Quantifying force and positional frequency bands in neurosurgical tasks

Tool-tissue forces in surgery: A systematic review

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.

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This review explores tool-tissue forces during surgery, a new and expanding field.

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Forces were lowest in ophthalmology (0.04N) and highest in orthopaedics (210N).

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Forces were lowest during sharp dissection (0.03N) and highest when drilling (14N).

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Being an expert (vs. novice) and having feedback mechanisms (e.g. haptic) reduced exerted forces.

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Development of force metrics will facilitate training, assessment & novel technology.

Data analytics interrogates robotic surgical performance using a microsurgery-specific haptic device

OBJECTIVES

With the increase in robot-assisted cases, recording the quantifiable dexterity of surgeons is essential for proficiency evaluations. The present study employs sensor-based kinematics and recorded surgeon experience for evaluating a new haptic device.

METHODS

Thirty surgeons performed a task simulating micromanipulation with neuroArmPLUS^HD and two commercially available hand-controllers. The surgical performance was evaluated based on subjective measures obtained from survey and objective features derived from the sensors. Statistical analyses were performed to assess the hand-controllers and regression analysis was used to identify the key features and develop a machine learning model for surgical skill assessment.

FINDINGS

MANCOVA tests on objective features demonstrated significance (α = 0.05) for time (p = 0.02), errors (p = 0.01), distance (p = 0.03), clutch incidents (p = 0.03), and forces (p = 0.00). The majority of metrics were in favor of neuroArmPLUS^HD. The surgeons found it smoother, more comfortable, less tiring, and easier to maneuver with more realistic force feedback. The ensemble machine learning model trained with 5-fold cross-validation showed an accuracy (SD) of 0.78 (0.15) in surgeon skill classification.

CONCLUSIONS

This study validates the importance of incorporating a superior haptic device in telerobotic surgery for standardization of surgical education and patient care.

Development of Phantom Material that Resembles Compression Properties of Human Brain Tissue for Training Models

There is a need to quantify and reproduce the mechanical behavior of brain tissue for a variety of applications from designing proper training models for surgeons to enabling research on the effectiveness of personal protective gear, such as football helmets. The mechanical response of several candidate phantom materials, including hydrogels and emulsions, was characterized and compared to porcine brain tissue under similar strains and strain rates. Some candidate materials were selected since their compositions were similar to brain tissue, such as emulsions that mimic the high content of lipids. Others, like silicone, were included since these are currently used as phantom materials. The mechanical response of the emulsion was closer to that of the native porcine brain tissue than the other candidates. The emulsions, created by addition of oil to a hydrogel, were able to withstand compressive strain greater than 40%. The addition of lipids in the emulsions also prevented the syneresis typically seen with hydrogel materials. This allowed the emulsion material to undergo freeze-thaw cycles with no significant change in their mechanical properties.

Challenges in developing a magnetic resonance-compatible haptic hand-controller for neurosurgical training

A haptic device is an actuated human-machine interface utilized by an operator to dynamically interact with a remote environment. This interaction could be virtual (virtual reality) or physical such as using a robotic arm. To date, different mechanisms have been considered to actuate the haptic device to reflect force feedback from the remote environment. In a low-force environment or limited working envelope, the control of some actuation mechanisms such as hydraulic and pneumatic may be problematic. In the development of a haptic device, challenges include limited space, high accuracy or resolution, limitations in kinematic and dynamic solutions, points of singularity, dexterity as well as control system development/design. Furthermore, the haptic interface designed to operate in a magnetic resonance imaging environment adds additional challenges related to electromagnetic interference, static/variable magnetic fields, and the use of magnetic resonance-compatible materials. Such a device would allow functional magnetic resonance imaging to obtain information on the subject's brain activity while performing a task. When used for surgical trainees, functional magnetic resonance imaging could provide an assessment of surgical skills. In this application, the trainee, located supine within the magnet bore while observing the task environment on a graphical user interface, uses a low-force magnetic resonance-compatible haptic device to perform virtual surgical tasks in a limited space. In the quest to develop such a device, this review reports the multiple challenges faced and their potential solutions. The review also investigates efforts toward prototyping such devices and classifies the main components of a magnetic resonance-compatible device including actuation and sensory systems and materials used.

