Supervised classification of psychomotor competence in minimally invasive surgery based on instruments motion analysis

García, Ignacio Oropesa; Sánchez-González, Patricia; Chmarra, Magdalena K.; Lamata, Pablo; Pérez‐Rodríguez, Rodrigo; Jansen, Frank Willem; Dankelman, Jenny; Gómez, Enrique J.

doi:10.1007/s00464-013-3226-7

Cited by 32 publications

(21 citation statements)

References 29 publications

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“…We aimed to establish a dynamic process that was tailored to individual needs and was continuously optimized based on accumulated evidence and experience [17]. By combining measurements of force and motion, fundamental laparoscopic skills in a box trainer model can be evaluated objectively and levels of competence can be determined more accurately [19–22]. It provides resident’s supervisor an objective tool to assess whether the trainee has reached the minimum required level of skill to actually start performing MIS in the OR [23].…”

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confidence: 99%

Force-based learning curve tracking in fundamental laparoscopic skills training

et al. 2018

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BackgroundWithin minimally invasive surgery (MIS), structural implementation of courses and structured assessment of skills are challenged by availability of trainers, time, and money. We aimed to establish and validate an objective measurement tool for preclinical skills acquisition in a basic laparoscopic at-home training program.MethodsA mobile laparoscopic simulator was equipped with a state-of-the-art force, motion, and time tracking system (ForceSense, MediShield B.V., Delft, the Netherlands). These performance parameters respectively representing tissue manipulation and instrument handling were continuously tracked during every trial. Proficiency levels were set by clinical experts for six different training tasks. Resident’s acquisition and development of fundamental skills were evaluated by comparing pre- and post-course assessment measurements and OSATS forms. A questionnaire was distributed to determine face and content validity.ResultsOut of 1842 captured attempts by novices, 1594 successful trials were evaluated. A decrease in maximum exerted absolute force was shown in comparison of four training tasks (p ≤ 0.023). Three of the six comparisons also showed lower mean forces during tissue manipulation (p ≤ 0.024). Lower instrument handling outcomes (i.e., time and motion parameters) were observed in five tasks (resp. (p ≤ 0.019) and (p ≤ 0.025)). Simultaneously, all OSATS scores increased (p ≤ 0.028). Proficiency levels for all tasks can be reached in 2 weeks of at home training.ConclusionsMonitoring force, motion, and time parameters during training showed to be effective in determining acquisition and development of basic laparoscopic tissue manipulation and instrument handling skills. Therefore, we were able to gain insight into the amount of training needed to reach certain levels of competence. Skills improved after sufficient amount of training at home. Questionnaire outcomes indicated that skills and self-confidence improved and that this training should therefore be part of the regular residency training program.

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confidence: 99%

Force-based learning curve tracking in fundamental laparoscopic skills training

et al. 2018

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“…Movement of economy (path length) and completion time are widely used as common metrics for laparoscopic surgery skills evaluation. Additional considerations are peak speed, average speed, and idle time (Riojas et al 2011;Oropesa et al 2014). We select several metrics from those measures and introduce a feature from video games to design the hierarchical fuzzy assessment system for the CAST system.…”

Section: Objective Assessment Metricsmentioning

confidence: 99%

“…Objective assessment metrics for laparoscopic surgery skills training have been proposed by several researchers (Chang et al 2016;Cotin et al 2002;Kowalewski et al 2014;Maithel et al 2006;Retrosi et al 2015;Reiley et al 2011;Oropesa et al 2014;Ritter and Scott 2007). Some proposed to design a scoring system to classify trainees' proficiency of surgical movements (Reiley et al 2011;Oropesa et al 2014). Others proposed assessment metrics to design a better training curriculum and a training system for medical students (Cotin et al 2002;Ritter and Scott 2007).…”

Section: Introductionmentioning

confidence: 99%

“…In (Oropesa et al 2014), three classifiers (linear discriminant analysis, support vector machine, and adaptive neuro-fuzzy inference systems) were developed to investigate the performance of the proposed classifiers that categorize participating groups. Several participants (i.e., 42 people divided into an expert group and a novice/intermediate group) were enrolled to verify the proposed classifiers.…”

Section: Introductionmentioning

confidence: 99%

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A Simulation-Based Assessment System for Computer Assisted Surgical Trainer

2017

Modeling and Simulation in Medicine Symposium (MSM 2017)

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An assessment system is presented to provide objective assessment results for laparoscopic surgery skills training. Six innovative evaluation metrics are introduced in the design of the proposed system. Like a video game, the system suggests achievable goals that are used to define evaluation metrics for a trainee who is performing a particular training task. To implement the proposed system, a design method of the hierarchical fuzzy system is used. The simulation results and the prototype illustrate the feasibility of the proposed evaluation approach.

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“…Classifiers based on the Markov model and the hidden Markov model have been successfully used in the literature; however, they present a high computational complexity [3], [12], [14]. Alternative less demanding approaches based on linear discriminant analysis (LDA) [13], [15], support vector machines (SVMs) [16], artificial neural networks (ANNs), or artificial neuro-fuzzy inference systems [17] have been explored. In this study, we compared the performance of three of these classifiers, LDA, SVM, and ANN, being the most suitable for future real-time implementations on sensory gloves.…”

Section: Introductionmentioning

confidence: 99%

Sensory-Glove-Based Open Surgery Skill Evaluation

Sbernini

Quitadamo

Riillo

et al. 2018

IEEE Trans. Human-Mach. Syst.

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Manual dexterity is one of the most important surgical skills, and yet there are limited instruments to evaluate this ability objectively. In this paper, we propose a system designed to track surgeons' hand movements during simulated open surgery tasks and to evaluate their manual expertise. Eighteen participants, grouped according to their surgical experience, performed repetitions of two basic surgical tasks, namely single interrupted suture and simple running suture. Subjects' hand movements were measured with a sensory glove equipped with flex and inertial sensors, tracking flexion/extension of hand joints, and wrist movement. The participants' level of experience was evaluated discriminating manual performances using linear discriminant analysis, support vector machines, and artificial neural network classifiers. Artificial neural networks showed the best performance, with a median error rate of 0.61% on the classification of single interrupted sutures and of 0.57% on simple running sutures. Strategies to reduce sensory glove complexity and increase its comfort did not affect system performances substantially.

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Supervised classification of psychomotor competence in minimally invasive surgery based on instruments motion analysis

Cited by 32 publications

References 29 publications

Force-based learning curve tracking in fundamental laparoscopic skills training

Force-based learning curve tracking in fundamental laparoscopic skills training

A Simulation-Based Assessment System for Computer Assisted Surgical Trainer

Sensory-Glove-Based Open Surgery Skill Evaluation

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