Skills evaluation in minimally invasive surgery using force/torque signatures

A systematic review on classification and assessment of surgical skill levels for simulation-based training programs

BACKGROUND

Nowadays, advances in medical informatics have made minimally invasive surgery (MIS) procedures the preferred choice. However, there are several problems with the education programs in terms of surgical skill acquisition. For instance, defining and objectively measuring surgical skill levels is a challenging process. Accordingly, the aim of this study is to conduct a literature review for an investigation of the current approaches for classifying the surgical skill levels and for identifying the skill training tools and measurement methods.

MATERIALS AND METHODS

In this research, a search is conducted and a corpus is created. Exclusion and inclusion criteria are applied by limiting the number of articles based on surgical education, training approximations, hand movements, and endoscopic or laparoscopic operations. To satisfy these criteria, 57 articles are included in the corpus of this study.

RESULTS

Currently used surgical skill assessment approaches have been summarized. Results show that various classification approaches for the surgical skill level definitions are being used. Besides, many studies are conducted by omitting particularly important skill levels in between. Additionally, some inconsistencies are also identified across the skill level classification studies.

CONCLUSION

In order to improve the benefits of simulation-based training programs, a standardized interdisciplinary approach should be developed. For this reason, specific to each surgical procedure, the required skills should be identified. Additionally, appropriate measures for assessing these skills, which can be defined in simulation-based MIS training environments, should be refined. Finally, the skill levels gained during the developmental stages of these skills, with their threshold values referencing the identified measures, should be redefined in a standardized manner.

Using the language of surgery to enhance ophthalmology surgical education

Background

Currently, surgical education utilizes a combination of the apprentice model, wet-lab training, and simulation, but due to reliance on subjective data, the quality of teaching and assessment can be variable. The "language of surgery," an established concept in engineering literature whose incorporation into surgical education has been limited, is defined as the description of each surgical maneuver using quantifiable metrics. This concept is different from the traditional notion of surgical language, generally thought of as the qualitative definitions and terminology used by surgeons.

Methods

A literature search was conducted through April 2023 using MEDLINE/PubMed using search terms to investigate wet-lab, virtual simulators, and robotics in ophthalmology, along with the language of surgery and surgical education. Articles published before 2005 were mostly excluded, although a few were included on a case-by-case basis.

Results

Surgical maneuvers can be quantified by leveraging technological advances in virtual simulators, video recordings, and surgical robots to create a language of surgery. By measuring and describing maneuver metrics, the learning surgeon can adjust surgical movements in an appropriately graded fashion that is based on objective and standardized data. The main contribution is outlining a structured education framework that details how surgical education could be improved by incorporating the language of surgery, using ophthalmology surgical education as an example.

Conclusion

By describing each surgical maneuver in quantifiable, objective, and standardized terminology, a language of surgery can be created that can be used to learn, teach, and assess surgical technical skill with an approach that minimizes bias.

Key message

The "language of surgery," defined as the quantification of each surgical movement's characteristics, is an established concept in the engineering literature. Using ophthalmology surgical education as an example, we describe a structured education framework based on the language of surgery to improve surgical education.

Classifications

Surgical education, robotic surgery, ophthalmology, education standardization, computerized assessment, simulations in teaching.

Competencies

Practice-Based Learning and Improvement.

Utilizing Grasp Monitoring to Predict Microsurgical Expertise

INTRODUCTION

Most microsurgical procedures require the surgeon to use tools to grasp and hold fragile objects in the surgical site. Prior research on grasping in surgery has mostly either been in other surgical techniques or used grasping as an auxiliary metric. We focus on microsurgery and investigate what grasping can tell about microsurgical skill and suturing performance. This study lays groundwork for using automatic detection of grasps to evaluate surgical skill.

METHODS

Five expert surgeons and six novices completed sutures on a microsurgical training board. Video recordings of the performance were annotated for the number of grasps, while an eye tracker recorded the participants' pupil dilations for cognitive workload assessment. Performance was measured with suturing duration and the University of Western Ontario Microsurgical Skills Assessment instrument (UWOMSA). Differences in skill, suturing performance and cognitive workload were compared with grasping behavior.

RESULTS

Novices needed significantly more grasps to complete sutures and failed to grasp more often than the experts. The number of grasps affected the suturing duration more in novices. Decreasing suturing efficiency as measured by UWOMSA instrument was associated with increase in grasps, even when we controlled for overall skill differences. Novices displayed larger pupil dilations when averaged over a sufficiently large sample, and the difference increased after the grasp.

CONCLUSIONS

Grasping action during microsurgical procedures can be used as a conceptually simple yet objective proxy in microsurgical performance assessment. If the grasps could be detected automatically, they could be used to aid in computational evaluation of surgical trainees' performance.

The Advances in Computer Vision That Are Enabling More Autonomous Actions in Surgery: A Systematic Review of the Literature

This is a review focused on advances and current limitations of computer vision (CV) and how CV can help us obtain to more autonomous actions in surgery. It is a follow-up article to one that we previously published in Sensors entitled, "Artificial Intelligence Surgery: How Do We Get to Autonomous Actions in Surgery?" As opposed to that article that also discussed issues of machine learning, deep learning and natural language processing, this review will delve deeper into the field of CV. Additionally, non-visual forms of data that can aid computerized robots in the performance of more autonomous actions, such as instrument priors and audio haptics, will also be highlighted. Furthermore, the current existential crisis for surgeons, endoscopists and interventional radiologists regarding more autonomy during procedures will be discussed. In summary, this paper will discuss how to harness the power of CV to keep doctors who do interventions in the loop.

