Analysis of hand motion differentiates expert and novice surgeons

Laparoscopic distal gastrectomy skill evaluation from video: a new artificial intelligence-based instrument identification system

The advent of Artificial Intelligence (AI)-based object detection technology has made identification of position coordinates of surgical instruments from videos possible. This study aimed to find kinematic differences by surgical skill level. An AI algorithm was developed to identify X and Y coordinates of surgical instrument tips accurately from video. Kinematic analysis including fluctuation analysis was performed on 18 laparoscopic distal gastrectomy videos from three expert and three novice surgeons (3 videos/surgeon, 11.6 h, 1,254,010 frames). Analysis showed the expert surgeon cohort moved more efficiently and regularly, with significantly less operation time and total travel distance. Instrument tip movement did not differ in velocity, acceleration, or jerk between skill levels. The evaluation index of fluctuation β was significantly higher in experts. ROC curve cutoff value at 1.4 determined sensitivity and specificity of 77.8% for experts and novices. Despite the small sample, this study suggests AI-based object detection with fluctuation analysis is promising because skill evaluation can be calculated in real time with potential for peri-operational evaluation.

Effectiveness of a vision-based handle trajectory monitoring system in studying robotic suture operation

Data on surgical robots are not openly accessible, limiting further study of the operation trajectory of surgeons' hands. Therefore, a trajectory monitoring system should be developed to examine objective indicators reflecting the characteristic parameters of operations. 20 robotic experts and 20 first-year residents without robotic experience were included in this study. A dry-lab suture task was used to acquire relevant hand performance data. Novices completed training on the simulator and then performed the task, while the expert team completed the task after warm-up. Stitching errors were measured using a visual recognition method. Videos of operations were obtained using the camera array mounted on the robot, and the hand trajectory of the surgeons was reconstructed. The stitching accuracy, robotic control parameters, balance and dexterity parameters, and operation efficiency parameters were compared. Experts had smaller center distance (p < 0.001) and larger proximal distance between the hands (p < 0.001) compared with novices. The path and volume ratios between the left and right hands of novices were larger than those of experts (both p < 0.001) and the total volume of the operation range of experts was smaller (p < 0.001). The surgeon trajectory optical monitoring system is an effective and non-subjective method to distinguish skill differences. This demonstrates the potential of pan-platform use to evaluate task completion and help surgeons improve their robotic learning curve.

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.

Relating process and outcome metrics for meaningful and interpretable cannulation skill assessment: A machine learning paradigm

BACKGROUND AND OBJECTIVES

The quality of healthcare delivery depends directly on the skills of clinicians. For patients on hemodialysis, medical errors or injuries caused during cannulation can lead to adverse outcomes, including potential death. To promote objective skill assessment and effective training, we present a machine learning approach, which utilizes a highly-sensorized cannulation simulator and a set of objective process and outcome metrics.

METHODS

In this study, 52 clinicians were recruited to perform a set of pre-defined cannulation tasks on the simulator. Based on data collected by sensors during their task performance, the feature space was then constructed based on force, motion, and infrared sensor data. Following this, three machine learning models- support vector machine (SVM), support vector regression (SVR), and elastic net (EN)- were constructed to relate the feature space to objective outcome metrics. Our models utilize classification based on the conventional skill classification labels as well as a new method that represents skill on a continuum.

RESULTS

With less than 5% of trials misplaced by two classes, the SVM model was effective in predicting skill based on the feature space. In addition, the SVR model effectively places both skill and outcome on a fine-grained continuum (versus discrete divisions) that is representative of reality. As importantly, the elastic net model enabled the identification of a set of process metrics that highly impact outcomes of the cannulation task, including smoothness of motion, needle angles, and pinch forces.

CONCLUSIONS

The proposed cannulation simulator, paired with machine learning assessment, demonstrates definite advantages over current cannulation training practices. The methods presented here can be adopted to drastically increase the effectiveness of skill assessment and training, thereby potentially improving clinical outcomes of hemodialysis treatment.

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.

Encouraging Young Pediatric Surgeons and Evaluation of the Effectiveness of a Pediatric Endosurgery Workshop by Self-Assessment and an Objective Skill Validation System

Background: Japanese pediatric endosurgery experts conducted a workshop for young pediatric surgeons in Russia in collaboration with Russian expert pediatric surgeons. This study was aimed to develop a contributive workshop program and evaluate its impact on young pediatric surgeons. Methods: A 2-day pediatric endosurgery workshop was held in Moscow in February 2020. After conducting a needs assessment survey, Japanese and Russian faculties developed the workshop contents, including pre- and postworkshop skills assessments, lectures, and hands-on training. Skills assessments were performed using the objective skill validation system, the "A-Lap Mini," mimicking intestinal anastomosis. The trainees self-evaluated their knowledge and skills using a five-point scale. Results: Fifteen novice trainee participated and 14 (93.3%) completed the workshop program. The completion rate for the suturing task before and after the workshop was 40.0% (6/15) and 85.7% (12/14), respectively. The following five skill evaluation criteria, which were objectively evaluated: performance time changed from 751.6 ± 247.1 seconds to 780.0 ± 313.3 seconds (P > .05), number of full-thickness sutures improved from 1.0 ± 1.41 to 2.64 ± 0.84 (P = .003), area of wound-opening changed from 0.42 ± 0.83 mm² to 0.53 ± 1.13 mm² (P > .05), suture tension improved from 55.48% ± 19.51% to 61.95% ± 23.91% (P > .05), and maximum air leakage pressure improved from 3.76 ± 2.11 kPa to 8.42 ± 7.68 kPa (P > .05). Regarding the self-assessed questionnaire administered before and after the workshop, the confidence in endosurgery skills significantly improved as follows: forceps manipulation ability improved from 2.7 to 3.7 (P < .05), and suturing performance improved from 2.5 to 3.6 (P < .05). The usefulness of the workshop for clinical surgery was scored at 4.3. Conclusions: Quantitative skill evaluation with an automatic feedback function was useful for endosurgery training. Delivering feedback concerning the assessment results to the trainee helps them to determine the specific training requirements needed for clinical endosurgery.

Influence of experience on kinematics of upper limbs during sewing gesture

To teach a skilled motor task, it is crucial to understand the characteristics of expertise. The aim of the present study was to compare the kinematics of the hand sewing task between novices (n = 10), intermediates (n = 10) and experts (n = 10). Compared to novices and intermediates, the proximal joint of expert participants was less involved in the task than their distal joints. The shoulder of experts stayed closer to the trunk, while the ranges of motion of the wrist and fingers were higher. This ability enabled them to avoid lifting the arm, which was resting on the table. We observed a low cycle-to-cycle variability of the movement pattern for experts, while it was more variable in novices. Moreover, experts shared similar joints synergies attesting of an "experts" common gesture. This knowledge gained about the hand sewing kinematics can be used to refine the training process of dressmakers.

