Clinical and Regulatory Challenges for Medical Devices

Robotic-assisted minimally invasive surgical systems suffer from one major limitation which is the lack of interaction forces feedback. The restricted sense of touch hinders the surgeons' performance and reduces their dexterity and precision during a procedure. In this work, we present a sensory substitution approach that relies on visual stimuli to transmit the tool-tissue interaction forces to the operating surgeon. Our approach combines a 3D diffeomorphic deformation mapping with a generative model to precisely label the force level. The main highlights of our approach are that the use of diffeomorphic transformation ensures anatomical structure preservation and the label assignment is based on a parametric form of several mixture elements. We performed experimentations on both ex-vivo and in-vivo datasets and offer careful numerical results evaluating our approach. The results show that our solution has an error measure less than 1mm in all directions and an average labeling error of 2.05%. It can also be applicable to other scenarios that require force feedback such as microsurgery, knot tying or needle-based procedures.

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Sensorless force estimation using a neuro-vision-based approach for robotic-assisted surgery

Avilés

Alsaleh

Sobrevilla

et al. 2015

2015 7th International IEEE/EMBS Conference on Neural Engineering (NER)

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Abstract-This paper addresses the issue of lack of force feedback in robotic-assisted minimally invasive surgeries. Force is an important measure for surgeons in order to prevent intra-operative complications and tissue damage. Thus, an innovative neuro-vision based force estimation approach is proposed. Tissue surface displacement is first measured via minimization of an energy functional. A neuro approach is then used to establish a geometric-visual relation and estimate the applied force. The proposed approach eliminates the need of add-on sensors, carrying out biocompatibility studies and is applicable to tissues of any shape. Moreover, we provided an improvement from 15.14% to 56.16% over other approaches which demonstrate the potential of our proposal.

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Clinical and Regulatory Challenges for Medical Devices

Cited by 4 publications

References 2 publications

Deep learning with 4D spatio-temporal data representations for OCT-based force estimation

Deep learning with 4D spatio-temporal data representations for OCT-based force estimation

Sight to touch: 3D diffeomorphic deformation recovery with mixture components for perceiving forces in robotic-assisted surgery

Sensorless force estimation using a neuro-vision-based approach for robotic-assisted surgery

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