Utilization of Flexible-Wearable Sensors to Describe the Kinematics of Surgical Proficiency

Zulbaran-Rojas, Alejandro; Najafi, Bijan; Arita, Nestor A.; Rahemi, Hadi; Razjouyan, Javad; Gilani, Ramyar

doi:10.1016/j.jss.2021.01.006

Cited by 5 publications

(2 citation statements)

References 59 publications

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“…Other studies have also reported that novices tend to neglect their non-dominant hand in laparoscopic tasks [ 14 , 15 , 18 ]. Zulbaran-Rojas et al reported that the novices’ non-dominant hand had not only less activity than the dominant hand (measured by velocity and traveled distance), but also more wasted movements, and suggested that the ability to use both hands equally is a sign of expertise [ 30 ]. Similar findings have been reported by Uemura et al, who defined a novel surgical skill score metric based partially on bimanual movements and found that experts were better at coordinating their movements bimanually [ 27 ].…”

Section: Discussionmentioning

confidence: 99%

Movement-level process modeling of microsurgical bimanual and unimanual tasks

et al. 2021

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Purpose Microsurgical techniques require highly skilled manual handling of specialized surgical instruments. Surgical process models are central for objective evaluation of these skills, enabling data-driven solutions that can improve intraoperative efficiency. Method We built a surgical process model, defined at movement level in terms of elementary surgical actions ($$n=4$$ n = 4 ) and targets ($$n=4$$ n = 4 ). The model also included nonproductive movements, which enabled us to evaluate suturing efficiency and bi-manual dexterity. The elementary activities were used to investigate differences between novice ($$n=5$$ n = 5 ) and expert surgeons ($$n=5$$ n = 5 ) by comparing the cosine similarity of vector representations of a microsurgical suturing training task and its different segments. Results Based on our model, the experts were significantly more efficient than the novices at using their tools individually and simultaneously. At suture level, the experts were significantly more efficient at using their left hand tool, but the differences were not significant for the right hand tool. At the level of individual suture segments, the experts had on average 21.0 % higher suturing efficiency and 48.2 % higher bi-manual efficiency, and the results varied between segments. Similarity of the manual actions showed that expert and novice surgeons could be distinguished by their movement patterns. Conclusions The surgical process model allowed us to identify differences between novices’ and experts’ movements and to evaluate their uni- and bi-manual tool use efficiency. Analyzing surgical tasks in this manner could be used to evaluate surgical skill and help surgical trainees detect problems in their performance computationally.

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Section: Discussionmentioning

confidence: 99%

Movement-level process modeling of microsurgical bimanual and unimanual tasks

et al. 2021

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“…However, video cannot capture the data related to tool–tissue interaction, such as applied force, muscular workload, cognitive workload, and gaze pattern. Therefore, various sensors, including electroencephalography (EEG) [ 21 ], electromyography (EMG) [ 22 ], inertial measurement units (IMU) [ 23 ], force sensors [ 24 ], and eye-tracking [ 25 ], have been employed in previous studies to evaluate surgical skills. However, the sensor-based data collection system must be minimal and should not impede the natural movements of the surgeons [ 26 ].…”

Section: Introductionmentioning

confidence: 99%

Craniotomy Simulator with Force Myography and Machine Learning-Based Skills Assessment

et al. 2023

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Craniotomy is a fundamental component of neurosurgery that involves the removal of the skull bone flap. Simulation-based training of craniotomy is an efficient method to develop competent skills outside the operating room. Traditionally, an expert surgeon evaluates the surgical skills using rating scales, but this method is subjective, time-consuming, and tedious. Accordingly, the objective of the present study was to develop an anatomically accurate craniotomy simulator with realistic haptic feedback and objective evaluation of surgical skills. A CT scan segmentation-based craniotomy simulator with two bone flaps for drilling task was developed using 3D printed bone matrix material. Force myography (FMG) and machine learning were used to automatically evaluate the surgical skills. Twenty-two neurosurgeons participated in this study, including novices (n = 8), intermediates (n = 8), and experts (n = 6), and they performed the defined drilling experiments. They provided feedback on the effectiveness of the simulator using a Likert scale questionnaire on a scale ranging from 1 to 10. The data acquired from the FMG band was used to classify the surgical expertise into novice, intermediate and expert categories. The study employed naïve Bayes, linear discriminant (LDA), support vector machine (SVM), and decision tree (DT) classifiers with leave one out cross-validation. The neurosurgeons’ feedback indicates that the developed simulator was found to be an effective tool to hone drilling skills. In addition, the bone matrix material provided good value in terms of haptic feedback (average score 7.1). For FMG-data-based skills evaluation, we achieved maximum accuracy using the naïve Bayes classifier (90.0 ± 14.8%). DT had a classification accuracy of 86.22 ± 20.8%, LDA had an accuracy of 81.9 ± 23.6%, and SVM had an accuracy of 76.7 ± 32.9%. The findings of this study indicate that materials with comparable biomechanical properties to those of real tissues are more effective for surgical simulation. In addition, force myography and machine learning provide objective and automated assessment of surgical drilling skills.

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