Laparoscopic surgical skill evaluation with motion capture and eyeglass gaze cameras: A pilot study

INTRODUCTION

An eyeglass gaze camera and a skeletal coordinate camera without sensors attached to the operator's body were used to monitor gaze and movement during a simulated surgical procedure. These new devices have the potential to change skill assessment for laparoscopic surgery. The suitability of these devices for skill assessment was investigated.

MATERIAL AND METHODS

Six medical students, six intermediate surgeons, and four experts performed suturing tasks in a dry box. The tip positions of the instruments were identified from video recordings. Performance was evaluated based on instrument movement, gaze, and skeletal coordination.

RESULTS

Task performance time and skeletal coordinates were not significantly different among skill levels. The total movement distance of the right instrument was significantly different depending on the skill level. The SD of the gaze coordinates was significantly different depending on skill level and was less for experts. The expert's gaze stayed in a small area with little blurring.

CONCLUSIONS

The SD of gaze point coordinates correlates with laparoscopic surgical skill level. These devices may facilitate objective intraoperative skill evaluation in future studies.

Surgesture: a novel instrument based on surgical actions for objective skill assessment

BACKGROUND

Due to varied surgical skills and the lack of an efficient rating system, we developed Surgesture based on elementary functional surgical gestures performed by surgeons, which could serve as objective metrics to evaluate surgical performance in laparoscopic cholecystectomy (LC).

METHODS

We defined 14 LC basic Surgestures. Four surgeons annotated Surgestures among LC videos performed by experts and novices. The counts, durations, average action time, and dissection/exposure ratio (D/E ratio) of LC Surgestures were compared. The phase of mobilizing hepatocystic triangle (MHT) was extracted for skill assessment by three professors using a modified Global Operative Assessment of Laparoscopic Skills (mGOALS).

RESULTS

The novice operation time was significantly longer than the expert operation time (58.12 ± 19.23 min vs. 26.66 ± 8.00 min, P < 0.001), particularly during MHT phase. Novices had significantly more Surgestures than experts in both hands (P < 0.05). The left hand and inefficient Surgesture of novices were dramatically more than those of experts (P < 0.05). The experts demonstrated a significantly higher D/E ratio of duration than novices (0.79 ± 0.37 vs. 2.84 ± 1.98, P < 0.001). The counts and time pattern map of LC Surgestures during MHT demonstrated that novices tended to complete LC with more types of Surgestures and spent more time exposing the surgical scene. The performance metrics of LC Surgesture had significant but weak associations with each aspect of mGOALS.

CONCLUSION

The newly constructed Surgestures could serve as accessible and quantifiable metrics for demonstrating the operative pattern and distinguishing surgeons with various skills. The association between Surgestures and Global Rating Scale laid the foundation for establishing a bridge to automated objective surgical skill evaluation.

Beyond Efficiency: Surface Electromyography Enables Further Insights into the Surgical Movements of Urologists

INTRODUCTION

Surgical skill evaluation while performing minimally invasive surgeries is a highly complex task. It is important to objectively assess an individual's technical skills throughout surgical training to monitor progress and to intervene when skills are not commensurate with the year of training. The miniaturization of wireless wearable platforms integrated with sensor technology has made it possible to non-invasively assess muscle activations and movement variability during performance of minimally invasive surgical tasks. Our objective was to use electromyography to deconstruct the motions of a surgeon during robotic suturing and distinguish quantifiable movements that characterize the skill of an experienced, expert urologic surgeon from trainees.

METHODS

Three skill groups of participants: novice (n=11), intermediate (n=12) and expert (n=3) were enrolled in the study. A total of 12 wireless wearable sensors consisting of surface electromyograms (EMGs) and accelerometers were placed along upper extremity muscles to assess muscle activations and movement variability, respectively. Participants then performed a robotic suturing task.

RESULTS

EMG-based parameters: total time, dominant frequency, cumulative muscular workload (CMW were significantly different across the three skill groups. We also found nonlinear movement variability parameters such as correlation dimension, Lyapunov exponent trended differently across the three skill groups.

CONCLUSIONS

These findings suggest that economy of motion variables and nonlinear movement variabilities are affected by surgical experience level. Wearable sensor signal analysis could make it possible to objectively evaluate surgical skill level periodically throughout the residency training experience.

Objective assessment of laparoscopic targeting skills using a Short-Time Power of Difference (STPOD) method

PURPOSE

To ensure that the use of surgical training tools results in improvement of surgical skills, it is necessary to be able to measure and assess surgeons' skills. We established the Short-Time Power of Difference (STPOD) method as an evaluation tool for evaluating targeting technique. The STPOD method evaluates the distance from the actual movement of the forceps to the shortest linear path between two points in a short time period. We examined the effectiveness of the STPOD method as a new forceps kinematic analysis.

METHODS

Six residents were categorized as novices and six urologists as experts. All participants performed box trainer training and LapPASS® Simulator training. During the procedure, objective scores (time, distance, and STPOD) were recorded. STPOD (Power) evaluated motion smoothness and STPOD (Stop) evaluated the stop time of the forceps.

RESULTS

STPOD (Stop) on the right side of the experts was significantly lower than that of the novices in the box trainer. Furthermore, there were significant differences in the distances of left side and STPOD (Power) between the experts and the novices in the simulator. In the correlation of parameters between the box trainer and the simulator, time showed the strongest correlation, STPOD (Power) and distance showed a mild correlation.

CONCLUSION

We showed the construct validity of STPOD (Power) and STPOD (Stop) using both the box trainer and the simulator. This method is a good evaluation tool for assessing a physician's skill; however, there are much more complex motions that are performed in actual surgery. Future studies are needed to focus on evaluation in an environment closer to actual surgery and comparing with other existing methods.