Surgical Hand Gesture Recognition Utilizing Electroencephalogram as Input to the Machine Learning and Network Neuroscience Algorithms

Surgical gestures detection can provide targeted, automated surgical skill assessment and feedback during surgical training for robot-assisted surgery (RAS). Several sources including surgical videos, robot tool kinematics, and an electromyogram (EMG) have been proposed to reach this goal. We aimed to extract features from electroencephalogram (EEG) data and use them in machine learning algorithms to classify robot-assisted surgical gestures. EEG was collected from five RAS surgeons with varying experience while performing 34 robot-assisted radical prostatectomies over the course of three years. Eight dominant hand and six non-dominant hand gesture types were extracted and synchronized with associated EEG data. Network neuroscience algorithms were utilized to extract functional brain network and power spectral density features. Sixty extracted features were used as input to machine learning algorithms to classify gesture types. The analysis of variance (ANOVA) F-value statistical method was used for feature selection and 10-fold cross-validation was used to validate the proposed method. The proposed feature set used in the extra trees (ET) algorithm classified eight gesture types performed by the dominant hand of five RAS surgeons with an accuracy of 90%, precision: 90%, sensitivity: 88%, and also classified six gesture types performed by the non-dominant hand with an accuracy of 93%, precision: 94%, sensitivity: 94%.

Visual Intelligence: Prediction of Unintentional Surgical-Tool-Induced Bleeding during Robotic and Laparoscopic Surgery

Unintentional vascular damage can result from a surgical instrument’s abrupt movements during minimally invasive surgery (laparoscopic or robotic). A novel real-time image processing algorithm based on local entropy is proposed that can detect abrupt movements of surgical instruments and predict bleeding occurrence. The uniform nature of the texture of surgical tools is utilized to segment the tools from the background. By comparing changes in entropy over time, the algorithm determines when the surgical instruments are moved abruptly. We tested the algorithm using 17 videos of minimally invasive surgery, 11 of which had tool-induced bleeding. Our preliminary testing shows that the algorithm is 88% accurate and 90% precise in predicting bleeding. The average advance warning time for the 11 videos is 0.662 s, with the standard deviation being 0.427 s. The proposed approach has the potential to eventually lead to a surgical early warning system or even proactively attenuate tool movement (for robotic surgery) to avoid dangerous surgical outcomes.

Tactile Perception Technologies and Their Applications in Minimally Invasive Surgery: A Review

Minimally invasive surgery (MIS) has been the preferred surgery approach owing to its advantages over conventional open surgery. As a major limitation, the lack of tactile perception impairs the ability of surgeons in tissue distinction and maneuvers. Many studies have been reported on industrial robots to perceive various tactile information. However, only force data are widely used to restore part of the surgeon’s sense of touch in MIS. In recent years, inspired by image classification technologies in computer vision, tactile data are represented as images, where a tactile element is treated as an image pixel. Processing raw data or features extracted from tactile images with artificial intelligence (AI) methods, including clustering, support vector machine (SVM), and deep learning, has been proven as effective methods in industrial robotic tactile perception tasks. This holds great promise for utilizing more tactile information in MIS. This review aims to provide potential tactile perception methods for MIS by reviewing literatures on tactile sensing in MIS and literatures on industrial robotic tactile perception technologies, especially AI methods on tactile images.

An extended algorithm for autonomous grasping of soft tissues during robotic surgery

BACKGROUND

Autonomous grasping of soft tissues can facilitate the robotic surgery procedures. The previous attempts for implementing auto-grasping have been based on a simplistic representation of the actual surgery maneuvers.

METHOD

A generalized three-zone grasp model was introduced to consider the effect of the pull force angulation on the grasp mode, that is, damage, slip, or safe grasp. Also, an extended auto-grasping algorithm was proposed in which the trigger force is automatically controlled against the pull force magnitude and direction, to achieve a safe and secure grasp.

RESULTS

The autonomous grasping experiments against a varying pull force in a phantom study indicated a good agreement between the desired and actual pinch and trigger forces (root mean square errors lower than 0.168 N and 0.280 N, respectively) and no sign of tissue tear or slippage.

CONCLUSIONS

The proposed auto-grasping algorithm can help manipulating the soft tissues safely and effectively during robotic surgery procedures.