Nonparametric bootstrap technique for calibrating surgical SmartForceps: theory and application

Knowledge of forces, exerted on the brain tissue during the performance of neurosurgical tasks, is critical for quality assurance, case rehearsal, and training purposes. Quantifying the interaction forces has been made possible by developing SmartForceps, a bipolar forceps retrofitted by a set of strain gauges. The forces are estimated using voltages read from strain gauges. We therefore need to quantify the force-voltage relationship to estimate the interaction forces during microsurgery. This problem has been addressed in the literature by following the physical and deterministic properties of the force-sensing strain gauges without obtaining the precision associated with each estimate. In this paper, we employ a probabilistic methodology by using a nonparametric Bootstrap approach to obtain both point and interval estimates of the applied forces at the tool tips, while the precision associated with each estimate is provided. To show proof-of-concept, the Bootstrap technique is employed to estimate unknown forces, and construct necessary confidence intervals using observed voltages in data sets that are measured from the performance of surgical tasks on a cadaveric brain. Results indicate that the Bootstrap technique is capable of estimating tool-tissue interaction forces with acceptable level of accuracy compared to the linear regression technique under the normality assumption.

Prevalence of haptic feedback in robot-mediated surgery: a systematic review of literature

With the successful uptake and inclusion of robotic systems in minimally invasive surgery and with the increasing application of robotic surgery (RS) in numerous surgical specialities worldwide, there is now a need to develop and enhance the technology further. One such improvement is the implementation and amalgamation of haptic feedback technology into RS which will permit the operating surgeon on the console to receive haptic information on the type of tissue being operated on. The main advantage of using this is to allow the operating surgeon to feel and control the amount of force applied to different tissues during surgery thus minimising the risk of tissue damage due to both the direct and indirect effects of excessive tissue force or tension being applied during RS. We performed a two-rater systematic review to identify the latest developments and potential avenues of improving technology in the application and implementation of haptic feedback technology to the operating surgeon on the console during RS. This review provides a summary of technological enhancements in RS, considering different stages of work, from proof of concept to cadaver tissue testing, surgery in animals, and finally real implementation in surgical practice. We identify that at the time of this review, while there is a unanimous agreement regarding need for haptic and tactile feedback, there are no solutions or products available that address this need. There is a scope and need for new developments in haptic augmentation for robot-mediated surgery with the aim of improving patient care and robotic surgical technology further.

Surgical Skill Assessment Using Motion Quality and Smoothness

OBJECTIVES

This article presents a quantitative technique to assess motion quality and smoothness during the performance of micromanipulation tasks common to surgical maneuvers. The objective is to investigate the effectiveness of the jerk index, a derivative of acceleration with respect to time, as a kinetostatic measure for assessment of surgical performance.

DESIGN

A surgical forceps was instrumented with a position tracker and accelerometer that allowed measurement of position and acceleration relative to tool motion. Participants were asked to perform peg-in-hole tasks on a modified O'Connor Dexterity board and a Tweezer Dexterity pegboard (placed inside a skull). Normalized jerk index was calculated for each individual task to compare smoothness of each group.

SETTING

This study was conducted at Project neuroArm, Cumming School of Medicine, the University of Calgary.

PARTICIPANTS

Four groups of participants (surgeons, surgery residents, engineers, and gamers) participated in the tests.

RESULTS

Results showed that the surgeons exhibited better jerk index performance in all tasks. Moreover, the residents experienced motions closer to the surgeons compared to the engineers and gamers. One-way analysis of variance test indicated a significant difference between the mean values of normalized jerk indices among 4 groups during the performance of all tasks. Moreover, the mean value of the normalized jerk index significantly varied for each group from one task to another.

CONCLUSIONS

Normalized jerk index as an independent parameter with respect to time and amplitude is an indicator of motion smoothness and can be used to assess hand motion dexterity of surgeons. Furthermore, the method provides a quantifiable metrics for trainee assessment and proficiency, particularly relevant as surgical training shifts toward a competency-based paradigm.