scholarly journals Automatic Instrument Segmentation in Robot-Assisted Surgery using Deep Learning

2018 ◽  
Author(s):  
Alexey A. Shvets ◽  
Alexander Rakhlin ◽  
Alexandr A. Kalinin ◽  
Vladimir I. Iglovikov
Keyword(s):  
Deep Learning ◽  
Robot Assisted ◽  
Automatic Instrument ◽  
Robot Assisted Surgery

scholarly journals Automatic Instrument Segmentation in Robot-Assisted Surgery Using Deep Learning

2018 ◽  
Author(s):  
Alexey A. Shvets ◽  
Alexander Rakhlin ◽  
Alexandr A. Kalinin ◽  
Vladimir I. Iglovikov
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Robot Assisted ◽  
Automatic Instrument ◽  
Binary Segmentation ◽  
Video Feed ◽  
Class Segmentation ◽  
Segmentation Problem ◽  
Robot Assisted Surgery

AbstractSemantic segmentation of robotic instruments is an important problem for the robot-assisted surgery. One of the main challenges is to correctly detect an instrument’s position for the tracking and pose estimation in the vicinity of surgical scenes. Accurate pixel-wise instrument segmentation is needed to address this challenge. In this paper we describe our deep learning-based approach for robotic instrument segmentation. Our approach demonstrates an improvement over the state-of-the-art results using several novel deep neural network architectures. It addressed the binary segmentation problem, where every pixel in an image is labeled as an instrument or background from the surgery video feed. In addition, we solve a multi-class segmentation problem, in which we distinguish between different instruments or different parts of an instrument from the background. In this setting, our approach outperforms other methods for automatic instrument segmentation thereby providing state-of-the-art results for these problems. The source code for our solution is made publicly available.

scholarly journals Deep learning-based computer vision to recognize and classify suturing gestures in robot-assisted surgery

Surgery ◽  
2020 ◽  
Author(s):  
Francisco Luongo ◽  
Ryan Hakim ◽  
Jessica H. Nguyen ◽  
Animashree Anandkumar ◽  
Andrew J. Hung
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Robot Assisted ◽  
Robot Assisted Surgery

scholarly journals Ensemble deep learning for the prediction of proficiency at a virtual simulator for robot-assisted surgery

2022 ◽  
Author(s):  
Andrea Moglia ◽  
Luca Morelli ◽  
Roberto D’Ischia ◽  
Lorenzo Maria Fatucchi ◽  
Valentina Pucci ◽  
...  
Keyword(s):  
Deep Learning ◽  
Random Forests ◽  
Early Stage ◽  
Boosted Regression Trees ◽  
Robot Assisted ◽  
Surgical Technical ◽  
Enhance Patient Safety ◽  
Gradient Boosted Regression Trees ◽  
Considerable Benefit ◽  
Robot Assisted Surgery

Abstract Background Artificial intelligence (AI) has the potential to enhance patient safety in surgery, and all its aspects, including education and training, will derive considerable benefit from AI. In the present study, deep-learning models were used to predict the rates of proficiency acquisition in robot-assisted surgery (RAS), thereby providing surgical programs directors information on the levels of the innate ability of trainees to facilitate the implementation of flexible personalized training. Methods 176 medical students, without prior experience with surgical simulators, were trained to reach proficiency in five tasks on a virtual simulator for RAS. Ensemble deep neural networks (DNN) models were developed and compared with other ensemble AI algorithms, i.e., random forests and gradient boosted regression trees (GBRT). Results DNN models achieved a higher accuracy than random forests and GBRT in predicting time to proficiency, 0.84 vs. 0.70 and 0.77, respectively (Peg board 2), 0.83 vs. 0.79 and 0.78 (Ring walk 2), 0.81 vs 0.81 and 0.80 (Match board 1), 0.79 vs. 0.75 and 0.71 (Ring and rail 2), and 0.87 vs. 0.86 and 0.84 (Thread the rings 2). Ensemble DNN models outperformed random forests and GBRT in predicting number of attempts to proficiency, with an accuracy of 0.87 vs. 0.86 and 0.83, respectively (Peg board 2), 0.89 vs. 0.88 and 0.89 (Ring walk 2), 0.91 vs. 0.89 and 0.89 (Match board 1), 0.89 vs. 0.87 and 0.83 (Ring and rail 2), and 0.96 vs. 0.94 and 0.94 (Thread the rings 2). Conclusions Ensemble DNN models can identify at an early stage the acquisition rates of surgical technical proficiency of trainees and identify those struggling to reach the required expected proficiency level.