Surgeon-Centered Analysis of Robot-Assisted Needle Driving Under Different Force Feedback Conditions

Robotic assisted minimally invasive surgery (RAMIS) systems present many advantages to the surgeon and patient over open and standard laparoscopic surgery. However, haptic feedback, which is crucial for the success of many surgical procedures, is still an open challenge in RAMIS. Understanding the way that haptic feedback affects performance and learning can be useful in the development of haptic feedback algorithms and teleoperation control systems. In this study, we examined the performance and learning of inexperienced participants under different haptic feedback conditions in a task of surgical needle driving via a soft homogeneous deformable object-an artificial tissue. We designed an experimental setup to characterize their movement trajectories and the forces that they applied on the artificial tissue. Participants first performed the task in an open condition, with a standard surgical needle holder, followed by teleoperation in one of three feedback conditions: (1) no haptic feedback, (2) haptic feedback based on position exchange, and (3) haptic feedback based on direct recording from a force sensor, and then again with the open needle holder. To quantify the effect of different force feedback conditions on the quality of needle driving, we developed novel metrics that assess the kinematics of needle driving and the tissue interaction forces, and we combined our novel metrics with classical metrics. We analyzed the final teleoperated performance in each condition, the improvement during teleoperation, and the aftereffect of teleoperation on the performance when using the open needle driver. We found that there is no significant difference in the final performance and in the aftereffect between the 3 conditions. Only the two conditions with force feedback presented statistically significant improvement during teleoperation in several of the metrics, but when we compared directly between the improvements in the three different feedback conditions none of the effects reached statistical significance. We discuss possible explanations for the relative similarity in performance. We conclude that we developed several new metrics for the quality of surgical needle driving, but even with these detailed metrics, the advantage of state of the art force feedback methods to tasks that require interaction with homogeneous soft tissue is questionable.

Haptic Feedback, Force Feedback, and Force-Sensing in Simulation Training for Laparoscopy: A Systematic Overview

OBJECTIVES

To provide a systematic overview of the literature assessing the value of haptic and force feedback in current simulators teaching laparoscopic surgical skills.

DATA SOURCES

The databases of Pubmed, Cochrane, Embase, Web of Science, and Google Scholar were searched to retrieve relevant studies published until January 31st, 2017. The search included laparoscopic surgery, simulation, and haptic or force feedback and all relevant synonyms.

METHODS

Duplicates were removed, and titles and abstracts screened. The remaining articles were subsequently screened full text and included in this review if they followed the inclusion criteria. A total of 2 types of feedback have been analyzed and will be discussed separately: haptic- and force feedback.

RESULTS

A total of 4023 articles were found, of which 87 could be used in this review. A descriptive analysis of the data is provided. Results of the added value of haptic interface devices in virtual reality are variable. Haptic feedback is most important for more complex tasks. The interface devices do not require the highest level of fidelity. Haptic feedback leads to a shorter learning curve with a steadier upward trend. Concerning force feedback, force parameters are measured through force sensing systems in the instrument and/or the environment. These parameters, especially in combination with motion parameters, provide box trainers with an objective evaluation of laparoscopic skills. Feedback of force-use both real time and postpractice has been shown to improve training.

CONCLUSIONS

Haptic feedback is added to virtual reality simulators to increase the fidelity and thereby improve training effect. Variable results have been found from adding haptic feedback. It is most important for more complex tasks, but results in only minor improvements for novice surgeons. Force parameters and force feedback in box trainers have been shown to improve training results.

Face and Construct Validity of a Novel Virtual Reality-Based Bimanual Laparoscopic Force-Skills Trainer With Haptics Feedback

BACKGROUND

The purpose of this study was to examine the face and construct validity of a custom-developed bimanual laparoscopic force-skills trainer with haptics feedback. The study also examined the effect of handedness on fundamental and complex tasks.

METHODS

Residents (n = 25) and surgeons (n = 25) performed virtual reality-based bimanual fundamental and complex tasks. Tool-tissue reaction forces were summed, recorded, and analysed. Seven different force-based measures and a 1-time measure were used as metrics. Subsequently, participants filled out face validity and demographic questionnaires.

RESULTS

Residents and surgeons were positive on the design, workspace, and usefulness of the simulator. Construct validity results showed significant differences between residents and experts during the execution of fundamental and complex tasks. In both tasks, residents applied large forces with higher coefficient of variation and force jerks (P < .001). Experts, with their dominant hand, applied lower forces in complex tasks and higher forces in fundamental tasks (P < .001). The coefficients of force variation (CoV) of residents and experts were higher in complex tasks (P < .001). Strong correlations were observed between CoV and task time for fundamental (r = 0.70) and complex tasks (r = 0.85). Range of smoothness of force was higher for the non-dominant hand in both fundamental and complex tasks.

CONCLUSIONS

The simulator was able to differentiate the force-skills of residents and surgeons, and objectively evaluate the effects of handedness on laparoscopic force-skills. Competency-based laparoscopic skills assessment curriculum should be updated to meet the requirements of bimanual force-based training.

Gesture segmentation and classification using affine speed and energy

The characterization and analysis of hand gestures are challenging tasks with an important number of applications in human-computer interaction, machine vision and control, and medical gesture recognition. Specifically, several researchers have tried to develop objective evaluation methods of surgical skills for medical training. As a result, the adequate selection and extraction of similarities and differences between experts and novices have become an important challenge in this area. In particular, some of these works have shown that human movements performed during surgery can be described as a sequence of constant affine-speed trajectories. In this article, we will show that affine speed can be used to segment medical hand movements and present how the mechanical energy computed in the segment is analyzed to compare surgical skills. The position and orientation of the instrument end effectors are determined by six video photographic cameras. In addition, two laparoscopic instruments are capable of measuring simultaneously the forces and torques applied to the tool. Finally, we will report the results of these experiments and present a correlation between the mechanical energy values, dissipated during a procedure, and the surgical skills.