Design and Validity Evidence for a Unique Endoscopy Simulator Using a Commercial Video Game

Background Procedural simulation enhances early endoscopy training. Multiple commercial simulators are available; however, their application is limited by cost and poor user compliance. First-person "shooter" (FPS) video games are popular. In this study, we aimed to show that a novel in-house designed colonoscope controller used to play an FPS video game shares similar constructs with real-life endoscopy. Methodology Participants completed the first three levels on an FPS video game, Portal (Valve Corporation, Bellevue, WA), first using a conventional controller and then the modified endoscope controller. A total of 12 expert endoscopists and 12 surgical residents with minimal endoscopy experience were evaluated based on completion time, button presses, and hand motion analyses. Results Experts outperformed novices for completion time (expert: 944 seconds; novice: 1,515 seconds; p = 0.006) and hand movements (expert: 1,263.1; novice: 2,052.6; p = 0.004) in using the novel colonoscope controller. There was no difference in button presses or total path length traveled. Furthermore, performance did not differ using conventional game controls. Conclusions Experts outperformed novices using the endoscope but not the conventional controller with respect to the economy of movement and completion time. This result confirms that our endoscope-controlled video game shares similar paradigms with real-life endoscopy and serves as a first step toward creating a more enjoyable and cheaper alternative to commercially available endoscopy simulators.

Years of experience is more effective in defining experts in the gaze analysis of laparoscopic suturing task than task duration

In this study, the relationship between gaze patterns, task duration, and years of experience, which are commonly used to define and evaluate expert surgeons in laparoscopic surgery, was identified. Ten surgeons with 1-28 years of experience and six inexperienced students were included. Subjects used forceps to repeat the task of suturing a suture pad. Each subject wore an eye-marking recorder, and gaze points were recorded and analyzed. No significant relationship between task duration and gaze pattern was observed. However, there was a significant relationship between a surgeon's years of experience and the percentage of time spent gazing at the forceps. Subjects with more years of experience operated without looking at the forceps and fixed their gaze on the operational target. Therefore, when analyzing laparoscopic gazing patterns, it may be more appropriate to define an "expert" based on the years of experience rather than task duration.

Motion analysis of the JHU-ISI Gesture and Skill Assessment Working Set II: learning curve analysis

PURPOSE

The Johns Hopkins-Intuitive Gesture and Skill Assessment Working Set (JIGSAWS) dataset is used to develop robotic surgery skill assessment tools, but there has been no detailed analysis of this dataset. The aim of this study is to perform a learning curve analysis of the existing JIGSAWS dataset.

METHODS

Five trials were performed in JIGSAWS by eight participants (four novices, two intermediates and two experts) for three exercises (suturing, knot-tying and needle passing). Global Rating Scores and time, path length and movements were analyzed quantitatively and qualitatively by graphical analysis.

RESULTS

There are no significant differences in Global Rating Scale scores over time. Time in the suturing exercise and path length in needle passing had significant differences. Other kinematic parameters were not significantly different. Qualitative analysis shows a learning curve only for suturing. Cumulative sum analysis suggests completion of the learning curve for suturing by trial 4.

CONCLUSIONS

The existing JIGSAWS dataset does not show a quantitative learning curve for Global Rating Scale scores, or most kinematic parameters which may be due in part to the limited size of the dataset. Qualitative analysis shows a learning curve for suturing. Cumulative sum analysis suggests completion of the suturing learning curve by trial 4. An expanded dataset is needed to facilitate subset analyses.

Is Experience in Hemodialysis Cannulation Related to Expertise? A Metrics-based Investigation for Skills Assessment

Cannulation is not only one of the most common medical procedures but also fraught with complications. The skill of the clinician performing cannulation directly impacts cannulation outcomes. However, current methods of teaching this skill are deficient, relying on subjective demonstrations and unrealistic manikins that have limited utility for skills training. Furthermore, of the factors that hinders effective continuing medical education is the assumption that clinical experience results in expertise. In this work, we examine if objective metrics acquired from a novel cannulation simulator are able to distinguish between experienced clinicians and established experts, enabling the measurement of true expertise. Twenty-two healthcare professionals, who practiced cannulation with varying experience, performed a simulated arteriovenous fistula cannulation task on the simulator. Four clinicians were peer-identified as experts while the others were designated to the experienced group. The simulator tracked the motion of the needle (via an electromagnetic sensor), rendered blood flashback function (via an infrared light sensor), and recorded pinch forces exerted on the needle (via force sensing elements). Metrics were computed based on motion, force, and other sensor data. Results indicated that, with near 80% of accuracy using both logistic regression and linear discriminant analysis, the objective metrics differentiated between experts and the experienced, including identifying needle motion and finger force as two prominent features that distinguished between the groups. Furthermore, results indicated that expertise was not correlated with years of experience, validating the central hypothesis of the study. These insights contribute to structured and standardized medical skills training by enabling a meaningful definition of expertise and could potentially lead to more effective skills training methods.

Objective and automated assessment of surgical technical skills with IoT systems: A systematic literature review

The assessment of surgical technical skills to be acquired by novice surgeons has been traditionally done by an expert surgeon and is therefore of a subjective nature. Nevertheless, the recent advances on IoT (Internet of Things), the possibility of incorporating sensors into objects and environments in order to collect large amounts of data, and the progress on machine learning are facilitating a more objective and automated assessment of surgical technical skills. This paper presents a systematic literature review of papers published after 2013 discussing the objective and automated assessment of surgical technical skills. 101 out of an initial list of 537 papers were analyzed to identify: 1) the sensors used; 2) the data collected by these sensors and the relationship between these data, surgical technical skills and surgeons' levels of expertise; 3) the statistical methods and algorithms used to process these data; and 4) the feedback provided based on the outputs of these statistical methods and algorithms. Particularly, 1) mechanical and electromagnetic sensors are widely used for tool tracking, while inertial measurement units are widely used for body tracking; 2) path length, number of sub-movements, smoothness, fixation, saccade and total time are the main indicators obtained from raw data and serve to assess surgical technical skills such as economy, efficiency, hand tremor, or mind control, and distinguish between two or three levels of expertise (novice/intermediate/advanced surgeons); 3) SVM (Support Vector Machines) and Neural Networks are the preferred statistical methods and algorithms for processing the data collected, while new opportunities are opened up to combine various algorithms and use deep learning; and 4) feedback is provided by matching performance indicators and a lexicon of words and visualizations, although there is considerable room for research in the context of feedback and visualizations, taking, for example, ideas from learning analytics.

Enhanced Training Benefits of Video Recording Surgery With Automated Hand Motion Analysis

Background

Hand motion analysis by video recording during surgery has potential for evaluation of surgical performance. The aim was to identify how technical skill during open surgery can be measured unobtrusively by video recording during a surgical procedure. We hypothesized that procedural-step timing, hand movements, instrument use and Shannon entropy differ with expertise and training and are concordant with a performance-based validated individual procedure score.

