scholarly journals Initial application of deep learning to borescope detection of endoscope working channel damage and residue

2022 ◽  
Vol 10 (01) ◽  
pp. E112-E118
Author(s):  
Monique T. Barakat ◽  
Mohit Girotra ◽  
Subhas Banerjee
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Gold Standard ◽  
Academic Institution ◽  
Contributing Factors ◽  
Time Investment ◽  
Initial Application ◽  
System P ◽  
Sterile Processing ◽  
And Training

Abstract Background and study aims Outbreaks of endoscopy-related infections have prompted evaluation for potential contributing factors. We and others have demonstrated the utility of borescope inspection of endoscope working channels to identify occult damage that may impact the adequacy of endoscope reprocessing. The time investment and training necessary for borescope inspection have been cited as barriers preventing implementation. We investigated the utility of artificial intelligence (AI) for streamlining and enhancing the value of borescope inspection of endoscope working channels. Methods We applied a deep learning AI approach to borescope inspection videos of the working channels of 20 endoscopes in use at our academic institution. We evaluated the sensitivity, accuracy, and reliability of this software for detection of endoscope working channel findings. Results Overall sensitivity for AI-based detection of borescope inspection findings identified by gold standard endoscopist inspection was 91.4 %. Labels were accurate for 67 % of these working channel findings and accuracy varied by endoscope segment. Read-to-read variability was noted to be minimal, with test-retest correlation value of 0.986. Endoscope type did not predict accuracy of the AI system (P = 0.26). Conclusions Harnessing the power of AI for detection of endoscope working channel damage and residue could enable sterile processing department technicians to feasibly assess endoscopes for working channel damage and perform endoscope reprocessing surveillance. Endoscopes that accumulate an unacceptable level of damage may be flagged for further manual evaluation and consideration for manufacturer evaluation/repair.

scholarly journals Using CycleGAN to Achieve the Sketch Recognition Process of Sketch-Based Modeling

2021 ◽  
pp. 26-34
Author(s):  
Yuqian Li ◽  
Weiguo Xu
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Deep Learning ◽  
Learning Technology ◽  
Modeling Technology ◽  
Image Translation ◽  
Mapping Process ◽  
And Training ◽  
Difficult Part ◽  
2D Sketches

AbstractArchitects usually design ideation and conception by hand-sketching. Sketching is a direct expression of the architect’s creativity. But 2D sketches are often vague, intentional and even ambiguous. In the research of sketch-based modeling, it is the most difficult part to make the computer to recognize the sketches. Because of the development of artificial intelligence, especially deep learning technology, Convolutional Neural Networks (CNNs) have shown obvious advantages in the field of extracting features and matching, and Generative Adversarial Neural Networks (GANs) have made great breakthroughs in the field of architectural generation which make the image-to-image translation become more and more popular. As the building images are gradually developed from the original sketches, in this research, we try to develop a system from the sketches to the images of buildings using CycleGAN algorithm. The experiment demonstrates that this method could achieve the mapping process from the sketches to images, and the results show that the sketches’ features could be recognised in the process. By the learning and training process of the sketches’ reconstruction, the features of the images are also mapped to the sketches, which strengthen the architectural relationship in the sketch, so that the original sketch can gradually approach the building images, and then it is possible to achieve the sketch-based modeling technology.

scholarly journals Understanding Human Impressions of Artificial Intelligence

2021 ◽  
Author(s):  
Kevin Robert McKee ◽  
Xuechunzi Bai ◽  
Susan Fiske
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Cognitive Processes ◽  
Intelligent Systems ◽  
Social Cognitive ◽  
Learning System ◽  
Human Interactions ◽  
Behavioral Studies ◽  
And Training ◽  
Willingness To Cooperate