Do People Trust in Robot-Assisted Surgery? Evidence from Europe (Preprint)

2020 ◽  
Author(s):  
Joan Torrent-Sellens ◽  
Ana Jiménez-Zarco ◽  
Francesc Saigí-Rubió
Keyword(s):  
Technology Acceptance ◽  
Surgical Intervention ◽  
Negative Relationship ◽  
Predictor Variable ◽  
Major Change ◽  
Ease Of Use ◽  
The European Union ◽  
Robot Assisted ◽  
Surgical Interventions ◽  
Robot Assisted Surgery

BACKGROUND Increasingly intelligent and autonomous robots are destined to have a huge impact on our society. Their adoption, however, represents a major change to the healthcare sector’s traditional practices, which, in turn, poses certain challenges. To what extent is it possible to foresee a near-future scenario in which minor routine surgery is directed by robots? And what are the patients’ or general public’s perceptions of having surgical procedures performed on them by robots, be it totally or partially? A patient’s trust in robots and AI may facilitate the spread and use of such technologies. OBJECTIVE The goal of our study was to establish the factors that influence how people feel about having a medical operation performed on them by a robot. METHODS We used data from a 2017 Flash Eurobarometer (number 460) of European Commission with 27,901 citizens aged 15 years and over in the 28 countries of the European Union. The research designs and tests a technology acceptance model (TAM). Logistic regression (odds ratios, OR) to model the predictors of trust in robot-assisted surgery was calculated through motivational factors, robots using experience and sociodemographic independent variables. RESULTS The negative relationship between most of the predictors of ease of use, expected benefits and attitude towards robots, and confidence in robot-assisted surgery was contrasted. The only non-sociodemographic predictor variable that has a positive relationship with trust in robots participating in a surgical intervention is previous experience in the use of robots. In this context, we analyze the confidence predictors for three different levels of robot use experience (zero use, average use, and high use). The results obtained indicate that, as the experience of using robots increases, the predictive coefficients related to information, attitude and perception of robots become more negative. Research results also determined that variables of a sociodemographic nature played an important predictive role. It was confirmed that the effect of experience on trust in robots for surgical interventions was greater among men, people between 40 and 54 years old, and those with higher educational levels. CONCLUSIONS Despite the considerable benefits for the patient that the use of robots can bring in a surgical intervention, the results obtained show that trust in robots goes beyond rational decision-making. By contrasting the reasons that generate trust and mistrust in robots, especially by highlighting the experience of use as a key element, the research makes a new contribution to the state of the art and draws practical implications of the use of robots for health policy and practice.

scholarly journals Telemanipulation of an Articulated Robotic Arm using a Commercial Virtual Reality Controller

2020 ◽  
Vol 6 (3) ◽  
pp. 127-130
Author(s):  
Max B. Schäfer ◽  
Kent W. Stewart ◽  
Nico Lösch ◽  
Peter P. Pott
Keyword(s):  
Virtual Reality ◽  
Real Time ◽  
Robotic Arm ◽  
Input Device ◽  
Robot Assisted ◽  
Promising Alternative ◽  
Input Devices ◽  
Current Input ◽  
Current Systems ◽  
Robot Assisted Surgery

AbstractAccess to systems for robot-assisted surgery is limited due to high costs. To enable widespread use, numerous issues have to be addressed to improve and/or simplify their components. Current systems commonly use universal linkage-based input devices, and only a few applicationoriented and specialized designs are used. A versatile virtual reality controller is proposed as an alternative input device for the control of a seven degree of freedom articulated robotic arm. The real-time capabilities of the setup, replicating a system for robot-assisted teleoperated surgery, are investigated to assess suitability. Image-based assessment showed a considerable system latency of 81.7 ± 27.7 ms. However, due to its versatility, the virtual reality controller is a promising alternative to current input devices for research around medical telemanipulation systems.

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