Methods

Surgeon and non-surgeon participants with varying training and levels of expertise were video recorded performing axillary artery exposure and control (AA) on un-preserved cadavers. Color-coded gloves permitted motion-tracking and automated extraction of entropy data from recordings. Timing and instrument-use metrics were obtained through observational video reviews. Shannon entropy measured speed, acceleration and direction by computer-vision algorithms. Findings were compared with individual procedure score for AA performance

Results

Experts had lowest entropy values, idle time, active time and shorter time to divide pectoralis minor, using fewer instruments. Residents improved with training, without reaching expert levels, and showed deterioration 12–18 months later. Individual procedure scores mirrored these results. Non-surgeons differed substantially.

Conclusions

Hand motion entropy and timing metrics discriminate levels of surgical skill and training, and these findings are congruent with individual procedure score evaluations. These measures can be collected using consumer-level cameras and analyzed automatically with free software. Hand motion with video timing data may have widespread application to evaluate resident performance and can contribute to the range of evaluation and testing modalities available to educators, training course designers and surgical quality assurance programs.

Supplementary Information

The online version of this article (10.1007/s00268-020-05916-1) contains supplementary material, which is available to authorized users.

Frontal theta brain activity varies as a function of surgical experience and task error

OBJECTIVE

Investigations into surgical expertise have almost exclusively focused on overt behavioral characteristics with little consideration of the underlying neural processes. Recent advances in neuroimaging technologies, for example, wireless, wearable scalp-recorded electroencephalography (EEG), allow an insight into the neural processes governing performance. We used scalp-recorded EEG to examine whether surgical expertise and task performance could be differentiated according to an oscillatory brain activity signal known as frontal theta-a putative biomarker for cognitive control processes.

DESIGN SETTING AND PARTICIPANTS

Behavioral and EEG data were acquired from dental surgery trainees with 1 year (n=25) and 4 years of experience (n=20) while they performed low and high difficulty drilling tasks on a virtual reality surgical simulator. EEG power in the 4-7 Hz range in frontal electrodes (indexing frontal theta) was examined as a function of experience, task difficulty and error rate.

RESULTS

Frontal theta power was greater for novices relative to experts (p=0.001), but did not vary according to task difficulty (p=0.15) and there was no Experience × Difficulty interaction (p=0.87). Brain-behavior correlations revealed a significant negative relationship between frontal theta and error in the experienced group for the difficult task (r=-0.594, p=0.0058), but no such relationship emerged for novices.

CONCLUSION

We find frontal theta power differentiates between surgical experiences but correlates only with error rates for experienced surgeons while performing difficult tasks. These results provide a novel perspective on the relationship between expertise and surgical performance.

Motion analysis of the JHU-ISI Gesture and Skill Assessment Working Set using Robotics Video and Motion Assessment Software

Purpose

The JIGSAWS dataset is a fixed dataset of robot-assisted surgery kinematic data used to develop predictive models of skill. The purpose of this study is to analyze the relationships of self-defined skill level with global rating scale scores and kinematic data (time, path length and movements) from three exercises (suturing, knot-tying and needle passing) (right and left hands) in the JIGSAWS dataset.

Methods

Global rating scale scores are reported in the JIGSAWS dataset and kinematic data were calculated using ROVIMAS software. Self-defined skill levels are in the dataset (novice, intermediate, expert). Correlation coefficients (global rating scale-skill level and global rating scale-kinematic parameters) were calculated. Kinematic parameters were compared among skill levels.

Results

Global rating scale scores correlated with skill in the knot-tying exercise (r = 0.55, p = 0.0005). In the suturing exercise, time, path length (left) and movements (left) were significantly different (p < 0.05) for novices and experts. For knot-tying, time, path length (right and left) and movements (right) differed significantly for novices and experts. For needle passing, no kinematic parameter was significantly different comparing novices and experts. The only kinematic parameter that correlated with global rating scale scores is time in the knot-tying exercise.

Conclusion

Global rating scale scores weakly correlate with skill level and kinematic parameters. The ability of kinematic parameters to differentiate among self-defined skill levels is inconsistent. Additional data are needed to enhance the dataset and facilitate subset analyses and future model development.

[Training simulation during the announcement of fetal malformation discovered on screening ultrasound: Results of a pilot study]

OBJECTIVES

Breaking bad news (BBN) to a pregnant woman with fetal abnormalities (FA) on ultrasound (US) examination is a challenge. Announcement technique influences patient reaction. Physicians receive little training in BBN. The simulation and using a BBN protocol as the English SPIKES protocol which guides the announcement consultation according to 6 steps (Setting Up, Perception, Invitation, Knowledge, Emotions and Empathy, Strategy and Summary) can be used for this teaching. The objective was to assess feasibility simulation scenarii of BBN for FA discovered during US and to evaluate the usefulness of SPIKES protocol in this situation.

METHODS

Two scenarios have been created combining US simulator (US Mentor, Symbionix®) with simulated patient (SP). Scenarii objectives were to diagnose FA and break it to SP. Checklist derived from SPIKES was fulfilled by two investigators thanks to video recording, the SP and every participant (residents, physicians, fetal medicine specialists [FMS]). Participants filled out survey about the usefulness of this exercise too.

RESULTS

Nine physicians (3 residents, 4 physicians, 2 FMS) produced 18 scenarii. Seventy-eight percent of physicians thought simulation was like real situation of BBN during US examination. Majority of participant (88%) found that this simulation training could help them to increase their ability to BBN and that it can be used to teach residents (89%) or physicians (100%). FMS had better SPIKES checklist than physicians (P<0,05).

CONCLUSION

Simulation scenario of BBN for FA discovered during US is feasible by combining US simulator and SP. SPIKES protocol can be useful but a validated checklist should be created.

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.

Effective Catheter Manoeuvre for the Removal of Phlegm by Suctioning: A Biomechanical Analysis of Experts and Novices

Purpose

The aim of this study was to determine the effective biomechanical technique for suctioning phlegm.

Methods

A novel tracheal suctioning simulator combined with a motion capture system was used to calculate the amount of simulated phlegm suctioned and the biomechanical parameters of the associated suctioning manoeuvre. A laboratory study, including 12 nurses with > 3 years of suctioning experience and 12 nursing students without any clinical suctioning experience, was conducted. The amount of phlegm suctioned, the maximum length of catheter insertion, and the biomechanical parameters of hand movement were calculated.

