Abstract:Tactile sensing and force reflection have been the subject of considerable research for tumor localization in soft-tissue palpation. The work presented in this paper investigates the relevance of force feedback (presented visually as well as directly) during tactile sensing (presented visually only) for tumor localization using an experimental setup close to one that could be applied for real robotics-assisted minimally invasive surgery. The setup is a teleoperated (master-slave) system facilitated with a stat… Show more
“…Next steps will consist of improving localization accuracy, [25] reducing the size of the WPP -to achieve a better maneuverability -and in demonstrating how the WPP can be used to assist liver resection in a series of in vivo trials. Blinded studies will be performed, where the operator is not aware of the location/number/stiffness of the buried lumps.…”
Section: Discussionmentioning
confidence: 99%
“…Another relevant future step will be the optimization of the user interface. This will include a study on the most effective way to convey the acquired information to the surgeon, along the lines of the results reported in [25].…”
“…Next steps will consist of improving localization accuracy, [25] reducing the size of the WPP -to achieve a better maneuverability -and in demonstrating how the WPP can be used to assist liver resection in a series of in vivo trials. Blinded studies will be performed, where the operator is not aware of the location/number/stiffness of the buried lumps.…”
Section: Discussionmentioning
confidence: 99%
“…Another relevant future step will be the optimization of the user interface. This will include a study on the most effective way to convey the acquired information to the surgeon, along the lines of the results reported in [25].…”
“…Specifically, a number of research studies highlight the importance of reliable tactile feedback during Robot-assisted Minimally Invasive Surgery (RMIS) to improve the clinical outcomes [3].…”
Abstract-Palpation or perception of tactile information from soft tissue organs during minimally invasive surgery is required to improve clinical outcomes. One of the methods of palpation includes examination using the modulation of applied force on the localized area. This paper presents a method of soft tissue autonomous palpation based on the mathematical model obtained from human tactile examination data using modulations of palpation force. Using a second order reactive auto-regressive model of applied force, a robotic probe with spherical indenter was controlled to examine silicone tissue phantoms containing artificial nodules. The results show that the autonomous palpation using the model abstracted from human demonstration can be used not only to detect embedded nodules, but also to enhance the stiffness perception compared to the static indentation of the probe.
“…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. In addition, a training simulator for femoral palpation and needle insertion was developed (Coles et al, 2011).…”
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.…”
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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