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Use of stochastic resonance methods for improving laparoscopic surgery performance

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Abstract

Background

Vibrotactile feedback (VIB) has been utilized in previous research as sensory augmentation to improve performance during minimally invasive surgical tasks. Stochastic resonance (SR), introduced into the human control system as white noise at a subthreshold level, has shown promise to improve the sensitivity of tactile receptors resulting in performance enhancement for sensorimotor tasks. The purpose of this study was to determine whether SR could improve performance (accuracy, speed) in a simulated laparoscopic palpation task.

Methods

Sixteen subjects performed a palpation task using a laparoscopic tool to detect the presence of tumors (compacted felt) embedded in simulated tissue samples (silicone gel) inside a laparoscopic trainer box. Subjects were randomly assigned to one of the four different conditions: (1) SR, (2) VIB, (3) VIB + SR, and (4) Control. The VIB and SR signals were administered via two separate haptic actuators attached to the subjects’ dominant upper arms and forearms, respectively. All subjects were presented with 36 tissue samples with no sensory augmentation (Control) to establish baseline, followed by another 36 samples under one of the randomly assigned vibration conditions (SR, VIB, VIB + SR, or Control).

Results

Results show a significantly larger improvement in tumor detection accuracy in the SR group compared to the VIB and Control groups. There was no difference in the time to task completion, indicating that there was no speed–accuracy trade-off.

Conclusions

The results have implications for the design of instruments and methods for increasing detection accuracy such as in palpation tasks. This technology could help surgeons better identify tumors located in healthy surrounding tissue.

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Acknowledgments

This work was supported by a grant from the Ohio Third Frontier to the Ohio Imaging Research and Innovation Network (OIRAIN). The authors acknowledge the assistance of Emily Diller and Katherine Babbitt.

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Authors and Affiliations

  1. Department of Biomedical, Industrial and Human Factors Engineering, Wright State University, 207 Russ Engineering Center, 3640 Colonel Glenn Hwy, Dayton, OH, 45435, USA

    Robert Hoskins, Jinling Wang & Caroline G. L. Cao

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  1. Robert Hoskins
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  2. Jinling Wang
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  3. Caroline G. L. Cao
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Correspondence to Caroline G. L. Cao.

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Robert D. Hoskins, Jinling Wang, and Caroline G.L. Cao have no conflicts of interest or financial ties to disclose.

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Hoskins, R., Wang, J. & Cao, C.G.L. Use of stochastic resonance methods for improving laparoscopic surgery performance. Surg Endosc 30, 4214–4219 (2016). https://doi.org/10.1007/s00464-015-4730-8

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  • DOI: https://doi.org/10.1007/s00464-015-4730-8

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