scholarly journals An Introduction to Robotically Assisted Surgical Systems: Current Developments and Focus Areas of Research

2021 ◽  
Vol 2 (3) ◽  
pp. 321-332
Author(s):  
Julian Klodmann ◽  
Christopher Schlenk ◽  
Anja Hellings-Kuß ◽  
Thomas Bahls ◽  
Roland Unterhinninghofen ◽  
...  

Abstract Purpose of Review Robotic assistance systems for diagnosis and therapy have become technically mature and widely available. Thus, they play an increasingly important role in patient care. This paper provides an overview of the general concepts of robotically assisted surgical systems, briefly revisiting historical and current developments in the surgical robotics market and discussing current focus areas of research. Comprehensiveness cannot be achieved in this format, but besides the general overview, references to further readings and more comprehensive reviews with regard to particular aspects are given. Therefore, the work at hand is considered as an introductory paper into the topic and especially addresses investigators, researchers, medical device manufacturers, and clinicians, who are new to this field. Recent Findings The current research in Robotically Assisted Surgical Systems (RASS) increasingly uses established robotic platforms. To minimize the patient trauma while optimizing the dexterity of the surgeon, miniaturized instruments and semi-autonomous assistance functions are developed. To provide the surgeon with all necessary information in an adequate manner, novel imaging sensors as well as techniques for multimodal sensory feedback and augmented reality are investigated. The Surgical Data Science applies data management and processing approaches including machine learning on medical data to provide optimal, individualized and contextual support to the surgeon. Summary Robotic systems will significantly influence future patient care. Since they must fulfill manifold medical, technical, regulatory and economic requirements, their development calls for a close, active and interdisciplinary cooperation between stakeholders from hospitals, industry and science.

2017 ◽  
Vol 2 (3) ◽  
pp. 145-152 ◽  
Author(s):  
Ralf Stauder ◽  
Daniel Ostler ◽  
Thomas Vogel ◽  
Dirk Wilhelm ◽  
Sebastian Koller ◽  
...  

AbstractDifferent components of the newly defined field of surgical data science have been under research at our groups for more than a decade now. In this paper, we describe our sensor-driven approaches to workflow recognition without the need for explicit models, and our current aim is to apply this knowledge to enable context-aware surgical assistance systems, such as a unified surgical display and robotic assistance systems. The methods we evaluated over time include dynamic time warping, hidden Markov models, random forests, and recently deep neural networks, specifically convolutional neural networks.


2021 ◽  
pp. 102306
Author(s):  
Lena Maier-Hein ◽  
Matthias Eisenmann ◽  
Duygu Sarikaya ◽  
Keno März ◽  
Toby Collins ◽  
...  

2018 ◽  
Vol 65 (11) ◽  
pp. 2649-2659 ◽  
Author(s):  
Sara Moccia ◽  
Sebastian J. Wirkert ◽  
Hannes Kenngott ◽  
Anant S. Vemuri ◽  
Martin Apitz ◽  
...  

2002 ◽  
Vol 8 (2) ◽  
pp. 52-54 ◽  
Author(s):  
Sharon Levy ◽  
David A Bradley ◽  
Moya J Morison ◽  
Michael T Swanston ◽  
Sylvia Harvey
Keyword(s):  

2002 ◽  
Vol 8 (2_suppl) ◽  
pp. 52-54 ◽  
Author(s):  
Sharon Levy ◽  
David A Bradley ◽  
Moya J Morison ◽  
Michael T Swanston ◽  
Sylvia Harvey
Keyword(s):  

OTO Open ◽  
2017 ◽  
Vol 1 (1) ◽  
pp. 2473974X1769277 ◽  
Author(s):  
Daniel B. Spielman ◽  
Wayne D. Hsueh ◽  
Karen Y. Choi ◽  
John P. Bent