A Dataset and Benchmarks for Segmentation and Recognition of Gestures in Robotic Surgery

OBJECTIVE

State-of-the-art techniques for surgical data analysis report promising results for automated skill assessment and action recognition. The contributions of many of these techniques, however, are limited to study-specific data and validation metrics, making assessment of progress across the field extremely challenging.

METHODS

In this paper, we address two major problems for surgical data analysis: First, lack of uniform-shared datasets and benchmarks, and second, lack of consistent validation processes. We address the former by presenting the JHU-ISI Gesture and Skill Assessment Working Set (JIGSAWS), a public dataset that we have created to support comparative research benchmarking. JIGSAWS contains synchronized video and kinematic data from multiple performances of robotic surgical tasks by operators of varying skill. We address the latter by presenting a well-documented evaluation methodology and reporting results for six techniques for automated segmentation and classification of time-series data on JIGSAWS. These techniques comprise four temporal approaches for joint segmentation and classification: hidden Markov model, sparse hidden Markov model (HMM), Markov semi-Markov conditional random field, and skip-chain conditional random field; and two feature-based ones that aim to classify fixed segments: bag of spatiotemporal features and linear dynamical systems.

RESULTS

Most methods recognize gesture activities with approximately 80% overall accuracy under both leave-one-super-trial-out and leave-one-user-out cross-validation settings.

CONCLUSION

Current methods show promising results on this shared dataset, but room for significant progress remains, particularly for consistent prediction of gesture activities across different surgeons.

SIGNIFICANCE

The results reported in this paper provide the first systematic and uniform evaluation of surgical activity recognition techniques on the benchmark database.

Navigation forces during wrist arthroscopy: assessment of expert levels

PURPOSE

To facilitate effective and efficient training in skills laboratory, objective metrics can be used. Forces exerted on the tissues can be a measure of safe tissue manipulation. To provide feedback during training, expert threshold levels need to be determined. The purpose of this study was to define the magnitude and the direction of navigation forces used during arthroscopic inspection of the wrist.

METHODS

We developed a set-up to mount a cadaver wrist to a 3D force platform that allowed measurement of the forces exerted on the wrist. Six experts in wrist arthroscopy performed two tasks: (1) Introduction of the camera and visualization of the hook. (2) Navigation through the wrist with visualization of five anatomic structures. The magnitude (Fabs) and direction of force were recorded, with the direction defined as α being the angle in the vertical plane and β being the angle in the horizontal plane. The 10th-90th percentile of the data were used to set threshold levels for training.

RESULTS

The results show distinct force patterns for each of the anatomic landmarks. Median Fabs of the navigation task is 3.8 N (1.8-7.3), α is 3.60 (-54-44) and β is 260 (0-72).

CONCLUSION

Unique expert data on navigation forces during wrist arthroscopy were determined. The defined maximum allowable navigation force of 7.3 N (90th percentile) can be used in providing feedback on performance during skills training. The clinical value is that this study contributes to objective assessment of skills levels.

Objective Assessment of Laparoscopic Force and Psychomotor Skills in a Novel Virtual Reality-Based Haptic Simulator

BACKGROUND

Most of the commercially available virtual reality-based laparoscopic simulators do not effectively evaluate combined psychomotor and force-based laparoscopic skills. Consequently, the lack of training on these critical skills leads to intraoperative errors.

OBJECTIVES

To assess the effectiveness of the novel virtual reality-based simulator, this study analyzed the combined psychomotor (i.e., motion or movement) and force skills of residents and expert surgeons. The study also examined the effectiveness of real-time visual force feedback and tool motion during training.

DESIGN

Bimanual fundamental (i.e., probing, pulling, sweeping, grasping, and twisting) and complex tasks (i.e., tissue dissection) were evaluated. In both tasks, visual feedback on applied force and tool motion were provided. The skills of the participants while performing the early tasks were assessed with and without visual feedback. Participants performed 5 repetitions of fundamental and complex tasks. Reaction force and instrument acceleration were used as metrics.

SETTING

Surgical Gastroenterology, Government Stanley Medical College and Hospital; Institute of Surgical Gastroenterology, Madras Medical College and Rajiv Gandhi Government General Hospital.

PARTICIPANTS

Residents (N = 25; postgraduates and surgeons with <2 years of laparoscopic surgery) and expert surgeons (N = 25; surgeons with >4 and ≤10 years of laparoscopic surgery).

RESULTS

Residents applied large forces compared with expert surgeons and performed abrupt tool movements (p < 0.001). However, visual + haptic feedback improved the performance of residents (p < 0.001). In complex tasks, visual + haptic feedback did not influence the applied force of expert surgeons, but influenced their tool motion (p < 0.001). Furthermore, in complex tissue sweeping task, expert surgeons applied more force, but were within the tissue damage limits. In both groups, exertion of large forces and abrupt tool motion were observed during grasping, probing or pulling, and tissue sweeping maneuvers (p < 0.001).

CONCLUSIONS

Modern day curriculum-based training should evaluate the skills of residents with robust force and psychomotor-based exercises for proficient laparoscopy. Visual feedback on force and motion during training has the potential to enhance the learning curve of residents.