Results

The mean amount of phlegm suctioned per second was significantly larger in the experienced group than in the non-experienced group. The amount of phlegm suctioned correlated positively with the length of the vertical path of motion of the wrist and forearm, and with the angular velocity of thumb rotation in both the groups.

Conclusion

Greater vertical motion of the wrist and thumb rotation improved the effectiveness of phlegm suctioning and prevented the need for deep suctioning, which is unsafe.

Using Surgeon Hand Motions to Predict Surgical Maneuvers

OBJECTIVE

This study explores how common machine learning techniques can predict surgical maneuvers from a continuous video record of surgical benchtop simulations.

BACKGROUND

Automatic computer vision recognition of surgical maneuvers (suturing, tying, and transition) could expedite video review and objective assessment of surgeries.

METHOD

We recorded hand movements of 37 clinicians performing simple and running subcuticular suturing benchtop simulations, and applied three machine learning techniques (decision trees, random forests, and hidden Markov models) to classify surgical maneuvers every 2 s (60 frames) of video.

RESULTS

Random forest predictions of surgical video correctly classified 74% of all video segments into suturing, tying, and transition states for a randomly selected test set. Hidden Markov model adjustments improved the random forest predictions to 79% for simple interrupted suturing on a subset of randomly selected participants.

CONCLUSION

Random forest predictions aided by hidden Markov modeling provided the best prediction of surgical maneuvers. Training of models across all users improved prediction accuracy by 10% compared with a random selection of participants.

APPLICATION

Marker-less video hand tracking can predict surgical maneuvers from a continuous video record with similar accuracy as robot-assisted surgical platforms, and may enable more efficient video review of surgical procedures for training and coaching.

Objective classification of psychomotor laparoscopic skills of surgeons based on three different approaches

BACKGROUND

The determination of surgeons' psychomotor skills in minimally invasive surgery techniques is one of the major concerns of the programs of surgical training in several hospitals. Therefore, it is important to assess and classify objectively the level of experience of surgeons and residents during their training process. The aim of this study was to investigate three classification methods for establishing automatically the level of surgical competence of the surgeons based on their psychomotor laparoscopic skills.

METHODS

A total of 43 participants, divided into an experienced surgeons group with ten experts (> 100 laparoscopic procedures performed) and non-experienced surgeons group with 24 residents and nine medical students (< 10 laparoscopic procedures performed), performed three tasks in the EndoViS training system. Motion data of the instruments were captured with a video-tracking system built into the EndoViS simulator and analyzed using 13 motion analysis parameters (MAPs). Radial basis function networks (RBFNets), K-star (K*), and random forest (RF) were used for classifying surgeons based on the MAPs' scores of all participants. The performance of the three classifiers was examined using hold-out and leave-one-out validation techniques.

RESULTS

For all three tasks, the K-star method was superior in terms of accuracy and AUC in both validation techniques. The mean accuracy of the classifiers was 93.33% for K-star, 87.58% for RBFNets, and 84.85% for RF in hold-out validation, and 91.47% for K-star, 89.92% for RBFNets, and 83.72% for RF in leave-one-out cross-validation.

CONCLUSIONS

The three proposed methods demonstrated high performance in the classification of laparoscopic surgeons, according to their level of psychomotor skills. Together with motion analysis and three laparoscopic tasks of the Fundamental Laparoscopic Surgery Program, these classifiers provide a means for objectively classifying surgical competence of the surgeons for existing laparoscopic box trainers.

A Comparison of the Effectiveness Between Three Different Endoscopic Surgical Skill Training Programs for Medical Students Using the Infant Laparoscopic Fundoplication Simulator: A Randomized Controlled Trial

Background and Aim: Training programs for developing laparoscopic suturing skills range from dry-box training to virtual simulator training. There are advantages and disadvantages to each training method; however, which training is best for medical students and young surgeons is unclear. The aim of this study was to compare the proficiency of medical students in acquiring laparoscopic suturing skills after various routes of short-term training: via a video, an expert teacher, or a virtual simulator. Materials and Methods: Seventeen medical students were registered and divided randomly into three groups: group receiving personal training while watching a training video (video group), group receiving training under the guidance of an expert (teaching group), and group receiving personal training with a virtual simulator (virtual group). The students practiced laparoscopic suturing and tying skills for 1 hour. Following their training, they performed the evaluation task of three sutures and ties using a laparoscopic fundoplication simulator. We developed a 1-year-old infant body model (body weight 10 kg) based on computed tomography data and established a pneumoperitoneum body model based on a clinical situation. Results: The path length of the assistant forceps in the virtual group tended to be longer than in the other groups. The average acceleration of the assistant forceps in the virtual group was faster than in the other groups (P = .04). There were no significant differences in the other evaluation parameters. Conclusion: A long-term and combination training study should be performed to develop the best method for training medical students and inexperienced young surgeons.

Surgical skill levels: Classification and analysis using deep neural network model and motion signals

BACKGROUND AND OBJECTIVES

Currently, the assessment of surgical skills relies primarily on the observations of expert surgeons. This may be time-consuming, non-scalable, inconsistent and subjective. Therefore, an automated system that can objectively identify the actual skills level of a junior trainee is highly desirable. This study aims to design an automated surgical skills evaluation system.

METHODS

We propose to use a deep neural network model that can analyze raw surgical motion data with minimal preprocessing. A platform with inertial measurement unit sensors was developed and participants with different levels of surgical experience were recruited to perform core open surgical skills tasks. JIGSAWS a publicly available robot based surgical training dataset was used to evaluate the generalization of our deep network model. 15 participants (4 experts, 4 intermediates and 7 novices) were recruited into the study.

RESULTS

The proposed deep model achieved an accuracy of 98.2%. With comparison to JIGSAWS; our method outperformed some existing approaches with an accuracy of 98.4%, 98.4% and 94.7% for suturing, needle-passing, and knot-tying, respectively. The experimental results demonstrated the applicability of this method in both open surgery and robot-assisted minimally invasive surgery.

CONCLUSIONS

This study demonstrated the potential ability of the proposed deep network model to learn the discriminative features between different surgical skills levels.

Can Haptic Simulators Distinguish Expert Performance? A Case Study in Central Venous Catheterization in Surgical Education

INTRODUCTION

High-tech simulators are gaining popularity in surgical training programs because of their potential for improving clinical outcomes. However, most simulators are static in nature and only represent a single anatomical patient configuration. The Dynamic Haptic Robotic Training (DHRT) system was developed to simulate these diverse patient anatomies during Central Venous Catheterization (CVC) training. This article explores the use of the DHRT system to evaluate objective metrics for CVC insertion by comparing the performance of experts and novices.

