Li, M;
Konstantinova, J;
Xu, G;
He, B;
Aminzadeh, V;
Xie, J;
Wurdemann, H;
(2017)
Evaluation of stiffness feedback for hard nodule identification on a phantom silicone model.
PLOS ONE
, 12
(3)
, Article e0172703. 10.1371/journal.pone.0172703.
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Abstract
Haptic information in robotic surgery can significantly improve clinical outcomes and help detect hard soft-tissue inclusions that indicate potential abnormalities. Visual representation of tissue stiffness information is a cost-effective technique. Meanwhile, direct force feedback, although considerably more expensive than visual representation, is an intuitive method of conveying information regarding tissue stiffness to surgeons. In this study, real-time visual stiffness feedback by sliding indentation palpation is proposed, validated, and compared with force feedback involving human subjects. In an experimental tele-manipulation environment, a dynamically updated color map depicting the stiffness of probed soft tissue is presented via a graphical interface. The force feedback is provided, aided by a master haptic device. The haptic device uses data acquired from an F/T sensor attached to the end-effector of a tele-manipulated robot. Hard nodule detection performance is evaluated for 2 modes (force feedback and visual stiffness feedback) of stiffness feedback on an artificial organ containing buried stiff nodules. From this artificial organ, a virtual-environment tissue model is generated based on sliding indentation measurements. Employing this virtual-environment tissue model, we compare the performance of human participants in distinguishing differently sized hard nodules by force feedback and visual stiffness feedback. Results indicate that the proposed distributed visual representation of tissue stiffness can be used effectively for hard nodule identification. The representation can also be used as a sufficient substitute for force feedback in tissue palpation.
| Type: | Article |
|---|---|
| Title: | Evaluation of stiffness feedback for hard nodule identification on a phantom silicone model |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1371/journal.pone.0172703 |
| Publisher version: | http://dx.doi.org/10.1371/journal.pone.0172703 |
| Language: | English |
| Additional information: | © 2017 Li et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
| Keywords: | Science & Technology, Multidisciplinary Sciences, Science & Technology - Other Topics, MINIMALLY INVASIVE SURGERY, TISSUE ABNORMALITY IDENTIFICATION, ROBOT-ASSISTED SURGERY, SENSORY SUBTRACTION, SYSTEM, REALITY, CANCER, PROBE |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Mechanical Engineering |
| URI: | https://discovery.ucl.ac.uk/id/eprint/1546328 |
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