Wireless Tissue Palpation for Intraoperative Detection of Lumps in the Soft Tissue

Beccani, Marco; Natali, Christian Di; Sliker, Levin J.; Schoen, Jonathan A.; Rentschler, Mark E.; Valdastri, Pietro

doi:10.1109/tbme.2013.2279337

Cited by 66 publications

(38 citation statements)

References 32 publications

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“…A different approach to WTP was presented in [1]. In this case, the MCR was designed to be grasped by a laparoscopic instrument and directly operated by the surgeon.…”

Section: Surgeon-operated Wireless Tissue Palpationmentioning

confidence: 99%

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SMAC — A Modular Open Source Architecture for Medical Capsule Robots

Beccani

Susilo

et al. 2014

International Journal of Advanced Robotic Systems

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The field of Medical Capsule Robots (MCRs) is gaining momentum in the robotics community, with applications spanning from abdominal surgery to gastrointestinal (GI) endoscopy. MCRs are miniature multifunctional devices usually constrained in both size and on-board power supply. The design process for MCRs is time consuming and resource intensive, as it involves the development of custom hardware and software components. In this work, we present the STORM Lab Modular Architecture for Capsules (SMAC), a modular open source architecture for MCRs aiming to provide the MCRs research community with a tool for shortening the design and development time for capsule robots. The SMAC platform consists of both hardware modules and firmware libraries that can be used for developing MCRs. In particular, the SMAC modules are miniature boards of uniform diameter (i.e., 9.8 mm) that are able to fulfill five different functions: signal coordination combined with wireless data transmission, sensing, actuation, powering and vision/illumination. They are small in size, low power, and have reconfigurable software libraries for the Hardware Abstraction Layer (HAL), which has been proven to work reliably for different types of MCRs. A design template for a generic SMAC application implementing a robust communication protocol is presented in this work, together with its finite state machine abstraction, capturing all the architectural components involved. The reliability of the wireless link is assessed for different levels of data transmission power and separation distances. The current consumption for each SMAC module is quantified and the timing of a SMAC radio message transmission is characterized. Finally, the applicability of SMAC in the field of MCRs is discussed by analysing examples from the literature.

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“…A different approach to WTP was presented in [1]. In this case, the MCR was designed to be grasped by a laparoscopic instrument and directly operated by the surgeon.…”

Section: Surgeon-operated Wireless Tissue Palpationmentioning

confidence: 99%

“…Master 3DA 3DG 3DM 3DAG 3DAM 3D Beccani et al, [46]  X X X X X Beccani et al, [1]   X X X X Di Natali et al, [47]   X X X X Carta et al, [49]  X X X X X Tortora et al, [15]  X X X X X…”

Section: Smac Module Mcrmentioning

confidence: 99%

SMAC — A Modular Open Source Architecture for Medical Capsule Robots

Beccani

Susilo

et al. 2014

International Journal of Advanced Robotic Systems

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“…They are designed to be inserted into the human body through natural orifices (e.g., mouth) to operate in the Gastrointestinal (GI) tract. Other possible areas of application for MCRs are Minimally Invasive Surgery (MIS) [3]- [6] and Natural Orifice Transluminal Endoscopic Surgery (NOTES) [7].…”

Section: Introductionmentioning

confidence: 99%

Toward rapid prototyping of miniature Capsule Robots

Taddese

Beccani

Susilo

et al. 2015

2015 IEEE International Conference on Robotics and Automation (ICRA)

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Abstract-Minimally invasive robotic surgery techniques are becoming popular thanks to their enhanced patient benefits, including shorter recovery time, better cosmetic results and reduced discomforts. Less invasive procedures would be achieved with the use of Medical Capsule Robots (MCRs). These devices are characterized by low power requirements and small dimensions as well as uncompromising safety. MCRs operate wirelessly in abdominal Minimally Invasive Surgery (MIS) and Natural Orifice Transluminal Endoscopic Surgery (NOTES) or in the Gastrointestinal (GI) tract. The design process of MCRs, however, is expensive and time consuming. A platform for rapid prototyping MCRs is needed so that MCR researchers can reduce development costs and spend more time in studying innovative MCR applications. In this work, we introduce an open source modular platform geared toward rapid prototyping MCRs. To speed up the prototyping process, the MCR is programmed using TinyOS instead of bare-bone C. We present the hardware architecture of the platform, and the motivation for using TinyOS. To show the viability of TinyOS, we present results from an experiment involving sensing, actuation and wireless communication. This work lays the foundation for our future goal of building an integrated design environment for the design, analysis and simulation of MCRs.

