Tool-tissue Interaction Forces Research Articles

Tool-tissue interaction force in glioma surgery

Tool-tissue interaction force in glioma surgery

TransVFS: A spatio-temporal local–global transformer for vision-based force sensing during ultrasound-guided prostate biopsy

Robot-assisted prostate biopsy is a new technology to diagnose prostate cancer, but its safety is influenced by the inability of robots to sense the tool-tissue interaction force accurately during biopsy. Recently, vision based force sensing (VFS) provides a potential solution to this issue by utilizing image sequences to infer the interaction force. However, the existing mainstream VFS methods cannot realize the accurate force sensing due to the adoption of convolutional or recurrent neural network to learn deformation from the optical images and some of these methods are not efficient especially when the recurrent convolutional operations are involved. This paper has presented a Transformer based VFS (TransVFS) method by leveraging ultrasound volume sequences acquired during prostate biopsy. The TransVFS method uses a spatio-temporal local–global Transformer to capture the local image details and the global dependency simultaneously to learn prostate deformations for force estimation. Distinctively, our method explores both the spatial and temporal attention mechanisms for image feature learning, thereby addressing the influence of the low ultrasound image resolution and the unclear prostate boundary on the accurate force estimation. Meanwhile, the two efficient local–global attention modules are introduced to reduce 4D spatio-temporal computation burden by utilizing the factorized spatio-temporal processing strategy, thereby facilitating the fast force estimation. Experiments on prostate phantom and beagle dogs show that our method significantly outperforms existing VFS methods and other spatio-temporal Transformer models. The TransVFS method surpasses the most competitive compared method ResNet3dGRU by providing the mean absolute errors of force estimation, i.e., 70.4 ± 60.0 millinewton (mN) vs 123.7 ± 95.6 mN, on the transabdominal ultrasound dataset of dogs.

FBG-based three-dimensional micro-force sensor with axial force sensitivity-enhancing and temperature compensation for micro-forceps.

During retinal microsurgery, excessive interaction force between surgical instruments and intraocular tissue can cause serious accidents such as tissue injury, irreversible retinal damage, and even vision loss. It is essential to accurately sense the micro tool-tissue interaction force, especially for the Ophthalmic Microsurgery Robot. In this study, a fiber Bragg grating (FBG) three-dimensional (3-D) micro-force sensor for micro-forceps is proposed, which is integrated with the drive module as an end-effector and can be conveniently mounted onto the ophthalmic surgical robot. An innovative axial force sensitivity-enhancing structure is proposed based on the principles of flexure-hinge and flexible levers to overcome the low sensitivity of axial force measurement. A dual-grating temperature compensation method is adopted for axial force measurement, which considers the differential temperature sensitivity of the two FBGs. Three FBGs are arranged along the circumference of the guide tube in this study to measure transverse forces and compensate for effects caused by changes in temperature. The experimental results demonstrate that the micro-forceps designed in this study achieved a resolution of 0.13 mN for transverse force and 0.30 mN for axial force. The temperature compensation experiments show that the 3-D micro-force sensor can simultaneously compensate for temperature effects in axial and transverse force measurement.

Safe Manipulation in Robotic Surgery Using Compliant Constant-Force Mechanism

Safe manipulation of soft tissue in robotic-assisted minimally invasive surgery (RMIS) is a challenging task for surgeons. The conventional solution is to introduce tactile sensors to measure the tool-tissue interaction force, and then transmit the signal back to the surgeon to enable manual force control. However, since the RMIS-forceps is very small, even the hand tremor of the surgeon could lead to unstable force applied to the tissue. To cope with this problem, we present a compliant constant-force mechanism (CFM) in this article to achieve stable force regulation of RMIS forceps. The proposed CFM is comprised of a pair of symmetrical compliant slider-crank mechanisms, whose mechanical behavior can be described by a pseudo-rigid-body (PRB) model. Cross-spring-based compliant joints are also introduced to improve the model accuracy, while the magnitude of the generated constant force can be adjusted by using detachable slider-crank mechanisms with different force configurations. In this work, the proposed CFM is selective-laser-sintered with polyamide (PA2200) and is actuated by a linear motor. Experiments were also conducted and successfully validated the constant-force performance of the proposed CFM. Furthermore, a grasping test of artificial artery was also performed, which demonstrated the application of the developed CFM system in RMIS.

