Laparoscopic training using a quantitative assessment and instructional system

Surgical tooltip motion metrics assessment using virtual marker: an objective approach to skill assessment for minimally invasive surgery

PURPOSE

Surgical skill assessment has primarily been performed using checklists or rating scales, which are prone to bias and subjectivity. To tackle this shortcoming, assessment of surgical tool motion can be implemented to objectively classify skill levels. Due to the challenges involved in motion tracking of surgical tooltips in minimally invasive surgeries, formerly used assessment approaches may not be feasible for real-world skill assessment. We proposed an assessment approach based on the virtual marker on surgical tooltips to derive the tooltip's 3D position and introduced a novel metric for surgical skill assessment.

METHODS

We obtained the 3D tooltip position based on markers placed on the tool handle. Then, we derived tooltip motion metrics to identify the metrics differentiating the skill levels for objective surgical skill assessment. We proposed a new tooltip motion metric, i.e., motion inconsistency, that can assess the skill level, and also can evaluate the stage of skill learning. In this study, peg transfer, dual transfer, and rubber band translocation tasks were included, and nine novices, five surgical residents and five attending general surgeons participated.

RESULTS

Our analyses showed that tooltip path length (p [Formula: see text] 0.007) and path length along the instrument axis (p [Formula: see text] 0.014) differed across the three skill levels in all the tasks and decreased by skill level. Tooltip motion inconsistency showed significant differences among the three skill levels in the dual transfer (p [Formula: see text] 0.025) and the rubber band translocation tasks (p [Formula: see text] 0.021). Lastly, bimanual dexterity differed across the three skill levels in all the tasks (p [Formula: see text] 0.012) and increased by skill level.

CONCLUSION

Depth perception ability (indicated by shorter tooltip path lengths along the instrument axis), bimanual dexterity, tooltip motion consistency, and economical tooltip movements (shorter tooltip path lengths) are related to surgical skill. Our findings can contribute to objective surgical skill assessment, reducing subjectivity, bias, and associated costs.

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.

A system for real-time multivariate feature combination of endoscopic mitral valve simulator training data

Purpose

For an in-depth analysis of the learning benefits that a stereoscopic view presents during endoscopic training, surgeons required a custom surgical evaluation system enabling simulator independent evaluation of endoscopic skills. Automated surgical skill assessment is in dire need since supervised training sessions and video analysis of recorded endoscope data are very time-consuming. This paper presents a first step towards a multimodal training evaluation system, which is not restricted to certain training setups and fixed evaluation metrics.

Methods

With our system we performed data fusion of motion and muscle-action measurements during multiple endoscopic exercises. The exercises were performed by medical experts with different surgical skill levels, using either two or three-dimensional endoscopic imaging. Based on the multi-modal measurements, training features were calculated and their significance assessed by distance and variance analysis. Finally, the features were used automatic classification of the used endoscope modes.

Results

During the study, 324 datasets from 12 participating volunteers were recorded, consisting of spatial information from the participants’ joint and right forearm electromyographic information. Feature significance analysis showed distinctive significance differences, with amplitude-related muscle information and velocity information from hand and wrist being among the most significant ones. The analyzed and generated classification models exceeded a correct prediction rate of used endoscope type accuracy rate of 90%.

Conclusion

The results support the validity of our setup and feature calculation, while their analysis shows significant distinctions and can be used to identify the used endoscopic view mode, something not apparent when analyzing time tables of each exercise attempt. The presented work is therefore a first step toward future developments, with which multivariate feature vectors can be classified automatically in real-time to evaluate endoscopic training and track learning progress.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11548-022-02588-1.

A cost-effective IoT learning environment for the training and assessment of surgical technical skills with visual learning analytics

BACKGROUND

Surgeons need to train and certify their technical skills. This is usually done with the intervention of experts who monitor and assess trainees. Nevertheless, this is a time-consuming task that is subject to variations among evaluators. In recent decades, subjectivity has been significantly reduced through 1) the introduction of standard curricula, such as the Fundamentals of Laparoscopic Surgery (FLS) program, which measures students' performance in specific exercises, and 2) rubrics, which are widely accepted in the literature and serve to provide feedback about the overall technical skills of the trainees. Although these two elements reduce subjectivity, they do not, however, eliminate the figure of the expert evaluator, and so the process remains time consuming.

OBJECTIVES

The objective of this work is to automate those parts of the work of the expert evaluator that the technology can measure objectively, using sensors to collect evidence, and visualizations to provide feedback. We designed and developed 1) a cost-effective IoT (Internet of Things) learning environment for the training and assessment of surgical technical skills and 2) visualizations supported by the literature on visual learning analytics (VLA) to provide feedback about the exercises (in real time) and overall performance (at the end of the training) of the trainee.

