scholarly journals Robotic Applications in Cranial Neurosurgery: Current and Future

2021 ◽  
Author(s):  
Tyler Ball ◽  
Jorge González-Martínez ◽  
Ajmal Zemmar ◽  
Ahmad Sweid ◽  
Sarat Chandra ◽  
...  

Abstract Robotics applied to cranial surgery is a fast-moving and fascinating field, which is transforming the practice of neurosurgery. With exponential increases in computing power, improvements in connectivity, artificial intelligence, and enhanced precision of accessing target structures, robots are likely to be incorporated into more areas of neurosurgery in the future—making procedures safer and more efficient. Overall, improved efficiency can offset upfront costs and potentially prove cost-effective. In this narrative review, we aim to translate a broad clinical experience into practical information for the incorporation of robotics into neurosurgical practice. We begin with procedures where robotics take the role of a stereotactic frame and guide instruments along a linear trajectory. Next, we discuss robotics in endoscopic surgery, where the robot functions similar to a surgical assistant by holding the endoscope and providing retraction, supplemental lighting, and correlation of the surgical field with navigation. Then, we look at early experience with endovascular robots, where robots carry out tasks of the primary surgeon while the surgeon directs these movements remotely. We briefly discuss a novel microsurgical robot that can perform many of the critical operative steps (with potential for fine motor augmentation) remotely. Finally, we highlight 2 innovative technologies that allow instruments to take nonlinear, predetermined paths to an intracranial destination and allow magnetic control of instruments for real-time adjustment of trajectories. We believe that robots will play an increasingly important role in the future of neurosurgery and aim to cover some of the aspects that this field holds for neurosurgical innovation.

2021 ◽  
pp. 155335062110035
Author(s):  
Auriel T. August ◽  
Kunj Sheth ◽  
Arthur Brandt ◽  
Vivian deRuijter ◽  
Janene H. Fuerch ◽  
...  

The combination of computing power, connectivity, and big data has been touted as the future of innovation in many fields, including medicine. There has been a groundswell of companies developing tools for improving patient care utilizing healthcare data, but procedural specialties, like surgery, have lagged behind in benefitting from data-based innovations, given the lack of data that is well structured. While many companies are attempting to innovate in the surgical field, some have encountered difficulties around collecting surgical data, given its complex nature. As there is no standardized way in which to interact with healthcare systems to purchase these data, the authors attempt to characterize the various ways in which surgical data are collected and shared. By surveying and conducting interviews with various surgical technology companies, at least 3 different methods to collect surgical data were identified. From this information, the authors conclude that an attempt to outline best practices should be undertaken that benefits all stakeholders.


2020 ◽  
Vol 3 (9) ◽  
pp. 269-271
Author(s):  
Abdumuminov Axrorjon

There is hardly anyone who has not heard of the concept of innovation, which is becoming an integral part of our lives. This is because the concept has had an impact on all sectors. As a result, the economy of our country is moving towards development. This is because innovation is a cost-effective process that is able to create new demands in the economy, more efficiently than existing ones, and help minimize costs. the role of entrepreneurship and small business, the future innovative plans of our country.


Author(s):  
Alan D. Chockie ◽  
M. Robin Graybeal ◽  
Scott D. Kulat

In the 1970’s and early 80’s there was a reevaluation of the role of inservice inspection programs. Inservice inspection programs as originally developed under the ASME Section XI Code requirements were based on the best information available at the time and helped establish the safety of the subject components. However, it was determined that the examination requirements were not efficient because examinations were being focused on many welds, components, and systems that are not as important as originally thought when the ASME Section III Classes 1, 2, and 3 categories were developed. It was determined that the appropriate locations were not being inspected and that the most effective types of examinations were not being performed. It was felt that a more optimal inspection approach was needed. This eventually led to the development of the risk-informed inservice inspection (RI-ISI) methodology. RI-ISI provides a structured and systematic framework for allocating inspection resources in a cost-effective manner while improving plant safety. It helps focus inspections where failure mechanisms are likely to be and where enhanced inspections are warranted. This paper examines the foundations for the current RI-ISI initiatives and how the RI-ISI methodology may be used in the future for current and next generation plants.


2017 ◽  
Vol 225 (3) ◽  
pp. 189-199 ◽  
Author(s):  
Tina B. Lonsdorf ◽  
Jan Richter

Abstract. As the criticism of the definition of the phenotype (i.e., clinical diagnosis) represents the major focus of the Research Domain Criteria (RDoC) initiative, it is somewhat surprising that discussions have not yet focused more on specific conceptual and procedural considerations of the suggested RDoC constructs, sub-constructs, and associated paradigms. We argue that we need more precise thinking as well as a conceptual and methodological discussion of RDoC domains and constructs, their interrelationships as well as their experimental operationalization and nomenclature. The present work is intended to start such a debate using fear conditioning as an example. Thereby, we aim to provide thought-provoking impulses on the role of fear conditioning in the age of RDoC as well as conceptual and methodological considerations and suggestions to guide RDoC-based fear conditioning research in the future.


2011 ◽  
Author(s):  
Daniel Bartels ◽  
Oleg Urminsky ◽  
Shane Frederick
Keyword(s):  

2013 ◽  
Author(s):  
Mihaly Csikszentmihalyi ◽  
Jeanne Nakamura

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