METHODS

Eleven expert surgeons and 13 first-year surgical residents (novices) performed multiple needle insertion trials on the DHRT system. Differences between expert and novice performance on the following five metrics were assessed using a multivariate analysis of variance: path length, standard deviation of deviations (SDoD), average velocity, distance to the center of the vessel, and time to complete (TtC) the needle insertion. A regression analysis was performed to identify if expertise could be predicted using these metrics. Then, a curve fit was conducted to identify whether learning curves were present for experts or novices on any of these five metrics.

RESULTS

Time to complete the insertion and SDoD of the needle tip from an ideal path were significantly different between experts and novices. Learning curves were not present for experts but indicated a significant decrease in path length and TtC for novices.

CONCLUSIONS

The DHRT system was able to identify significant differences in TtC and SDoD between experts and novices during CVC needle insertion procedures. In addition, novices were shown to improve their skills through DHRT training.

A Mobile Acquisition System and a Method for Hips Sway Fluency Assessment

The present contribution focuses on the estimation of the Cartesian kinematic jerk of the hips’ orientation during a full three-dimensional movement in the context of enabling eHealth applications of advanced mathematical signal analysis. The kinematic jerk index is estimated on the basis of gyroscopic signals acquired offline through a smartphone. A specific free mobile application is used to acquire the gyroscopic signals and to transmit them to a personal computer through a wireless network. The personal computer elaborates the acquired data and returns the kinematic jerk index associated with a motor task. A comparison of the kinematic jerk index value on a number of data sets confirms that such index can be used to evaluate the fluency of hips orientation during motion. The present research confirms that the proposed gyroscopic data acquisition/processing setup constitutes an inexpensive and portable solution to motion fluency analysis. The proposed data-acquisition and data-processing setup may serve as a supporting eHealth technology in clinical bio-mechanics as well as in sports science.

Artificial Neural Network for Laparoscopic Skills Classification Using Motion Signals from Apple Watch

The acquisition of laparoscopic technical skills is constrained by the limited training opportunities and the necessity of having staff physicians on site to provide feedback to the trainees. In addition, the assessment tools used to measure trainees performance are not always sensitive enough to detect different levels of expertise. To address this problem, two Apple Watches worn on inexperienced subjects in laparoscopy were used to record their motion signals (attitude, rotation rate and acceleration) during multiple practices of the peg transfer task in a fundamentals of laparoscopic surgery (FLS) trainer box. This training process was carried out through a massed practice methodology (two hours of training), in which subjects were assessed following the guidelines of the FLS program. Subsequently, a series of metrics were estimated from the acquired motion signals and the Spearman's rank correlation coefficient was used to select the most statistically significant attributes. Then, a classification model based on artificial neural networks was trained, using these attributes as model inputs, to classify trainees according to their level of expertise into three classes: low, intermediate and high. Using this approach, an average classification performance of F1=86.11% was achieved on a test subset. This suggests that new technologies, such as smartwatches, can be used to complement surgical training by including motion-based metrics to improve current clinical education and offering a new source of feedback through objective assessment.

Eye-Hand Coordination Patterns of Intermediate and Novice Surgeons in a Simulation-Based Endoscopic Surgery Training Environment

Endoscopic surgery procedures require specific skills, such as eye-hand coordination to be developed. Current education programs are facing with problems to provide appropriate skill improvement and assessment methods in this field. This study aims to propose objec-tive metrics for hand-movement skills and assess eye-hand coordination. An experimental study is conducted with 15 surgical residents to test the newly proposed measures. Two computer-based both-handed endoscopic surgery practice scenarios are developed in a simulation environment to gather the participants' eye-gaze data with the help of an eye tracker as well as the related hand movement data through haptic interfaces. Additionally, participants' eye-hand coordination skills are analyzed. The results indicate higher correla-tions in the intermediates' eye-hand movements compared to the novices. An increase in intermediates' visual concentration leads to smoother hand movements. Similarly, the novices' hand movements are shown to remain at a standstill. After the first round of practice, all participants' eye-hand coordination skills are improved on the specific task targeted in this study. According to these results, it can be concluded that the proposed metrics can potentially provide some additional insights about trainees' eye-hand coordi-nation skills and help instructional system designers to better address training requirements.

Upper body position analysis of different experience level surgeons during laparoscopic suturing maneuvers using optical motion capture

BACKGROUND

This study aimed to analyze the posture patterns of surgeons with two different skill levels during laparoscopic surgery using an optical motion capture system.

METHODS

Twenty participants were divided into novice and expert groups. Their upper body motions during suturing tasks were captured, including average angle and angle variability (shoulder, elbow, wrist), joint fixation, head movement, and thoracolumbar flexion angle.

RESULTS

Our analysis showed that (1) the arms of the expert surgeons were more loosely held at their sides by about 7°; (2) their elbows were more bent by about 10°; (3) they had a greater change in shoulder angle by about 1.4 times and a more fluid posture; (4) their heads were more stable, particularly in the longitudinal and vertical axes; and (5) their thoracolumbar flexion angle was smaller by about 10°.

CONCLUSIONS

The posture patterns of different technical level surgeons during laparoscopic suturing maneuvers revealed differences in limb positions. These results may provide new insights into the efficient acquisition of technical skills and reduced physical stress during laparoscopic surgery.

Human-centric predictive model of task difficulty for human-in-the-loop control tasks

Quantitatively measuring the difficulty of a manipulation task in human-in-the-loop control systems is ill-defined. Currently, systems are typically evaluated through task-specific performance measures and post-experiment user surveys; however, these methods do not capture the real-time experience of human users. In this study, we propose to analyze and predict the difficulty of a bivariate pointing task, with a haptic device interface, using human-centric measurement data in terms of cognition, physical effort, and motion kinematics. Noninvasive sensors were used to record the multimodal response of human user for 14 subjects performing the task. A data-driven approach for predicting task difficulty was implemented based on several task-independent metrics. We compare four possible models for predicting task difficulty to evaluated the roles of the various types of metrics, including: (I) a movement time model, (II) a fusion model using both physiological and kinematic metrics, (III) a model only with kinematic metrics, and (IV) a model only with physiological metrics. The results show significant correlation between task difficulty and the user sensorimotor response. The fusion model, integrating user physiology and motion kinematics, provided the best estimate of task difficulty (R2 = 0.927), followed by a model using only kinematic metrics (R2 = 0.921). Both models were better predictors of task difficulty than the movement time model (R2 = 0.847), derived from Fitt's law, a well studied difficulty model for human psychomotor control.