Investigating haptic distance-to-break using linear and nonlinear materials in a simulated minimally invasive surgery task

Accurate detection of mediated haptic information in minimally invasive surgery (MIS) is critical for applying appropriate force magnitudes onto soft tissue with the aim of minimising tissue trauma. Force perception in MIS is a dynamic process, with surgeons' administration of force into tissue revealing information about the remote surgical site which further informs the surgeons' haptic interactions. The relationship between applied force and material deformation rate provides biomechanical information specifying the deformation distance remaining until a tissue will fail: which is termed distance-to-break (DTB). The current study demonstrates that observers can detect DTB while deforming simulated tissues and stop before reaching the tissues' failure points. The design of training simulators, control devices and automated robotic systems for applications outside of MIS is discussed. Practitioner Summary: In MIS, haptic information is critical for applying appropriate forces onto soft tissue to minimise tissue trauma. Observers used force information to detect how far they could deform a virtual tissue before it would break. The design of training simulators, control devices and automated robotic systems is discussed.

Real-Time Measurement of the Tool-Tissue Interaction in Minimally Invasive Abdominal Surgery: The First Step to Developing the Next Generation of Smart Laparoscopic Instruments

Introduction Analysis of force application in laparoscopic surgery is critical to understanding the nature of the tool-tissue interaction. The aim of this study is to provide real-time data about manipulations to abdominal organs. Methods An instrumented short fenestrated grasper was used in an in vivo porcine model, measuring force at the grasper handle. Grasping force and duration over 5 small bowel manipulation tasks were analyzed. Forces required to retract gallbladder, bladder, small bowel, large bowel, and rectum were measured over 30 seconds. Four parameters were calculated-T(hold), the grasp time; T(close), time taken for the jaws to close; F(max), maximum force reached; and F(rms), root mean square force (representing the average force across the grasp time). Results Mean F(max) to manipulate the small bowel was 20.5 N (±7.2) and F(rms) was 13.7 N (±5.4). Mean T(close) was 0.52 seconds (±0.26) and T(hold) was 3.87 seconds (±1.5). In individual organs, mean F(max) was 49 N (±15) to manipulate the rectum and 59 N (±13.4) for the colon. The mean F(max) for bladder and gallbladder retraction was 28.8 N (±7.4) and 50.7 N (±3.8), respectively. All organs exhibited force relaxation, the F(rms) reduced to below 25 N for all organs except the small bowel, with a mean F(rms) of less than 10 N. Conclusion This study has commenced the process of quantifying tool-tissue interaction. The static measurements discussed here should evolve to include dynamic measurements such as shear, torque, and retraction forces, and be correlated with evidence of histological damage to tissue.

Magnetic Surgical Instruments for Robotic Abdominal Surgery

This review looks at the implementation of magnetic-based approaches in surgical instruments for abdominal surgeries. As abdominal surgical techniques advance toward minimizing surgical trauma, surgical instruments are enhanced to support such an objective through the exploration of magnetic-based systems. With this design approach, surgical devices are given the capabilities to be fully inserted intraabdominally to achieve access to all abdominal quadrants, without the conventional rigid link connection with the external unit. The variety of intraabdominal surgical devices are anchored, guided, and actuated by external units, with power and torque transmitted across the abdominal wall through magnetic linkage. This addresses many constraints encountered by conventional laparoscopic tools, such as loss of triangulation, fulcrum effect, and loss/lack of dexterity for surgical tasks. Design requirements of clinical considerations to aid the successful development of magnetic surgical instruments, are also discussed.

Quantification of Forces During a Neurosurgical Procedure: A Pilot Study

OBJECTIVE

Knowledge of tool-tissue interaction is mostly taught and learned in a qualitative manner because a means to quantify the technical aspects of neurosurgery is currently lacking. Neurosurgeons typically require years of hands-on experience, together with multiple initial trial and error, to master the optimal force needed during the performance of neurosurgical tasks. The aim of this pilot study was to develop a novel force-sensing bipolar forceps for neurosurgery and obtain preliminary data on specific tasks performed on cadaveric brains.

METHODS

A novel force-sensing bipolar forceps capable of measuring coagulation and dissection forces was designed and developed by installing strain gauges along the length of the bipolar forceps prongs. The forceps was used in 3 cadaveric brain experiments and forces applied by an experienced neurosurgeon for 10 surgical tasks across the 3 experiments were quantified.

RESULTS

Maximal peak (effective) forces of 1.35 N and 1.16 N were observed for dissection (opening) and coagulation (closing) tasks, respectively. More than 70% of forces applied during the neurosurgical tasks were less than 0.3 N. Mean peak forces ranged between 0.10 N and 0.41 N for coagulation of scalp vessels and pia-arachnoid, respectively, and varied from 0.16 N for dissection of small cortical vessel to 0.65 N for dissection of the optic chiasm.

CONCLUSIONS

The force-sensing bipolar forceps were able to successfully measure and record real-time tool-tissue interaction throughout the 3 experiments. This pilot study serves as a first step toward quantification of tool-tissue interaction forces in neurosurgery for training and improvement of instrument handling skills.

A Novel Device for Measuring Forces in Endoluminal Procedures

In this paper a simple but effective measuring system for endoluminal procedures is presented. The device allows measuring forces during the endoluminal manipulation of tissues with a standard surgical instrument for laparoscopic procedures. The force measurement is performed by recording both the forces applied directly by the surgeon at the instrument handle and the reaction forces on the access port. The measuring system was used to measure the forces necessary for appropriate surgical manipulation of tissues during transanal endoscopic microsurgery (TEM). Ex-vivo and in-vivo measurements were performed, reported and discussed. The obtained data can be used for developing and appropriately dimensioning novel dedicated instrumentation for TEM procedures.