Tool-tissue force segmentation and pattern recognition for evaluating neurosurgical performance

Surgical data quantification and comprehension expose subtle patterns in tasks and performance. Enabling surgical devices with artificial intelligence provides surgeons with personalized and objective performance evaluation: a virtual surgical assist. Here we present machine learning models developed for analyzing surgical finesse using tool-tissue interaction force data in surgical dissection obtained from a sensorized bipolar forceps. Data modeling was performed using 50 neurosurgery procedures that involved elective surgical treatment for various intracranial pathologies. The data collection was conducted by 13 surgeons of varying experience levels using sensorized bipolar forceps, SmartForceps System. The machine learning algorithm constituted design and implementation for three primary purposes, i.e., force profile segmentation for obtaining active periods of tool utilization using T-U-Net, surgical skill classification into Expert and Novice, and surgical task recognition into two primary categories of Coagulation versus non-Coagulation using FTFIT deep learning architectures. The final report to surgeon was a dashboard containing recognized segments of force application categorized into skill and task classes along with performance metrics charts compared to expert level surgeons. Operating room data recording of > 161 h containing approximately 3.6 K periods of tool operation was utilized. The modeling resulted in Weighted F1-score = 0.95 and AUC = 0.99 for force profile segmentation using T-U-Net, Weighted F1-score = 0.71 and AUC = 0.81 for surgical skill classification, and Weighted F1-score = 0.82 and AUC = 0.89 for surgical task recognition using a subset of hand-crafted features augmented to FTFIT neural network. This study delivers a novel machine learning module in a cloud, enabling an end-to-end platform for intraoperative surgical performance monitoring and evaluation. Accessed through a secure application for professional connectivity, a paradigm for data-driven learning is established.

A force-sensing surgical drill for real-time force feedback in robotic mastoidectomy.

Robotic assistance in otologic surgery can reduce the task load of operating surgeons during the removal of bone around the critical structures in the lateral skull base. However, safe deployment into the anatomical passageways necessitates the development of advanced sensing capabilities to actively limit the interaction forces between the surgical tools and critical anatomy. We introduce a surgical drill equipped with a force sensor that is capable of measuring accurate tool-tissue interaction forces to enable force control and feedback to surgeons. The design, calibration and validation of the force-sensing surgical drill mounted on a cooperatively controlled surgical robot are described in this work. The force measurements on the tip of the surgical drill are validated with raw-egg drilling experiments, where a force sensor mounted below the egg serves as ground truth. The average root mean square error for points and path drilling experiments is 41.7 (± 12.2) mN and 48.3 (± 13.7) mN, respectively. The force-sensing prototype measures forces with sub-millinewton resolution and the results demonstrate that the calibrated force-sensing drill generates accurate force measurements with minimal error compared to the measured drill forces. The development of such sensing capabilities is crucial for the safe use of robotic systems in a clinical context.

Optical force estimation for interactions between tool and soft tissues

Robotic assistance in minimally invasive surgery offers numerous advantages for both patient and surgeon. However, the lack of force feedback in robotic surgery is a major limitation, and accurately estimating tool-tissue interaction forces remains a challenge. Image-based force estimation offers a promising solution without the need to integrate sensors into surgical tools. In this indirect approach, interaction forces are derived from the observed deformation, with learning-based methods improving accuracy and real-time capability. However, the relationship between deformation and force is determined by the stiffness of the tissue. Consequently, both deformation and local tissue properties must be observed for an approach applicable to heterogeneous tissue. In this work, we use optical coherence tomography, which can combine the detection of tissue deformation with shear wave elastography in a single modality. We present a multi-input deep learning network for processing of local elasticity estimates and volumetric image data. Our results demonstrate that accounting for elastic properties is critical for accurate image-based force estimation across different tissue types and properties. Joint processing of local elasticity information yields the best performance throughout our phantom study. Furthermore, we test our approach on soft tissue samples that were not present during training and show that generalization to other tissue properties is possible.

Multilevel Modelling for Surgical Tool Calibration Using LINEX Loss Function

Quantifying the tool–tissue interaction forces in surgery can be utilized in the training of inexperienced surgeons, assist them better use surgical tools and avoid applying excessive pressures. The voltages read from strain gauges are used to approximate the unknown values of implemented forces. To this objective, the force-voltage connection must be quantified in order to evaluate the interaction forces during surgery. The progress of appropriate statistical learning approaches to describe the link between the genuine force applied on the tissue and numerous outputs obtained from sensors installed on surgical equipment is a key problem. In this study, different probabilistic approaches are used to evaluate the realized force on tissue using voltages read from strain gauges, including bootstrapping, Bayesian regression, weighted least squares regression, and multi-level modelling. Estimates from the proposed models are more precise than the maximum likelihood and restricted maximum likelihood techniques. The suggested methodologies are proficient of assessing tool-tissue interface forces with an adequate level of accuracy.