METHODS

A hybrid approach was followed based on previous research for the design of the sensor based IoT learning environment. Previous studies were used as the basis for getting best practices on the tracking of surgical instruments and on the detection of the force applied to the tissue, with a focus on reducing the costs of data collection. The monitoring of the specific exercises required the design of sensors and collection mechanisms from scratch as there is little existing research on this subject. Moreover, it was necessary to design the overall architecture to collect, process, synchronize and communicate the data coming from the different sensors to provide high-level information relevant to the end user. The information to be presented was already validated by the literature and the focus was on how to visualize this information and the optimal time for its presentation to end users. The visualizations were validated with 18 VLA experts assessing the technical aspects of the visualizations and 4 medical experts assessing their functional aspects.

RESULTS

This IoT learning environment amplifies the evaluation mechanisms already validated by the literature, allowing automatic data collection. First, it uses IoT sensors to automatically correct two of the exercises defined in the FLS (peg transfer and precision cutting), providing real-time visualizations. Second it monitors the movement of the surgical instruments and the force applied to the tissues during the exercise, computing 6 of the high-level indicators used by expert evaluators in their rubrics (efficiency, economy of movement, hand tremor, depth perception, bimanual dexterity, and respect for tissue), providing feedback about the technical skills of the trainee using a radar chart with these six indicators at the end of the training (summative visualizations).

CONCLUSIONS

The proposed IoT learning environment is a promising and cost-effective alternative to help in the training and assessment of surgical technical skills. The system shows the trainees' progress and presents new indicators about the correctness of each specific exercise through real-time visualizations, as well as their general technical skills through summative visualizations, aligned with the 6 more frequent indicators in standardized scales. Early results suggest that although both types of visualizations are useful, it is necessary to reduce the cognitive load of the graphs presented in real time during training. Nevertheless, an additional evaluation is needed to confirm these results.

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.

Analysis of Instrument Motion and the Impact of Residency Level and Concurrent Distraction on Laparoscopic Skills

OBJECTIVE

Using a laparoscopic box trainer fitted with motion analysis trackers and software, we aim to identify differences between junior and senior residents performing the peg transfer task, and the impact of a distracting secondary task on performance.

DESIGN

General surgery residents were asked to perform the laparoscopic peg transfer task on a trainer equipped with a motion tracker. They were also asked to perform the laparoscopic task while completing a secondary task. Extreme velocity and acceleration events of instrument movement in the 3 rotational degrees of freedom were measured during task completion. The number of extreme events, defined as velocity or acceleration exceeding 1 SD above or below their own mean, were tabulated. The performance of junior residents was compared to senior residents.

SETTING

Simulation learning institute, Beaumont Hospital, Royal Oak, Michigan.

PARTICIPANTS

Thirty-seven general surgery residents from Beaumont Hospital, Royal Oak.

RESULTS

When completing the primary task alone, senior residents executed significantly fewer extreme motion events specific to acceleration in pitch (16.63 vs. 20.69, p = 0.04), and executed more extreme motion events specific to velocity in roll (16.14 vs. 15.11, p = 0.038), when compared to junior residents. With addition of a secondary task, senior residents had fewer extreme acceleration events specific to pitch, (14.69 vs. 22.22, p < 0.001).

CONCLUSIONS

While junior and senior residents completed the peg transfer task with similar times, motion analysis identified differences in extreme motion events between the groups, even when a secondary task was added. Motion analysis may prove useful for real-time feedback during laparoscopic skill acquisition.

Simulation platforms to assess laparoscopic suturing skills: a scoping review

BACKGROUND

Laparoscopic suturing (LS) has become a common technique used in a variety of advanced laparoscopic procedures. However, LS is a challenging skill to master, and many trainees may not be competent in performing LS at the end of their training. The purpose of this review is to identify simulation platforms available for assessment of LS skills, and determine the characteristics of the platforms and the LS skills that are targeted.

METHODS

A scoping review was conducted between January 1997 and October 2018 for full-text articles. The search was done in various databases. Only articles written in English or French were included. Additional studies were identified through reference lists. The search terms included "laparoscopic suturing" and "clinical competence."

RESULTS

Sixty-two studies were selected. The majority of the simulation platforms were box trainers with inanimate tissue, and targeted basic suturing and intracorporeal knot-tying techniques. Most of the validation came from internal structure (rater reliability) and relationship to other variables (compare training levels/case experience, and various metrics). Consequences were not addressed in any of the studies.

CONCLUSION

We identified many types of simulation platforms that were used for assessing LS skills, with most being for assessment of basic skills. Platforms assessing the competence of trainees for advanced LS skills were limited. Therefore, future research should focus on development of LS tasks that better reflect the needs of the trainees.