Simulator-based assessment of haptic surgical skill: a comparative study

The aim of this study was to examine if the forces applied by users of a haptic simulator could be used to distinguish expert surgeons from novices. Seven surgeons with significant operating room expertise and 9 novices with no surgical experience participated in this study. The experimental task comprised exploring 4 virtual materials with the haptic device and learning the precise forces required to compress the materials to various depths. The virtual materials differed in their stiffness and force-displacement profiles. The results revealed that for nonlinear virtual materials, surgeons applied significantly greater magnitudes of force than novices. Furthermore, for the softer nonlinear and linear materials, surgeons were significantly more accurate in reproducing forces than novices. The results of this study suggest that the magnitudes of force measured using haptic simulators may be used to objectively differentiate experts' haptic skill from that of novices. This knowledge can inform the design of virtual reality surgical simulators and lead to the future incorporation of haptic skills training in medical school curricula.

Advanced training in laparoscopic abdominal surgery: a systematic review

BACKGROUND

Simulation has spread widely this last decade, especially in laparoscopic surgery, and training out of the operating room has proven its positive impact on basic skills during real laparoscopic procedures. Few articles dealing with advanced training in laparoscopic abdominal surgery, however, have been published. Such training may decrease learning curves in the operating room for junior surgeons with limited access to complex laparoscopic procedures as a primary operator.

METHODS

Two reviewers, using MEDLINE, EMBASE, and The Cochrane Library conducted a systematic research with combinations of the following keywords: (teaching OR education OR computer simulation) AND laparoscopy AND (gastric OR stomach OR colorectal OR colon OR rectum OR small bowel OR liver OR spleen OR pancreas OR advanced surgery OR advanced procedure OR complex procedure). Additional studies were searched in the reference lists of all included articles.

RESULTS

Fifty-four original studies were retrieved. Their level of evidence was low: most of the studies were case series and one fifth were purely descriptive, but there were eight randomized trials. Pig models and video trainers as well as gastric and colorectal procedures were mainly assessed. The retrieved studies showed some encouraging trends in terms of trainee satisfaction with improvement after training, but the improvements were mainly on the training tool itself. Some tools have been proven to be construct-valid.

CONCLUSION

Higher-quality studies are required to appraise educational value in this field.

Expertise in medicine: using the expert performance approach to improve simulation training

CONTEXT

We critically review how medical education can benefit from systematic use of the expert performance approach as a framework for measuring and enhancing clinical practice. We discuss how the expert performance approach can be used to better understand the mechanisms underpinning superior performance among health care providers and how the framework can be applied to create simulated learning environments that present increased opportunities to engage in deliberate practice.

EXPERT PERFORMANCE APPROACH

The expert performance approach is a systematic, evidence-based framework for measuring and analysing superior performance. It has been applied in a variety of domains, but has so far been relatively neglected in medicine and health care. Here we outline the framework and demonstrate how it can be effectively applied to medical education.

DELIBERATE PRACTICE

Deliberate practice is defined as a structured and reflective activity, which is designed to develop a critical aspect of performance. Deliberate practice provides an opportunity for error detection and correction, repetition, access to feedback and requires maximal effort, complete concentration and full attention. We provide guidance on how to structure simulated learning environments to encourage the accumulation of deliberate practice.

CONCLUSIONS

We highlight the role of simulation-based training in conjunction with deliberate practice activities such as reflection, rehearsal, trial-and-error learning and feedback in improving the quality of patient care. We argue that the development of expertise in health care is directly related to the systematic identification and improvement of quantifiable performance metrics. In order to optimise the training of expert health care providers, advances in simulation technology need to be coupled with effective instructional systems design, with the latter being strongly guided by empirical research from the learning and cognitive sciences.

Assessment of laparoscopic skills based on force and motion parameters

Box trainers equipped with sensors may help in acquiring objective information about a trainee's performance while performing training tasks with real instruments. The main aim of this study is to investigate the added value of force parameters with respect to commonly used motion and time parameters such as path length, motion volume, and task time. Two new dynamic bimanual positioning tasks were developed that not only requiring adequate motion control but also appropriate force control successful completion. Force and motion data for these tasks were studied for three groups of participants with different experience levels in laparoscopy (i.e., 11 novices, 19 intermediates, and 12 experts). In total, 10 of the 13 parameters showed a significant difference between groups. When the data from the significant motion, time, and force parameters are used for classification, it is possible to identify the skills level of the participants with 100% accuracy. Furthermore, the force parameters of many individuals in the intermediate group exceeded the maximum values in the novice and expert group. The relatively high forces used by the intermediates argue for the inclusion of training and assessment of force application during tissue handling in future laparoscopic skills training programs.

Sensor fusion for laparoscopic surgery skill acquisition

Surgical techniques are becoming more complex and require substantial training to master. The development of automated, objective methods to analyze and evaluate surgical skill is necessary to provide trainees with reliable and accurate feedback during their training programs. We present a system to capture, visualize, and analyze the movements of a laparoscopic surgeon for the purposes of skill evaluation. The system records the upper body movement of the surgeon, the position, and orientation of the instruments, and the force and torque applied to the instruments. An empirical study was conducted using the system to record the performances of a number of surgeons with a wide range of skill. The study validated the usefulness of the system, and demonstrated the accuracy of the measurements.