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“…For instance, there are hand-held devices that are directly manipulated by a surgeon to acquire information about the target tissue (Ottermo et al, 2004;Schostek et al, 2006;Beccani et al, 2014;Escoto et al, 2015;Solodova et al, 2016). Moreover, master-slave surgical systems with a force/tactile sensor (Tavakoli et al, 2006;Talasaz and Patel, 2013;Meli et al, 2016;Pacchierotti et al, 2016;Li et al, 2017) or force estimation by a state observer (Gwilliam et al, 2009;Yamamoto et al, 2012;Schorr et al, 2015) have been developed.…”

mentioning

confidence: 99%

“…In addition, a training simulator for femoral palpation and needle insertion was developed (Coles et al, 2011). Among the above-mentioned systems it is common to provide visual feedback, such as by displaying a color map (Schostek et al, 2006;Talasaz and Patel, 2013;Beccani et al, 2014;Escoto et al, 2015;Solodova et al, 2016;Li et al, 2017), a graphical bar (Gwilliam et al, 2009;Schorr et al, 2015), a sequential lamp (Tavakoli et al, 2006), or a color map overlaid on an endoscopic image (Yamamoto et al, 2012). As an additional approach for sensory feedback, a tactile display (Ottermo et al, 2004;Coles et al, 2011;Schorr et al, 2015;Pacchierotti et al, 2016), or force feedback through a master console (Tavakoli et al, 2006;Gwilliam et al, 2009;Schorr et al, 2015;Meli et al, 2016) have either been developed or implemented.…”

mentioning

confidence: 99%

DNN-Based Assistant in Laparoscopic Computer-Aided Palpation

et al. 2018

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Tactile sensory input of surgeons is severely limited in minimally invasive surgery, therefore manual palpation cannot be performed for intraoperative tumor detection. Computer-aided palpation, in which tactile information is acquired by devices and relayed to the surgeon, is one solution for overcoming this limitation. An important design factor is the method by which the acquired information is fed back to the surgeon. However, currently there is no systematic method for achieving this aim, and it is possible that a badly implemented feedback mechanism could adversely affect the performance of the surgeon. In this study, we propose an assistance algorithm for intraoperative tumor detection in laparoscopic surgery. Our scenario is that the surgeon manipulates a sensor probe, makes a decision based on the feedback provided from the sensor, while simultaneously, the algorithm analyzes the time series of the sensor output. Thus, the algorithm assists the surgeon in making decisions by providing independent detection results. A deep neural network model with three hidden layers was used to analyze the sensor output. We propose methods to input the time series of the sensor output to the model for real-time analysis, and to determine the criterion for detection by the model. This study aims to validate the feasibility of the algorithm by using data acquired in our previous psychophysical experiment. There, novice participants were asked to detect a phantom of an early-stage gastric tumor through visual feedback from the tactile sensor. In addition to the analysis of the accuracy, signal detection theory was employed to assess the potential detection performance of the model. The detection performance was compared with that of human participants. We conducted two types of validation, and found that the detection performance of the model was not significantly different from that of the human participants if the data from a known user was included in the model construction. The result supports the feasibility of the proposed algorithm for detection assistance in computer-aided palpation.

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Wireless Tissue Palpation for Intraoperative Detection of Lumps in the Soft Tissue

Cited by 66 publications

References 32 publications

SMAC — A Modular Open Source Architecture for Medical Capsule Robots

SMAC — A Modular Open Source Architecture for Medical Capsule Robots

Toward rapid prototyping of miniature Capsule Robots

DNN-Based Assistant in Laparoscopic Computer-Aided Palpation

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