Sensorized Psychomotor Skill Assessment Platform Built on a Robotic Surgery Phantom

2021 IEEE 25th International Conference on Intelligent Engineering Systems (INES)

2021

Self Cite

The new kinds of minimally invasive surgical tools and methods require new and complex skillsets from the surgeons, thus the objective skill-based training became essential. This new domain can greatly rely on the automated assessment of surgical skills. There are many devices and approaches to measure different aspects of surgical task accution, however, the results are often hard to interpret. This paper describes an adaptive neuro-fuzzy inference system for the classification of the performance of subjects using a skill assessment device designed for psychomotor skill training for robotic surgical procedures (the FRS Dome). The FRS Dome offers 7 independent surgical tasks, this paper focuses on two of them, since the rest were often impossible to perform for novices. The fuzzy-systems for the two tasks were designed based on 27 performances of subjects with varying skill-levels, the system is capable of scoring the tasks separately, and also scoring the whole performance on all the tasks together on 1-3 scales. In general, the neurofuzzy system is capable of optimizing the classification based on future measurements, thus our method will be able to tune the classification of the rest of the tasks based on upcoming trial results.

Section: Introductionmentioning

confidence: 87%

Section: B Basic Principles Of the Fuzzy Systemsmentioning

confidence: 99%

“…In an earlier paper, we presented a psychomotor skill training device -the sensorized FRS Dome -capable of automated skill assessment [12]. The original FRS Dome (Fig.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Adaptive Neuro-fuzzy Inference System for Automated Skill Assessment in Robot-Assisted Minimally Invasive Surgery

Takacs

2021 IEEE 25th International Conference on Intelligent Engineering Systems (INES)

2021

Self Cite

“…External sensors such as sensory gloves have been used to evaluate surgical skill in open surgery [ 4 ]. Similarly, for MIS, mounted flex sensors have been employed [ 5 ] or the Fundamentals of Robotic Surgery (FRS) Dome was used to gather data [ 6 ].…”

Section: Introductionmentioning

confidence: 99%

Endoscopic Image-Based Skill Assessment in Robot-Assisted Minimally Invasive Surgery

Lajkó

Elek

2021

Sensors

Objective skill assessment-based personal performance feedback is a vital part of surgical training. Either kinematic—acquired through surgical robotic systems, mounted sensors on tooltips or wearable sensors—or visual input data can be employed to perform objective algorithm-driven skill assessment. Kinematic data have been successfully linked with the expertise of surgeons performing Robot-Assisted Minimally Invasive Surgery (RAMIS) procedures, but for traditional, manual Minimally Invasive Surgery (MIS), they are not readily available as a method. 3D visual features-based evaluation methods tend to outperform 2D methods, but their utility is limited and not suited to MIS training, therefore our proposed solution relies on 2D features. The application of additional sensors potentially enhances the performance of either approach. This paper introduces a general 2D image-based solution that enables the creation and application of surgical skill assessment in any training environment. The 2D features were processed using the feature extraction techniques of a previously published benchmark to assess the attainable accuracy. We relied on the JHU–ISI Gesture and Skill Assessment Working Set dataset—co-developed by the Johns Hopkins University and Intuitive Surgical Inc. Using this well-established set gives us the opportunity to comparatively evaluate different feature extraction techniques. The algorithm reached up to 95.74% accuracy in individual trials. The highest mean accuracy—averaged over five cross-validation trials—for the surgical subtask of Knot-Tying was 83.54%, for Needle-Passing 84.23% and for Suturing 81.58%. The proposed method measured well against the state of the art in 2D visual-based skill assessment, with more than 80% accuracy for all three surgical subtasks available in JIGSAWS (Knot-Tying, Suturing and Needle-Passing). By introducing new visual features—such as image-based orientation and image-based collision detection—or, from the evaluation side, utilising other Support Vector Machine kernel methods, tuning the hyperparameters or using other classification methods (e.g., the boosted trees algorithm) instead, classification accuracy can be further improved. We showed the potential use of optical flow as an input for RAMIS skill assessment, highlighting the maximum accuracy achievable with these data by evaluating it with an established skill assessment benchmark, by evaluating its methods independently. The highest performing method, the Residual Neural Network, reached means of 81.89%, 84.23% and 83.54% accuracy for the skills of Suturing, Needle-Passing and Knot-Tying, respectively.

Performance and Capability Assessment in Surgical Subtask Automation

Nagy

2022

Sensors

Robot-Assisted Minimally Invasive Surgery (RAMIS) has reshaped the standard clinical practice during the past two decades. Many believe that the next big step in the advancement of RAMIS will be partial autonomy, which may reduce the fatigue and the cognitive load on the surgeon by performing the monotonous, time-consuming subtasks of the surgical procedure autonomously. Although serious research efforts are paid to this area worldwide, standard evaluation methods, metrics, or benchmarking techniques are still not formed. This article aims to fill the void in the research domain of surgical subtask automation by proposing standard methodologies for performance evaluation. For that purpose, a novel characterization model is presented for surgical automation. The current metrics for performance evaluation and comparison are overviewed and analyzed, and a workflow model is presented that can help researchers to identify and apply their choice of metrics. Existing systems and setups that serve or could serve as benchmarks are also introduced and the need for standard benchmarks in the field is articulated. Finally, the matter of Human–Machine Interface (HMI) quality, robustness, and the related legal and ethical issues are presented.