Surgical gesture classification from video and kinematic data

Much of the existing work on automatic classification of gestures and skill in robotic surgery is based on dynamic cues (e.g., time to completion, speed, forces, torque) or kinematic data (e.g., robot trajectories and velocities). While videos could be equally or more discriminative (e.g., videos contain semantic information not present in kinematic data), they are typically not used because of the difficulties associated with automatic video interpretation. In this paper, we propose several methods for automatic surgical gesture classification from video data. We assume that the video of a surgical task (e.g., suturing) has been segmented into video clips corresponding to a single gesture (e.g., grabbing the needle, passing the needle) and propose three methods to classify the gesture of each video clip. In the first one, we model each video clip as the output of a linear dynamical system (LDS) and use metrics in the space of LDSs to classify new video clips. In the second one, we use spatio-temporal features extracted from each video clip to learn a dictionary of spatio-temporal words, and use a bag-of-features (BoF) approach to classify new video clips. In the third one, we use multiple kernel learning (MKL) to combine the LDS and BoF approaches. Since the LDS approach is also applicable to kinematic data, we also use MKL to combine both types of data in order to exploit their complementarity. Our experiments on a typical surgical training setup show that methods based on video data perform equally well, if not better, than state-of-the-art approaches based on kinematic data. In turn, the combination of both kinematic and video data outperforms any other algorithm based on one type of data alone.

Skills transfer after proficiency-based simulation training in superficial femoral artery angioplasty

INTRODUCTION

The purpose of this study was to explore whether basic endovascular skills acquired using proficiency-based simulation training in superficial femoral artery (SFA) angioplasty translate to real-world performance.

METHODS

Five international experts were invited to evaluate a preliminary 28-item rating scale for SFA angioplasty using a modified Delphi study. To test the procedural scale, 4 experts and 11 final-year medical students then performed 2 SFA angioplasties each on the vascular intervention simulation trainer simulator. Thereafter, 10 general surgical residents (novices) received didactic training in SFA angioplasty. Trainees were then randomized with 5 trainees receiving further training on the vascular intervention simulation trainer simulator up to proficiency level. All 10 trainees then performed 1 SFA angioplasty on a patient within 5 days of training. The trainees' performance was assessed by 1 attending consultant blinded to the trainees' training status, using the developed procedural scale and a global rating scale.

RESULTS

Four items were eliminated from the procedural scale after the Delphi study. There were significant differences in the procedural scale scores between the experts and the students in the first trial [mean (SD), 94.25 (2.22) vs. 74.90 (8.79), P = 0.001] and the second trial [95.25 (0.50) vs. 76.82 (9.44), P < 0.001]. Simulation-trained trainees scored higher than the controls on the procedural scale [86.8 (5.4) vs. 67.6 (6), P = 0.001] and the global rating scale [37.2 (4.1) vs. 24.4 (5.3), P = 0.003].

CONCLUSIONS

Basic endovascular skills acquired using proficiency-based simulation training in SFA angioplasty do translate to real-world performance.

Objective differentiation of force-based laparoscopic skills using a novel haptic simulator

BACKGROUND

There is a growing need for effective surgical simulators to train the novice resident with a core skill set that can be later used in advanced operating room training. The most common simulator-based laparoscopic skills curriculum, the Fundamentals of Laparoscopic Skills (FLS), has been demonstrated to effectively teach basic surgical skills; however, a key deficiency in current surgical simulators is lack of validated training for force-based or haptic skills. In this study, a novel haptic simulator was examined for construct validity by determining its ability to differentiate between the force skills of surgeons and novices.

METHODS

A total of 34 participants enrolled in the study and were divided into two groups: novices, with no previous surgical experience and surgeons, with some level of surgical experience (including upper level residents and attendings). All participants performed a force-based task using grasping, probing, or sweeping motions with laparoscopic tools on the simulator. In the first session, participants were given 3 trials to learn specific forces associated with locations on a graphic; after this, they were asked to reproduce forces at each of the locations in random order. A force-based metric (score) was used to record performance.

RESULTS

On probing and grasping tasks, novices applied significantly greater overall forces than surgeons. When analyzed by force levels, novices applied greater forces on the probing task at lower and mid-range forces, for grasping at low-range forces ranges and, for sweeping at high-range forces.

CONCLUSIONS

The haptic simulator successfully differentiated between novice and surgeon force skill level at specific ranges for all 3 salient haptic tasks, establishing initial construct validity of the haptic simulator. Based on these results, force-based simulator metrics may be used to objectively measure haptic skill level and potentially train residents. Haptic simulator development should focus on the 3 salient haptic skills (grasping, probing, and sweeping) where precise force application is necessary for successful task outcomes.

Technical competence in surgeons

In this paper, we review the literature to date on technical competence in surgeons; how it can be defined, taught to trainees and assessed. We also examine how we can predict which candidates for surgical training will most likely develop technical competence. While technical competency is just one aspect of what makes a good surgeon, we have recognized a need to review the literature in this area and to combine this with broader definitions of competency. Our review found that several methods are available to objectively measure, assess and predict technical competence and should be used in surgical training.