Tool-tissue Forces in Surgery: A Systematic Review

Abstract Background Excessive tool-tissue interaction forces often result in tissue damage and intraoperative complications, while insufficient forces prevent the completion of the task. This review sought to explore the tool-tissue interaction forces exerted by instruments during surgery across different specialities, tissues, manoeuvres and experience levels. Materials & methods A PRISMA-guided systematic review was carried out using Embase, Medline and Web of Science databases. Results Of 462 articles screened, 45 studies discussing surgical tool-tissue forces were included. The studies were categorized into 9 different specialities with the mean of average forces lowest for ophthalmology (0.04N) and highest for orthopaedic surgery (210N). Nervous tissue required the least amount of force to manipulate (mean of average: 0.4N), whilst connective tissue (including bone) required the most (mean of average: 45.8). For manoeuvres, drilling recorded the highest forces (mean of average: 14N), whilst sharp dissection recorded the lowest (mean of average: 0.03N). When comparing differences in the mean of average forces between groups, novices exerted 22.7% more force than experts, and presence of a feedback mechanism (e.g. audio) reduced exerted forces by 47.9%. Conclusions The measurement of tool-tissue forces is a novel but rapidly expanding field. The range of forces applied varies according to surgical speciality, tissue, manoeuvre, operator experience and feedback provided. Knowledge of the safe range of surgical forces will improve surgical safety whilst maintaining effectiveness. Measuring forces during surgery may provide an objective metric for training and assessment. Development of smart instruments, robotics and integrated feedback systems will facilitate this.

Three-Dimensional Force Perception of Robotic Bipolar Forceps for Brain Tumor Resection

Abstract Three-dimensional force perception is critically important in the enhancement of human force perception to minimize brain injuries resulting from excessive forces applied by surgical instruments in robot-assisted brain tumor resection. And surgeons are not responsive enough to interpret tool-tissue interaction forces. In previous studies, various force measurement techniques have been published. In neurosurgical scenarios, there are still some drawbacks to these presented approaches to forces perception. Because of the narrow, and slim configuration of bipolar forceps, three-dimensional contact forces on forceps tips are not easy to be traced in real-time. Five fundamental acts of handling bipolar forceps are poking, opposing, pressing, opening, and closing. The first three acts independently correspond to the axial force of z, x, y. So, in this paper, typical interactions between bipolar forceps and brain tissues have been analyzed. A three-dimensional force perception technique to collect force data on bipolar forceps tips by installing three fiber Bragg grating sensors (FBGs) on each prong of bipolar forceps in real-time is proposed. Experiments using a tele-neurosurgical robot were performed on an in vitro pig brain. In the experiments, three-dimensional forces were tracked in real-time. It is possible to experience forces at a minimum of 0.01 N. The three-dimensional force perception range is 0–4 N. The calibrating resolution on x, y, and z, is 0.01, 0.03, 0.1 N, separately. According to our observation, the measurement accuracy precision is over 95%.

662 Tool-tissue Forces in Surgery: A Systematic Review

Abstract Introduction Excessive tool-tissue interaction forces in surgery may result in tissue damage and intraoperative complications, while insufficient forces prevent the completion of the task. Method A systematic review of studies exploring tool-tissue forces applied during surgery was performed in accordance with PRISMA guidelines. Results 45 studies discussing tool-tissue forces during surgical procedures or tasks were included. Mean forces per speciality were as follows: ophthalmology (0.04N), vascular (0.7N), neurosurgery (0.68N), cardiothoracic surgery (1.5N), general surgery (4.7N), otorhinolaryngology (8.5N), obstetrics & gynaecology (mean 8.7N), urology (9.8N) and orthopaedic surgery (210N). Senior surgeons applied 25% less force than novice surgeons, and feedback (haptic, visual or audio) reduced force application by 40% - across specialities and tasks. Nervous tissue required the least amount of force to manipulate (0.4N, n = 17), followed by epithelial (3.8N, n = 18), muscle (4.1N, n = 4) and connective tissue (45.8, n = 10). For manoeuvres, retraction-with-grasping recorded the highest forces (3.65N, n = 13), whilst vessel clamping recorded the lowest (0.5N, n = 2). Conclusions The measurement of tool-tissue forces is a novel but rapidly expanding field. Knowledge of the safe range of surgical forces will improve surgical safety whilst maintaining effectiveness. Measuring surgical forces may provide an objective metric for training and assessment. Development of smart instruments, robotics and integrated feedback systems will facilitate this.

