of abdomen mechanics by using machine learning (ML) and finite element (FE) modelling to virtually render internal tissue deformation in real time. We first developed a new high-fidelity FE model of the abdomen mechanics from computerized tomography (CT) images. We performed palpation simulations to produce a large database of stress distribution on the liver edge, an area of interest in most examinations. We then used artificial neural networks (ANNs) to develop the surrogate model and demonstrated its application in an experimental palpation platform. Our FE simulations took 1.5 h to predict stress distribution for each palpation while this only took a fraction of a second for the surrogate model. Our results show that our artificial neural network (ANN) surrogate has an accuracy of 92.6%. We also showed that the surrogate model is able to use the experimental input of palpation location and force to provide real-time projections onto the robotics platform. This enhanced robotics platform has the potential to be used as a training simulator for trainees to hone their palpation skills.

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Planning for bone tumor resection surgery is a technically demanding and time-consuming task, reliant on manual positioning of cutting planes (CPs). This work describes an automated approach for generating bone tumor resection plans, where the volume of healthy bone collaterally resected with the tumor is minimized through optimized placement of CPs. Particle swarm optimization calculates the optimal position and orientation of the CPs by introducing a single new CP to an existing resection, then optimizing all CPs to find the global minima. The bone bounded by all CPs is collaterally resected with the tumor. The approach was compared to manual resection plans from an experienced surgeon for 20 tumor cases. It was found that a greater number of CPs reduce the collaterally resected healthy bone, with diminishing returns on this improvement after five CPs. The algorithm-generated resection plan with equivalent number of CPs resulted in a statistically significant improvement over manual plans (paired t-test, p < 0.001). The described approach has potential to improve patient outcomes by reducing loss of healthy bone in tumor surgery while offering a surgeon multiple resection plan options.

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The field of robotic surgery has progressed from small teams of researchers repurposing industrial robots, to a competitive and highly innovative subsection of the medical device industry. Surgical robots allow surgeons to perform tasks with greater ease, accuracy, or safety, and fall under one of four levels of autonomy; active, semi-active, passive, and remote manipulator. The increased accuracy afforded by surgical robots has allowed for cementless hip arthroplasty, improved postoperative alignment following knee arthroplasty, and reduced duration of intraoperative fluoroscopy among other benefits. Cutting of bone has historically used tools such as hand saws and drills, with other elaborate cutting tools now used routinely to remodel bone. Improvements in cutting accuracy and additional options for safety and monitoring during surgery give robotic surgeries some advantages over conventional techniques. This article aims to provide an overview of current robots and tools with a common target tissue of bone, proposes a new process for defining the level of autonomy for a surgical robot, and examines future directions in robotic surgery.

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The piecewise affine (PWA) model represents an attractive model structure for approximating nonlinear systems. In this paper, a procedure for obtaining the PWA autoregressive exogenous (ARX) (autoregressive systems with exogenous inputs) models of nonlinear systems is proposed. Two key parameters defining a PWARX model, namely, the parameters of locally affine subsystems and the partition of the regressor space, are estimated, the former through a least-squares-based identification method using multiple models, and the latter using standard procedures such as neural network classifier or support vector machine classifier. Having obtained the PWARX model of the nonlinear system, a controller is then derived to control the system for reference tracking. Both simulation and experimental studies show that the proposed algorithm can indeed provide accurate PWA approximation of nonlinear systems, and the designed controller provides good tracking performance.

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