Real-time control and evaluation of a teleoperated miniature arm for Single Port Laparoscopy

This paper presents the control architecture and the first performance evaluation results of a novel and highly-dexterous 18 degrees of freedom (DOF) miniature master/slave teleoperated robotic system called SPRINT (Single-Port la-paRoscopy bimaNual roboT). The system was evaluated in terms of positioning accuracy, repeatability, tracking error during local teleoperation and end-effector payload. Moreover, it was experimentally verified that the control architecture is real-time compliant at an operating frequency of 1 kHz and it is also reliable in terms of safety. The architecture accounts for cases when the robot is lead through singularities, and includes other safety mechanisms, such as supervision tasks and watchdog timers. Peliminary tests that were performed by surgeons in-vitro suggest that the SPRINT robot, along with its real-time control architecture, could become in the near future a reliable system in the field of Single Port Laparoscopy.

Force Parameters for Skills Assessment in Laparoscopy

When equipped with motion and force sensors, box-trainers can be good alternatives for relatively expensive Virtual Reality (VR) trainers. As in VR trainers, the sensors in a box trainer could provide the trainee with objective information about his performance. Recently, multiple tracking systems were developed for classification of participants based on motion and time parameters. The aim of this study is the development of force parameters that reflect the trainee's performance in a suture task. Our second goal is to investigate if the level of the participant's skills can be classified as experts or novice level. In the experiment, experts (n = 11) and novices (n = 21) performed a two-handed needle driving and knot tying task on artificial tissue inside a box trainer. The tissue was mounted on the Force platform that was used to measure the force, which the subject applied on the tissue in three directions. We evaluated the potential of 16 different performance parameters, related to the magnitude, direction, and variability of applied forces, to distinguish between different levels of surgical expertise. Nine of the parameters showed significant differences between experts and novices. Principal Component Analysis was used to convert these nine partly correlating parameters, such as peak force, mean force, and main direction of force, into two uncorrelated variables. By performing a Leave-One-Out-Cross Validation with Linear Discriminant Analysis on each participants' score on these two variables, it was possible to correctly classify 84 percent of all participants as an expert or novice. We conclude that force measurements in a box trainer can be used to classify the level of performance of trainees and can contribute to objective assessment of suture skills.

An observation support system with an adaptive ontology-driven user interface for the modeling of complex behaviors during surgical interventions

The field of surgical interventions emphasizes knowledge and experience; explicit and detailed models of surgical processes are hard to obtain by observation or measurement. However, in medical engineering and related developments, such models are highly valuable. Surgical process modeling deals with the generation of complex process descriptions by observation. This places high demands on the observers, who have to use a sizable terminology to denominate surgical actions, instruments, and patient anatomies, and to describe processes unambiguously. Here, we present a novel method, employing an ontology-based user interface that adapts to the actual situation and describe the principles of the system. A validation study showed that this method enables observers with little recording experience to reach a recording accuracy of >90%. Furthermore, this method can be used for live and video observation. We conclude that the method of ontology-supported recording for complex behaviors can be advantageously employed when surgical processes are modeled.

Toward effective pediatric minimally invasive surgical simulation

BACKGROUND/PURPOSE

Simulation is increasingly being recognized as an important tool in the training and evaluation of surgeons. Currently, there is no simulator that is specific to pediatric minimally invasive surgery (MIS). A fundamental technical difference between adult and pediatric MIS is the degree of motion scaling. Smaller instruments and areas of dissection under greater optical magnification require finer, more precise hand movements. We hypothesized that this can be used to detect differences in skills proficiency between pediatric and general surgeons.

METHODS

We programmed a virtual reality simulation of intracorporeal suturing with modes that used motion scaling to mimic conditions of either adult or pediatric MIS. The participants consisted of pediatric and general surgeons who wore motion-sensing gloves. Metrics included time elapsed, penetration errors, tool movement smoothness, hand movement smoothness, and gesture level proficiency.

RESULTS

For all measures, pediatric surgeons demonstrated superior proficiency on exercises conducted in pediatric conditions (P < .05). Performance in adult conditions was similar between the 2 groups.

CONCLUSION

Pediatric surgeons possess unique skills compared with general surgeons that relate to the technical challenges they routinely face, reinforcing the need for a surgical simulator specific to pediatric MIS. This validates our simulator and the manipulation of motion scaling as a useful training tool.

A step toward identification of surgical actions in mastoidectomy

Mastoidectomy is a core surgical procedure in otologic surgery. It is believed that the procedure is performed by different surgeons with some variability. However, it is also believed that all surgeons use a finite number of fundamental surgical actions to complete the procedure. To determine how a surgeon performs a mastoidectomy, we sought to identify the fundamental surgical actions [called action primitives (APs)] and determine the transition boundaries among those APs. Our motivation for this paper is both to delineate the APs necessary to complete a mastoidectomy and to optimize and potentially automate the surgical process. In this paper, we present a new approach to developing methods for parsing raw data (position and orientation of the surgical tool and end-effector force) into a sequence of surgical tasks. The overall objective is to de-construct the surgical procedure into a series of APs. This paper presents results from our initial investigation on detecting transition boundaries and identifying APs involved in mastoidectomy.