Elena De Momi

Force feedback in a piezoelectric linear actuator for neurosurgery

Force feedback in robotic minimally invasive surgery allows the human operator to manipulate tissues as if his/her hands were in contact with the patient organs. A force sensor mounted on the probe raises problems with sterilization of the overall surgical tool. Also, the use of off‐axis gauges introduces a moment that increases the friction force on the bearing, which can easily mask off the signal, given the small force to be measured.

Relationship between cutting errors and learning curve in computer-assisted total knee replacement.

Computer-assisted total knee replacement (TKR) has been shown to improve radiographic alignment. Continuous feedback from the navigation system allows accurate adjustment of the bone cuts, thus reducing errors. The aim of this study was to determine the impact of experience both with computer navigation and knee replacement surgery on the frequency of errors in intraoperative bone cuts and implant alignment. Three homogeneous patient groups undergoing computer assisted TKR were included in the study. Each group was treated by one of three surgeons with varying experience in computer-aided and knee replacement surgery. Surgeon A had extensive experience in knee replacement and computer-assisted surgery. Surgeon B was an experienced knee replacement surgeon. A general orthopaedic surgeon with limited knee replacement surgery experience performed all surgeries in group C. The cutting errors and the number of re-cuts were determined intraoperatively. The complications and mean surgical time were collected for each group. The postoperative frontal femoral component angle, frontal tibial component angle, hip–knee–ankle angle and component slopes were evaluated. The results showed that the number of cutting errors were lowest for TKR performed by the surgeon with experience in navigation. This difference was statistically significant when compared to the general orthopaedic surgeon. A statistically significant superior result was achieved in final mechanical axis alignment for the surgeon experienced in computer-guided surgery compared to the other two groups (179.3° compared to 178.9° and 178.1°). However, the total number of outliers was similar, with no statistically significant differences among the three surgeons. Experience with navigation significantly reduced the surgical time.

In-vitro experimental assessment of a new robust algorithm for hip joint centre estimation

Hip joint centre (HJC) localization is used in several biomedical applications, such as movement analysis and computer-assisted orthopaedic surgery. The purpose of this study was to validate in vitro a new algorithm (MC-pivoting) for HJC computation and to compare its performances with the state-of-the-art (least square approach-LSA). The MC-pivoting algorithm iteratively searches for the 3D coordinates of the point belonging to the femoral bone that, during the circumduction of the femur around the hip joint (pivoting), runs the minimum length trajectory. The algorithm was initialized with a point distribution that can be considered close to a Monte Carlo simulation sampling all around the LSA estimate. The performances of the MC-pivoting algorithm, compared with LSA, were evaluated with tests on cadavers. Dynamic reference frames were applied on both the femur and the pelvis and were tracked by an optical localizer. Results proved the algorithm accuracy (1.7mm+/-1.6, 2.3-median value+/-quartiles), reliability (smaller upper quartiles of the errors distribution with respect to LSA) and robustness (reduction of the errors also in case of large pelvis displacements).

Haptics in Robot-Assisted Surgery: Challenges and Benefits

Robotic surgery is transforming the current surgical practice, not only by improving the conventional surgical methods but also by introducing innovative robot-enhanced approaches that broaden the capabilities of clinicians. Being mainly of man-machine collaborative type, surgical robots are seen as media that transfer pre- and intraoperative information to the operator and reproduce his/her motion, with appropriate filtering, scaling, or limitation, to physically interact with the patient. The field, however, is far from maturity and, more critically, is still a subject of controversy in medical communities. Limited or absent haptic feedback is reputed to be among reasons that impede further spread of surgical robots. In this paper, objectives and challenges of deploying haptic technologies in surgical robotics are discussed, and a systematic review is performed on works that have studied the effects of providing haptic information to the users in major branches of robotic surgery. It attempts to encompass both classical works and the state-of-the-art approaches, aiming at delivering a comprehensive and balanced survey both for researchers starting their work in this field and for the experts.

Error mapping controller: a closed loop neuroprosthesis controlled by artificial neural networks

BackgroundThe design of an optimal neuroprostheses controller and its clinical use presents several challenges. First, the physiological system is characterized by highly inter-subjects varying properties and also by non stationary behaviour with time, due to conditioning level and fatigue. Secondly, the easiness to use in routine clinical practice requires experienced operators. Therefore, feedback controllers, avoiding long setting procedures, are required.MethodsThe error mapping controller (EMC) here proposed uses artificial neural networks (ANNs) both for the design of an inverse model and of a feedback controller. A neuromuscular model is used to validate the performance of the controllers in simulations. The EMC performance is compared to a Proportional Integral Derivative (PID) included in an anti wind-up scheme (called PIDAW) and to a controller with an ANN as inverse model and a PID in the feedback loop (NEUROPID). In addition tests on the EMC robustness in response to variations of the Plant parameters and to mechanical disturbances are carried out.ResultsThe EMC shows improvements with respect to the other controllers in tracking accuracy, capability to prolong exercise managing fatigue, robustness to parameter variations and resistance to mechanical disturbances.ConclusionDifferent from the other controllers, the EMC is capable of balancing between tracking accuracy and mapping of fatigue during the exercise. In this way, it avoids overstressing muscles and allows a considerable prolongation of the movement. The collection of the training sets does not require any particular experimental setting and can be introduced in routine clinical practice.