Calibration of surgical tools using multilevel modeling with LINEX loss function: Theory and experiment.

Quantifying the tool–tissue interaction forces in surgery can be used in the training process of novice surgeons to help them better handle surgical tools and avoid exerting excessive forces. A significant challenge concerns the development of proper statistical learning techniques to model the relationship between the true force exerted on the tissue and several outputs read from sensors mounted on the surgical tools. We propose a nonparametric bootstrap technique and a Bayesian multilevel modeling methodology to estimate the true forces. We use the linear exponential loss function to asymmetrically penalize the over and underestimation of the applied forces to the tissue. We incorporate the direction of the force as a group factor in our analysis. A weighted approach is used to account for the nonhomogeneity of read voltages from the surgical tool. Our proposed Bayesian multilevel models provide estimates that are more accurate than those under the maximum likelihood and restricted maximum likelihood approaches. Moreover, confidence bounds are much narrower and the biases and root mean squared errors are significantly smaller in our multilevel models with the linear exponential loss function.

A Three-Axial Force Sensor Based on Fiber Bragg Gratings for Surgical Robots

Haptic feedback is absent in flexible endoscopic surgical robots due to the size constraint of installing sensors on the small robotic arms. Besides, inherent hysteresis caused by the nonlinear friction between tendons and sheaths makes it hard to estimate the distal force by modeling. In this work, we addressed this challenge by proposing a new three-axial force sensor. This standalone device can be seamlessly integrated into the endoscopic robotic arm. Three optical fibers with fiber Bragg gratings (FBGs) are embedded in the sensing structure, where one is located at the center hole of the structure (ø 1.4 mm) and the other two are eccentrically placed around the structure at 90° apart from each other. This device can measure the pulling force and lateral forces of an articulated surgical instrument. Mechanics analysis has been studied to reveal the link between FBGs’ wavelength shifts and forces caused by the elongation and the bending with a temperature-compensation feature. The sensor has a lateral force sensitivity of 838.386 pm/N, with a measurement resolution of 1.19 mN. Performance comparison with a commercial force sensor Nano17 was made, with measurement errors from 4.50% to 6.18%. In the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ex vivo</i> tests, we teleoperated the sensorized grasper to pull, steer, and lift a piece of pig colon tissue. The tool–tissue interaction forces measured by the force sensor were displayed on the computer screen in real time. In addition to the endoscopic robots, the force sensor can also be integrated with other surgical robots such as laparoscopic robots and catheters.

Functional Grading of a Transversely Isotropic Hyperelastic Model with Applications in Modeling Tricuspid and Mitral Valve Transition Regions.

Surgical simulators and injury-prediction human models require a combination of representative tissue geometry and accurate tissue material properties to predict realistic tool–tissue interaction forces and injury mechanisms, respectively. While biological tissues have been individually characterized, the transition regions between tissues have received limited research attention, potentially resulting in inaccuracies within simulations. In this work, an approach to characterize the transition regions in transversely isotropic (TI) soft tissues using functionally graded material (FGM) modeling is presented. The effect of nonlinearities and multi-regime nature of the TI model on the functional grading process is discussed. The proposed approach has been implemented to characterize the transition regions in the leaflet (LL), chordae tendinae (CT) and the papillary muscle (PM) of porcine tricuspid valve (TV) and mitral valve (MV). The FGM model is informed using high resolution morphological measurements of the collagen fiber orientation and tissue composition in the transition regions, and deformation characteristics predicted by the FGM model are numerically validated to experimental data using X-ray diffraction imaging. The results indicate feasibility of using the FGM approach in modeling soft-tissue transitions and has implications in improving physical representation of tissue deformation throughout the body using a scalable version of the proposed approach.

A Comparison of Manual and Robot Assisted Retinal Vein Cannulation in Chicken Chorioallantoic Membrane.