Feasibility of an AI-Based Measure of the Hand Motions of Expert and Novice Surgeons

This study investigated whether parameters derived from hand motions of expert and novice surgeons accurately and objectively reflect laparoscopic surgical skill levels using an artificial intelligence system consisting of a three-layer chaos neural network. Sixty-seven surgeons (23 experts and 44 novices) performed a laparoscopic skill assessment task while their hand motions were recorded using a magnetic tracking sensor. Eight parameters evaluated as measures of skill in a previous study were used as inputs to the neural network. Optimization of the neural network was achieved after seven trials with a training dataset of 38 surgeons, with a correct judgment ratio of 0.99. The neural network that prospectively worked with the remaining 29 surgeons had a correct judgment rate of 79% for distinguishing between expert and novice surgeons. In conclusion, our artificial intelligence system distinguished between expert and novice surgeons among surgeons with unknown skill levels.

From dV-Trainer to Real Robotic Console: The Limitations of Robotic Skill Training

OBJECTIVES

To investigate operators' performance quality, mental stress, and ergonomic habits through a training curriculum on robotic simulators.

DESIGN

Forty volunteers without robotic surgery experience were recruited to practice 2 exercises on a dV-Trainer (dVT) for 14 hours. The simulator software (M-score^a) provided an automatic evaluation of the overall score for the surgeons' performance. Each participant provided a subjective difficulty score (validity to be proven) for each exercise. Their ergonomic habits were evaluated based on the workspace range and armrest load-validated criteria for evaluating the proficiency of using the armrest. They then repeated the same tasks on a da Vinci Surgical Skill Simulator for a final-level test. Their final scores were compared with their initial scores and the scores of 5 experts on the da Vinci Surgical Skill Simulator.

RESULTS

A total of 14 hours of training on the dVT significantly improved the surgeons' performance scores to the expert level with a significantly reduced workload, but their ergonomic score was still far from the expert level.

CONCLUSION

Sufficient training on the dVT improves novices' performance, reduces psychological stress, and inculcates better ergonomic habits. Among the evaluated criteria, novices had the most difficulty in achieving expert levels of ergonomic skills. The training benefits of robotic surgery simulators should be determined with quantified variables. The detection of the limitations during robotic training curricula could guide the targeted training and improve the training effect.

An Automated Methodology for Assessing Anatomy-Specific Instrument Motion during Endoscopic Endonasal Skull Base Surgery

Objectives  Describe instrument motion during live endoscopic skull base surgery (ESBS) and evaluate kinematics within anatomic regions. Design  Case series. Setting  Tertiary academic center. Participants  A single skull base surgeon performed six anterior skull base approaches to the pituitary. Main Outcomes and Measures  Time-stamped instrument coordinates were recorded using an optical tracking system. Kinematics (i.e., mean cumulative instrument travel, velocity, acceleration, and angular velocity) was calculated by anatomic region including nasal vestibule, anterior and posterior ethmoid, sphenoid, and lateral opticocarotid recess (lOCR) regions. Results  We observed mean (standard deviation, SD) velocities of 6.14 cm/s (1.55) in the nasal vestibule versus 1.65 cm/s (0.34) near the lOCR. Mean (SD) acceleration was 7,480 cm/s ² (5790) in the vestibule versus 928 cm/s ² (662) near the lOCR. Mean (SD) angular velocity was 17.2 degrees/s (8.31) in the vestibule and 5.37 degrees/s (1.09) near the lOCR. We observed a decreasing trend in the geometric mean velocity, acceleration, and angular velocity when approaching the pituitary ( p  < 0.001). Conclusion  Using a novel method for analyzing instrument motion during live ESBS, we observed a decreasing trend in kinematics with proximity to the pituitary. Additional characterization of surgical instrument motion is paramount for optimizing patient safety and training.

An Instrumented Glove to Assess Manual Dexterity in Simulation-Based Neurosurgical Education

The traditional neurosurgical apprenticeship scheme includes the assessment of trainee’s manual skills carried out by experienced surgeons. However, the introduction of surgical simulation technology presents a new paradigm where residents can refine surgical techniques on a simulator before putting them into practice in real patients. Unfortunately, in this new scheme, an experienced surgeon will not always be available to evaluate trainee’s performance. For this reason, it is necessary to develop automatic mechanisms to estimate metrics for assessing manual dexterity in a quantitative way. Authors have proposed some hardware-software approaches to evaluate manual dexterity on surgical simulators. This paper presents IGlove, a wearable device that uses inertial sensors embedded on an elastic glove to capture hand movements. Metrics to assess manual dexterity are estimated from sensors signals using data processing and information analysis algorithms. It has been designed to be used with a neurosurgical simulator called Daubara NS Trainer, but can be easily adapted to another benchtop- and manikin-based medical simulators. The system was tested with a sample of 14 volunteers who performed a test that was designed to simultaneously evaluate their fine motor skills and the IGlove’s functionalities. Metrics obtained by each of the participants are presented as results in this work; it is also shown how these metrics are used to automatically evaluate the level of manual dexterity of each volunteer.

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.

Preoperative simulation regarding the appropriate port location for laparoscopic hepaticojejunostomy: a randomized study using a disease-specific training simulator

PURPOSE

We verified the appropriate port location for laparoscopic hepaticojejunostomy using a comprehensive laparoscopic training simulator.

METHODS

We developed a hepaticojejunostomy model, consist of common hepatic duct and intestine and participants required to place two sutures precisely using two different port locations (A: standard port location, B: modified port location). The order of tasks was randomly determined using the permuted block method (Group I: Task A → Task B, Group II: Task B → Task A). The time for task completion and total number of errors were recorded. In addition, we evaluated the spatial paths and velocity of both forceps. Statistical analyses were performed using a statistical software program.

RESULTS

The time for the task, the total error score, and the spatial paths and velocity of both forceps were not significantly different between groups I and II. Furthermore, the port location and order of tasks (group I or group II) did not significantly affect the results. In contrast, there were significant differences in the performance between experts and novices, who were classified as such based on the total number of experienced endoscopic surgeries.

CONCLUSION

Preoperative port simulation in advanced surgery using our artificial simulator is feasible and may facilitate minimally invasive surgery for children.

A new innovative laparoscopic fundoplication training simulator with a surgical skill validation system

PURPOSE

We developed and validated a specific laparoscopic fundoplication simulator for use with the objective endoscopic surgical skills evaluation system. The aim of this study was to verify the quality of skills of surgeons.