Objective Measure the effects of a structured morbidity and mortality conference format on the attitudes of resident and faculty participants. Study Design Prospective cohort study. Setting Otorhinolaryngology–head and neck surgery residency training program. Subjects and Methods Two changes were implemented to the structure of our morbidity and mortality conference: (1) we adopted a recently described presentation framework called situation-background-assessment-recommendation and (2) appointed a faculty moderator to lead the conference. Surveys were distributed to residents and faculty before and after these modifications were implemented to measure changes in attitude of conference attendees. Results After implementing the above changes to the morbidity and mortality conference, participant engagement increased from “moderately engaged” to “extremely engaged” ( P < .01). Among both faculty and residents, the perceived educational value of conference also improved from “moderately educational” to “extremely educational” ( P < .01). Finally in the attending cohort, the impact on future patient care increased from “no change” to “greatly enhanced” ( P < .01). Conclusion By implementing the situation-background-assessment-recommendation framework and appointing a faculty moderator to morbidity and mortality conference, participants reported significantly enhanced engagement during the conference, increased educational value of the session, and a positive impact on future patient care.


2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Lena Maier-Hein ◽  
Martin Wagner ◽  
Tobias Ross ◽  
Annika Reinke ◽  
Sebastian Bodenstedt ◽  
...  

AbstractImage-based tracking of medical instruments is an integral part of surgical data science applications. Previous research has addressed the tasks of detecting, segmenting and tracking medical instruments based on laparoscopic video data. However, the proposed methods still tend to fail when applied to challenging images and do not generalize well to data they have not been trained on. This paper introduces the Heidelberg Colorectal (HeiCo) data set - the first publicly available data set enabling comprehensive benchmarking of medical instrument detection and segmentation algorithms with a specific emphasis on method robustness and generalization capabilities. Our data set comprises 30 laparoscopic videos and corresponding sensor data from medical devices in the operating room for three different types of laparoscopic surgery. Annotations include surgical phase labels for all video frames as well as information on instrument presence and corresponding instance-wise segmentation masks for surgical instruments (if any) in more than 10,000 individual frames. The data has successfully been used to organize international competitions within the Endoscopic Vision Challenges 2017 and 2019.


2017 ◽  
Vol 2 (3) ◽  
pp. 109-121 ◽  
Author(s):  
S. Swaroop Vedula ◽  
Gregory D. Hager

AbstractHealthcare in general, and surgery/interventional care in particular, is evolving through rapid advances in technology and increasing complexity of care, with the goal of maximizing the quality and value of care. Whereas innovations in diagnostic and therapeutic technologies have driven past improvements in the quality of surgical care, future transformation in care will be enabled by data. Conventional methodologies, such as registry studies, are limited in their scope for discovery and research, extent and complexity of data, breadth of analytical techniques, and translation or integration of research findings into patient care. We foresee the emergence of surgical/interventional data science (SDS) as a key element to addressing these limitations and creating a sustainable path toward evidence-based improvement of interventional healthcare pathways. SDS will create tools to measure, model, and quantify the pathways or processes within the context of patient health states or outcomes and use information gained to inform healthcare decisions, guidelines, best practices, policy, and training, thereby improving the safety and quality of healthcare and its value. Data are pervasive throughout the surgical care pathway; thus, SDS can impact various aspects of care, including prevention, diagnosis, intervention, or postoperative recovery. The existing literature already provides preliminary results, suggesting how a data science approach to surgical decision-making could more accurately predict severe complications using complex data from preoperative, intraoperative, and postoperative contexts, how it could support intraoperative decision-making using both existing knowledge and continuous data streams throughout the surgical care pathway, and how it could enable effective collaboration between human care providers and intelligent technologies. In addition, SDS is poised to play a central role in surgical education, for example, through objective assessments, automated virtual coaching, and robot-assisted active learning of surgical skill. However, the potential for transforming surgical care and training through SDS may only be realized through a cultural shift that not only institutionalizes technology to seamlessly capture data but also assimilates individuals with expertise in data science into clinical research teams. Furthermore, collaboration with industry partners from the inception of the discovery process promotes optimal design of data products as well as their efficient translation and commercialization. As surgery continues to evolve through advances in technology that enhance delivery of care, SDS represents a new knowledge domain to engineer surgical care of the future.


2016 ◽  
Vol 14 (8) ◽  
pp. 508-517 ◽  
Author(s):  
Alison C. Tree ◽  
Victoria Harding ◽  
Aneel Bhangu ◽  
Venkatesh Krishnasamy ◽  
Dion Morton ◽  
...  

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