Automatic classification of epilepsy types using ontology-based and genetics-based machine learning

OBJECTIVES In the presurgical analysis for drug-resistant focal epilepsies, the definition of the epileptogenic zone, which is the cortical area where ictal discharges originate, is usually carried out by using clinical, electrophysiological and neuroimaging data analysis. Clinical evaluation is based on the visual detection of symptoms during epileptic seizures. This work aims at developing a fully automatic classifier of epileptic types and their localization using ictal symptoms and machine learning methods. METHODS We present the results achieved by using two machine learning methods. The first is an ontology-based classification that can directly incorporate human knowledge, while the second is a genetics-based data mining algorithm that learns or extracts the domain knowledge from medical data in implicit form. RESULTS The developed methods are tested on a clinical dataset of 129 patients. The performance of the methods is measured against the performance of seven clinicians, whose level of expertise is high/very high, in classifying two epilepsy types: temporal lobe epilepsy and extra-temporal lobe epilepsy. When comparing the performance of the algorithms with that of a single clinician, who is one of the seven clinicians, the algorithms show a slightly better performance than the clinician on three test sets generated randomly from 99 patients out of the 129 patients. The accuracy obtained for the two methods and the clinician is as follows: first test set 65.6% and 75% for the methods and 56.3% for the clinician, second test set 66.7% and 76.2% for the methods and 61.9% for the clinician, and third test set 77.8% for the methods and the clinician. When compared with the performance of the whole population of clinicians on the rest 30 patients out of the 129 patients, where the patients were selected by the clinicians themselves, the mean accuracy of the methods (60%) is slightly worse than the mean accuracy of the clinicians (61.6%). Results show that the methods perform at the level of experienced clinicians, when both the methods and the clinicians use the same information. CONCLUSION Our results demonstrate that the developed methods form important ingredients for realizing a fully automatic classification of epilepsy types and can contribute to the definition of signs that are most important for the classification.

Laparoscopic Tissue Retractor Based on Local Magnetic Actuation

Magnetic instruments for laparoscopic surgery have the potential to enhance triangulation and reduce invasiveness, as they can be rearranged inside the abdominal cavity and do not need a dedicated port during the procedure. Onboard actuators can be used to achieve a controlled and repeatable motion at the interface with the tissue. However, actuators that can fit through a single laparoscopic incision are very limited in power and do not allow performance of surgical tasks such as lifting an organ. In this study, we present a tissue retractor based on local magnetic actuation (LMA). This approach combines two pairs of magnets, one providing anchoring and the other transferring motion to an internal mechanism connected to a retracting lever. Design requirements were derived from clinical considerations, while finite element simulations and static modeling were used to select the permanent magnets, set the mechanism parameters, and predict the lifting and supporting capabilities of the tissue retractor. A three-tier validation was performed to assess the functionality of the device. First, the retracting performance was investigated via a benchtop experiment, by connecting an increasing load to the lever until failure occurred, and repeating this test for different intermagnetic distances. Then, the feasibility of liver resection was studied with an ex vivo experiment, using porcine hepatic tissue. Finally, the usability and the safety of the device were tested in vivo on an anesthetized porcine model. The developed retractor is 154 mm long, 12.5 mm in diameter, and weights 39.16 g. When abdominal wall thickness is 2 cm, the retractor is able to lift more than ten times its own weight. The model is able to predict the performance with a relative error of 9.06 ± 0.52%. Liver retraction trials demonstrate that the device can be inserted via laparoscopic access, does not require a dedicated port, and can perform organ retraction. The main limitation is the reduced mobility due to the length of the device. In designing robotic instrument for laparoscopic surgery, LMA can enable the transfer of a larger amount of mechanical power than what is possible to achieve by embedding actuators on board. This study shows the feasibility of implementing a tissue retractor based on this approach and provides an illustration of the main steps that should be followed in designing a LMA laparoscopic instrument.

Optically tracked multi-robot system for keyhole neurosurgery

Robotic systems have been introduced in surgery to increase the intervention accuracy. In this framework, the ROBOCAST system is an optically controlled multi-robot chain aimed at enhancing the accuracy of surgical probe insertion during keyhole neurosurgery procedures. The system is composed by three robots, connected as a multiple kinematic chain (serial, parallel and linear) totaling 13 degrees of freedom (DoFs) and is it is used to automatically align the probe onto the desired trajectory.

Adaptive Hands-On Control for Reaching and Targeting Tasks in Surgery

Cooperatively controlled robotic assistants can be used in surgery for the repetitive execution of targeting/reaching tasks, which require smooth motions and accurate placement of a tool inside a working area. A variable damping controller, based on a priori knowledge of the location of the surgical site, is proposed to enhance the physical human-robot interaction experience. The performance of this and of typical constant damping controllers is comparatively assessed using a redundant light-weight robot. Results show that it combines the positive features of both null (acceleration capabilities > 0.8m/s2) and optimal (mean pointing error < 1.5mm) constant damping controllers, coupled with smooth and intuitive convergence to the target (direction changes reduced by 30%), which ensures that assisted tool trajectories feel natural to the user. An application scenario is proposed for brain cortex stimulation procedures, where the surgeons intentions of motion are explicitly defined intra-operatively through an image-guided navigational system.

Multi kinect people detection for intuitive and safe human robot cooperation in the operating room

Microsoft Kinect cameras are widely used in robotics. The cameras can be mounted either to the robot itself (in case of mobile robotics) or can be placed where they have a good view on robots and/or humans. The use of cameras in the surgical operating room adds additional complexity in placing the cameras and adds the necessity of coping with a highly uncontrolled environment with occlusions and unknown objects. In this paper we present an approach that accurately detects humans using multiple Kinect cameras. Experiments were performed to show that our approach is robust to interference, noise and occlusions. It provides a good detection and identification rate of the user which is crucial for safe human robot cooperation.