MATERIALS AND METHODS

We developed a 1-year-old infant body model based on computed tomography data and reproduced pneumoperitoneum model based on the clinical situation. The examinees were divided into three groups: fifteen pediatric surgery experts (PSE), twenty-four pediatric surgery trainees (PSN), and ten general surgeons (GS). They each had to perform three sutures ligatures for construction of Nissen wrap. Evaluate points are time for task, the symmetry of the placement of the sutures, and the uniformity of the interval of suture ligatures in making wrap. And the total path length and velocity of forceps were measured to assess bi-hand coordination.

RESULTS

PSE were significantly superior to PSN regarding total time spent (p < 0.01) and total path length (p < 0.01). GS used both forceps faster than the other groups, and PSN used the right forceps faster than the left forceps (p < 0.05). PSE were shorter with regard to the total path length than GS (p < 0.01). PSE showed most excellent results in the symmetry of the wrap among three groups.

CONCLUSION

Our new model was used useful to validate the characteristics between GS and pediatric surgeon. Both PSE and GS have excellent bi-hand coordination and can manipulate both forceps equally and had superior skills compared to PSN. In addition, PSE performed most compact and accurate skills in the conflicted operative space.

The effect of forceps manipulation for expert pediatric surgeons using an endoscopic pseudo-viewpoint alternating system: the phenomenon of economical slow and fast performance in endoscopic surgery

PURPOSE

Endoscopic surgery is performed under a horizontal view in comparison to the vertical view that is associated with open surgery. We developed an endoscopic pseudo-viewpoint alternation system with out any scope action. We investigate the effect of this novel system on forceps manipulation among expert pediatric surgeons.

METHODS

Six expert pediatric surgeons performed a Nissen wrap in a fundoplication simulator either with or without this system. The constructed Nissen wrap was evaluated. The total path length and the average velocity of the forceps were also analyzed.

RESULTS

The times required either with or without this system were 587.5 ± 122.7 and 634.0 ± 212.4 s (p = 0.45), respectively. The total path lengths of right and left forceps either with or without this system were 12,309 ± 2495.5 and 15,726 ± 5649.6 mm (p = 0.07), 10,091 ± 2439.2 and 12,575 ± 5511.1 mm (p = 0.11), respectively. The average velocity of the right and left forceps with or without this system were 26.9 ± 5.29 and 31.6 ± 1.62 mm/s (p = 0.04), 21.6 ± 2.48 and 25.5 ± 6.48 mm/s (p = 0.15), respectively. There was no significance in the suture balance and suture interval.

CONCLUSION

The endoscopic pseudo-viewpoint alternation system thus made it possible for expert pediatric surgeons to carry out slow and economical forceps manipulation. These effects make it possible for surgeons to perform safe and precise surgery, thus leading to a shortening of operation time.

Procedural surgical skill assessment in laparoscopic training environments

PURPOSE

This study aimed to identify detailed differences in laparoscopic surgical processes between expert and novice surgeons in a training environment and demonstrate that surgical process modeling can be used for such detailed analysis.

METHODS

Eleven expert surgeons each of whom had performed [Formula: see text] laparoscopic procedures were compared with 10 young surgeons each of whom had performed [Formula: see text] laparoscopic procedures, and five medical students. Each examinee performed a specific skill assessment task. During tasks, instrument motion was monitored using a video capture system. From the video, the corresponding workflow was recorded by labeling the surgeons' activities according to a predefined terminology. Activities represented manual work steps performed during the task, described by a combination of a verb (representing the action), a tool, and the involved structure. The results were described as the number of occurrences (times), average duration (seconds), total duration (seconds), minimal duration (seconds), maximal duration (seconds), and occupancy percentage (%).

RESULTS

The terminology for describing the processes of this task included 10 actions, six tools, four structures, and three events for each hand. There were 63 combinations of different possible activities; significant differences in 12 activities were observed between the expert and novice groups (young surgeons and medical students). The expert group performed the task with fewer occurrences and shorter duration than did the novice group in the left hand.

CONCLUSIONS

We identified differences in surgical process between experts and novices in laparoscopic surgical simulation. Our proposed method would be useful for education and training in laparoscopic surgery.

Exploring personality dimensions that influence practice and performance of a simulated laparoscopic task in the objective structured clinical examination

BACKGROUND

Surgical educators have encouraged the investigation of individual differences in aptitude and personality in surgical performance. An individual personality difference that has been shown to influence laparoscopic performance under time pressure is movement specific reinvestment. Movement specific reinvestment has 2 dimensions, movement self-consciousness (MS-C) (i.e., the propensity to consciously monitor movements) and conscious motor processing (CMP) (i.e., the propensity to consciously control movements), which have been shown to differentially influence laparoscopic performance in practice but have yet to be investigated in the context of psychological stress (e.g., the objective structured clinical examination [OSCE]).

OBJECTIVE

This study investigated the role of individual differences in propensity for MS-C and CMP in practice of a fundamental laparoscopic skill and in laparoscopic performance during the OSCE. Furthermore, this study examined whether individual differences during practice of a fundamental laparoscopic skill were predictive of laparoscopic performance during the OSCE.

METHODS

Overall, 77 final-year undergraduate medical students completed the movement specific reinvestment scale, an assessment tool that quantifies the propensity for MS-C and CMP. Participants were trained to proficiency on a fundamental laparoscopic skill. The number of trials to reach proficiency was measured, and completion times were recorded during early practice, later practice, and the OSCE.

RESULTS

There was a trend for CMP to be negatively associated with the number of trials to reach proficiency (p = 0.064). A higher propensity for CMP was associated with fewer trials to reach proficiency (β = -0.70, p = 0.023). CMP and MS-C did not significantly predict completion times in the OSCE (p > 0.05). Completion times in early practice (β = 0.05, p = 0.016) and later practice (β = 0.47, p < 0.001) and number of trials to reach proficiency (β = 0.23, p = 0.003) significantly predicted completion times in the OSCE.

CONCLUSION

It appears that a higher propensity for CMP predicts faster rates of learning of a fundamental laparoscopic skill. Furthermore, laparoscopic performance during practice is indicative of laparoscopic performance in the challenging conditions of the OSCE. The lack of association between the 2 dimensions of movement specific reinvestment and performance during the OSCE is explained using the theory of reinvestment as a framework. Overall, consideration of personality differences and individual differences in ability during practice could help inform the development of individualized surgical training programs.