Retinal vein occlusion (RVO) is a vision threatening condition occurring in the central or the branch retinal veins. Risk factors include but are not limited to hypercoagulability, thrombus or other cause of low blood flow. Current clinically proven treatment options limit complications of vein occlusion without treating the causative occlusion. In recent years, a more direct approach called Retinal Vein Cannulation (RVC) has been explored both in animal and human eye models. Though RVC has demonstrated potential efficacy, it remains a challenging and risky procedure that demands precise needle manipulation to achieve safely. During RVC, a thin cannula (diameter 70-110 µm) is delicately inserted into a retinal vein. Its intraluminal position is maintained for up to 2 minutes while infusion of a therapeutic drug occurs. Because the tool-tissue interaction forces at the needle tip are well below human tactile perception, a robotic assistant combined with a force sensing microneedle could alleviate the challenges of RVC. In this paper we present a comparative study of manual and robot assisted retinal vein cannulation in chicken chorioallantoic membrane (CAM) using a force sensing microneedle tool. The results indicate that the average puncture force and average force during the infusion period are larger in manual mode than in robot assisted mode. Moreover, retinal vein cannulation was more stable during infusion, in robot assisted mode.

Camera-Based Optical-Fiber Tactile Sensor for Intraoperative Grasping Force Measurement

Introduction During minimally invasive surgery, surgeons insert specially-designed instruments through a small incision into the patient’s body. Despite all the advantages of this procedure, surgeons do not have the natural tactile force feedback in the surgery. Tactile feedback helps the surgeon to apply an appropriate force to avoid tissue damage [1]. Therefore, researchers have proposed different optical force sensors to attach at the tip of surgical instruments for measuring the tool-tissue interaction force [2]. To this end, a new sensing principle, i.e., variable bending radius (VBR) for light intensity modulation (LIM) in optical-fiber-based sensor was proposed and experimentally validated in our previous study [3]. This method allowed further miniaturization of MIS force sensors and increased its sensitivity. In the current study, a miniaturized VBR-based force sensor was fabricated using rapid prototyping techniques. The sensor was mounted on a custom-designed surgical grasper and an RGB camera was used to measure the transmitting light through the optical fiber. Finally, the calibrated sensor was studied for multiple grasping tasks on freshly excised bovine tissue. Material and Method As depicted in Fig. 1, the sensor has a flexible shell with a semi-circular indenter on the top of the optical fiber. Upon any contact between the flexible shell and tissue, the shell bends downward and the semi-circular indenter applies a force F on the optical fiber. Since optical fiber is anchored from both ends, it behaves as an Euler-Bernoulli beam under an external contact force. The sensor was 3D printed with an SLA printer with flexible and transparent resins. In order to acquire the output light intensity at the end of the optical fiber, an image-based intensity measurement system was developed. To this end, a Logitech C920 camera was used and a projection was 3D printed. The chamber was designed in such a way that the projection spot of laser in optical fiber would be visible in camera as depicted in Fig. 2(a). Noh et al. [4] have shown that average greyscale values, σ, of the image would be proportional to the total intensity of the laser spot and was defined as (1) where σi is the greyscale value of i-th pixel, and n = 640x480 was the total number of pixels in each frame. Fig. 2(b) depicts the variation of brightness of the laser spot as the sinusoidal force is applied on the sensor. Due to the bending of optical fiber, light intensity modulation happens as external force increases. In previous studies showed that the percentage of power decay in fiber is non-linearly dependent on the applied external force F. Also due to the viscoelastic properties of the 3D printed materials, the power decay is dependent on the rate of application of F and which would show its effects on the rate of change in output power. Therefore, a nonlinear rate-dependent learning-based support vector regression (SVR) calibration was trained with the data from a sinusoidal compression test (0-2N) with 0.5, 1.0, and 2.0Hz frequency. Fig. 2(c) depicts the training test setup. To further assess the performance of the proposed sensor in real surgical procedures, an ex-vivo test on freshly excised bovine muscle was performed (Fig. 3(a)). Also, to have a reference for the contact force between the jaws of the sensor and tissue, a FlexiForce sensor was calibrated and attached to the upper jaw of the sensorized grasper. Afterward, the tissue was manually grasped in the MIS grasper, while reference force was recorded through an Arduino MEGA2560 interface. In parallel, the force was estimated using the SVR formula. Results and Conclusions During the sinusoidal compression tests, the average greyscale value of the image taken by the camera decreased from 27- to 2-unit for 0 to 2N compressive force. SVR estimations showed an adjusted-R2 = 0.94 with a root-mean-square error of 0.09N between the testing machine measurements and SVR estimations. Also, SVR force estimations in the ex-vivo test showed fair agreement with the force readings from FlexiForce sensor (Fig. 3 (b)). In the ex-vivo validation test, the minimum detectable force of the sensor was 0.14N and the average error was 0.11±0.06N. As indicated in [1], force sensors for MIS applications must show a minimum detectable force of 0.2N with an average error of less than 0.2N. Therefore, authors conclude that the proposed and fabricated sensor with the implemented camera-based intensity measurement and the employed learning-based calibration technique have satisfactorily contributed to an applicable optical MIS force sensor. To expand the scope of this study, it is suggested that the ex-vivo tests be performed in an aqueous environment to study the effects of electrolytic interferences. Also, robustness of the proposed sensor against the electromagnetic interferences is to be studied.

