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

Pérez-Escamirosa, Fernando; Alarcón-Paredes, Antonio; Alonso-Silverio, Gustavo A.; García, Ignacio Oropesa; Camacho-Nieto, Oscar; Espinoza, Daniel Lorias; Minor-Martínez, Arturo

doi:10.1007/s11548-019-02073-2

Cited by 32 publications

(41 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Besides public datasets, studies reported assessment on private datasets collected using the dVSS, simulators, sensors, and cameras. 54–56,58,60…”

Section: Review Resultsmentioning

confidence: 99%

“…Besides public datasets, studies reported assessment on private datasets collected using the dVSS, simulators, sensors, and cameras. [54][55][56]58,60 Following our analysis, we found 5 studies out of 25 that acquired data virtually using simulators. In addition, 15 papers, including the JIGSAWS studies, were robotic, and 4 were of laparoscopic surgery.…”

Section: Datasetsmentioning

confidence: 99%

“…The kinematic data can also be collected virtually using software such as EndoVis for virtual training applications, which does not suffer from the limitations of physical sensors. 16 Unlike kinematic data, video-based data are gathered non-invasively and are easier to work with. However, video-based data require extensive post-processing to extract meaningful information.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Deep neural networks for the assessment of surgical skills: A systematic review

Yanik

Intes

Krüger

et al. 2021

Journal of Defense Modeling & Simulation

View full text Add to dashboard Cite

Surgical training in medical school residency programs has followed the apprenticeship model. The learning and assessment process is inherently subjective and time-consuming. Thus, there is a need for objective methods to assess surgical skills. Here, we use the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to systematically survey the literature on the use of Deep Neural Networks for automated and objective surgical skill assessment, with a focus on kinematic data as putative markers of surgical competency. There is considerable recent interest in deep neural networks (DNNs) due to the availability of powerful algorithms, multiple datasets, some of which are publicly available, as well as efficient computational hardware to train and host them. We have reviewed 530 papers, of which we selected 25 for this systematic review. Based on this review, we concluded that DNNs are potent tools for automated, objective surgical skill assessment using both kinematic and video data. The field would benefit from large, publicly available, annotated datasets representing the surgical trainee and expert demographics and multimodal data beyond kinematics and videos.

show abstract

“…Besides public datasets, studies reported assessment on private datasets collected using the dVSS, simulators, sensors, and cameras. 54–56,58,60…”

Section: Review Resultsmentioning

confidence: 99%

Section: Datasetsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Deep neural networks for the assessment of surgical skills: A systematic review

Yanik

Intes

Krüger

et al. 2021

Journal of Defense Modeling & Simulation

View full text Add to dashboard Cite

show abstract

“…The first category is tool-related and makes up the majority of the literature. Methods in this category rely on tool motion data from various sources, including video object tracking or detection [24,5,44], video spatiotemporal descriptors [65,64,62,7], robotic kinematics [63,55,15,9], external sensors [13,4,23], and virtual reality interfaces [25]. The second category is proxy-related.…”

Section: Automatic Surgical Skill Assessmentmentioning

confidence: 99%

Towards Unified Surgical Skill Assessment

Liu¹,

Li²,

Jiang³

et al. 2021

Preprint

View full text Add to dashboard Cite

Surgical skills have a great influence on surgical safety and patients' well-being. Traditional assessment of surgical skills involves strenuous manual efforts, which lacks efficiency and repeatability. Therefore, we attempt to automatically predict how well the surgery is performed using the surgical video. In this paper, a unified multi-path framework for automatic surgical skill assessment is proposed, which takes care of multiple composing aspects of surgical skills, including surgical tool usage, intraoperative event pattern, and other skill proxies. The dependency relationships among these different aspects are specially modeled by a path dependency module in the framework. We conduct extensive experiments on the JIGSAWS dataset of simulated surgical tasks, and a new clinical dataset of real laparoscopic surgeries. The proposed framework achieves promising results on both datasets, with the state-of-the-art on the simulated dataset advanced from 0.71 Spearman's correlation to 0.80. It is also shown that combining multiple skill aspects yields better performance than relying on a single aspect. Codes at https://git.io/JG2OA.

show abstract

“…The Simball Box or research and development results like TrEndo provide skill assessment by instrument tracking, continuous attachment of instruments restrict alterations of the training setup and can interfere training through altered instrument handling [7][8][9]. Analyzing multiple motion analysis parameters (MAP) through instrument tracking with additional sensors or colored markers and image analysis pose smaller influences on the tools' characteristic behavior, yet, are inefficient due to instrument modification and simulator-dependent software adjustments [10][11][12][13][14]. Determining instrument positions and angles by edge detection alone forgoes the problem entirely, the necessary image processing increases the complexity of the system, decreases reliability in altered circumstances, and decreases portability to different phantom trainers [15][16][17][18][19].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

View full text Add to dashboard Cite

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.

show abstract

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

Cited by 32 publications

References 39 publications

Deep neural networks for the assessment of surgical skills: A systematic review

Deep neural networks for the assessment of surgical skills: A systematic review

Towards Unified Surgical Skill Assessment

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

Contact Info

Product

Resources

About