Sclera Force Evaluation During Vitreoretinal Surgeries in Ex Vivo Porcine Eye Model.

Vitreoretinal surgery is among the most challenging microsurgical procedures as it requires precise tool manipulation in a constrained environment, while the tool-tissue interaction forces are at the human perception limits. While tool tip forces are certainly important, the scleral forces at the tool insertion ports are also important. Clinicians often rely on these forces to manipulate the eyeball position during surgery. Measuring sclera forces could enable valuable sensory input to avoid tissue damage, especially for a cooperatively controlled robotic assistant that otherwise removes the sensation of these familiar intraoperative forces. Previously, our group has measured sclera forces in phantom experiments. However, to the best of our knowledge, there are no published data measuring scleral forces in biological (ex-vivo/in-vivo) eye models. In this paper, we measured sclera forces in ex-vivo porcine eye model. A Fiber Bragg Grating (FBG) based force sensing instrument with a diameter of ~900 μm and a resolution of ~1 mN was used to measure the forces while the clinician-subject followed retinal vessels in manual and robot-assisted modes. Analysis of measured forces show that the average sclera force in manual mode was 133.74 mN while in robot-assisted mode was 146.03 mN.

A sensorless force-feedback system for robot-assisted laparoscopic surgery

The existing surgical robots for laparoscopic surgery offer no or limited force feedback, and there are many problems for the traditional sensor-based solutions. This paper builds a teleoperation surgical system and validates the effectiveness of sensorless force feedback. The tool-tissue interaction force at the surgical grasper tip is estimated using the driving motor's current, and fed back to the master robot with a position-force bilateral control algorithm. The stiffness differentiation experiment and tumor detection experiment were conducted. In the stiffness differentiation experiment, 43 out of 45 pairs of ranking relationships were identified correctly, yielding a success rate of 96%. In the tumor detection experiment, 4 out of 5 participants identified the correct tumor location with force feedback, yielding a success rate of 80%. The proposed sensorless force-feedback system for robot-assisted laparoscopic surgery can help surgeons regain tactile information and distinguish between the healthy and cancerous tissue.

A sensorless force-feedback system for robot-assisted laparoscopic surgery

The existing surgical robots for laparoscopic surgery offer no or limited force feedback, and there are many problems for the traditional sensor-based solutions. This paper builds a teleoperation surgical system and validates the effectiveness of sensorless force feedback. The tool-tissue interaction force at the surgical grasper tip is estimated using the driving motor’s current, and fed back to the master robot with position-force bilateral control algorithm. The stiffness differentiation experiment and tumor detection experiment were conducted. In the stiffness differentiation experiment, 43 out of 45 pairs of ranking relationships were identified correctly, yielding a success rate of 96%. In the tumor detection experiment, 4 out of 5 participants identified the correct tumor location with force feedback, yielding a success rate of 80%. The proposed sensorless force-feedback system for robot-assisted laparoscopic surgery can help surgeons regain tactile information and distinguish between the healthy and cancerous tissue.

Development of a 2-DOF Sensorized Surgical Grasper for Grasping and Axial Force Measurements

Minimally invasive surgery (MIS) has been limited from its inception by insufficient haptic feedback to surgeons. To address this, various force sensing methods have been developed that can provide information about tool–tissue interaction forces. However, the results have been unreliable in most of these studies, since there are many sources of error and uncertainty in the force acquisition method. Furthermore, none of the force sensing devices described in the literature can be fully cleaned or sterilized for use in surgery. To address this, a sterilizable needle-driver type grasper is presented in this paper that incorporates fiber Bragg gratings to measure axial and grasping forces. The results of its performance evaluation show that the two-degrees of freedom grasper is capable of measuring grasping and axial forces with an accuracy of 0.27 and 0.3 N, respectively. Integration of this device into a needle driver instrument will result in a sterilizable instrument capable of measuring tool–tissue interaction forces during MIS.