Artificial intelligence increasingly suffuses everyday life. However, people are frequently reluctant to interact with A.I. systems. This challenges both the deployment of beneficial A.I. technology and the development of deep learning systems that depend on humans for oversight, direction, and training. Previously neglected but fundamental, social-cognitive processes guide human interactions with A.I. systems. In five behavioral studies (N = 3,099), warmth and competence feature prominently in participants’ impressions of artificially intelligent systems. Judgments of warmth and competence systematically depend on human-A.I. interdependence. In particular, participants perceive systems that optimize interests aligned with human interests as warmer and systems that operate independently from human direction as more competent. Finally, a prisoner’s dilemma game shows that warmth and competence judgments predict participants’ willingness to cooperate with a deep learning system. These results demonstrate the generality of intent detection to interactions with technological actors. Researchers and developers should carefully consider the degree and alignment of interdependence between humans and new artificial intelligence systems.

scholarly journals The Impact of Artificial Intelligence and Deep Learning in Eye Diseases: A Review

2021 ◽  
Vol 8 ◽  
Author(s):  
Raffaele Nuzzi ◽  
Giacomo Boscia ◽  
Paola Marolo ◽  
Federico Ricardi
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Computer Science ◽  
New Technology ◽  
Eye Diseases ◽  
Human Intelligence ◽  
Basic Principles ◽  
The Impact ◽  
And Training ◽  
Clinical Overview

Artificial intelligence (AI) is a subset of computer science dealing with the development and training of algorithms that try to replicate human intelligence. We report a clinical overview of the basic principles of AI that are fundamental to appreciating its application to ophthalmology practice. Here, we review the most common eye diseases, focusing on some of the potential challenges and limitations emerging with the development and application of this new technology into ophthalmology.

Meanings of burnout and resilience in Ontario dietitians

2012 ◽  
Vol 1 (2) ◽  
pp. 13-20
Author(s):  
Yuka Asada
Keyword(s):  
Job Stress ◽  
Professional Growth ◽  
Maslach Burnout Inventory ◽  
Professional Relationships ◽  
Contributing Factors ◽  
Dietetic Education ◽  
Body Of Knowledge ◽  
Growth And Change ◽  
And Training ◽  
Preventative Approach

  ABSTRACT   Objectives: Although experiences of burnout are well documented among some health professionals, there is limited research that explores similar experiences among dietitians. This study aims (1) to describe the varied qualitative dimensions of burnout that are particular to dietitians and (2) to identify the factors that might be deemed protective against burnout. Methods: Fourteen dietitians were recruited from a larger quantitative study that assessed prevalence of burnout in Ontario, Canada using the Maslach Burnout Inventory (MBI). Those who completed the MBI were invited to participate in two phenomenological interviews. Transcribed interviews were analyzed by naïve readings and identified meaning units with a larger team for increased rigor and trustworthiness. Results: Dietitians describe burnout as having bodily and overall health consequences. Both social/professional relationships and dietitians’ passion for their work contributed to experiences of burnout and resilience. Opportunities for continued professional growth and change were contributing factors for resilience. Implications & Conclusions: This study contributes to the limited body of knowledge on dietitians’ lived experiences of burnout and resilience. The findings have implications for those involved in the education and training of student dietitians, and for those in a position to offer support to dietitians who are struggling with job stress. In the context of fostering resilience, a preventative approach to dietetic education is explored with the intention to protect future practitioners from burnout.

Artificial intelligence and the management transformation

2020 ◽  
Vol 16 (4) ◽  
pp. 600-612
Author(s):  
L.F. Nikulin ◽  
V.V. Velikorossov ◽  
S.A. Filin ◽  
A.B. Lanchakov
Keyword(s):  
Artificial Intelligence ◽  
Social Life ◽  
Soft Power ◽  
Process Integration ◽  
Management Practice ◽  
Scientific School ◽  
Strategic Culture ◽  
Analysis And Synthesis ◽  
And Training

Subject. The article discusses how management transforms as artificial intelligence gets more important in governance, production and social life. Objectives. We identify and substantiate trends in management transformation as artificial intelligence evolves and gets more important in governance, production and social life. The article also provides our suggestions for management and training of managers dealing with artificial intelligence. Methods. The study employs methods of logic research, analysis and synthesis through the systems and creative approach, methodology of technological waves. Results. We analyzed the scope of management as is and found that threats and global challenges escalate due to the advent of artificial intelligence. We provide the rationale for recognizing the strategic culture as the self-organizing system of business process integration. We suggest and substantiate the concept of soft power with reference to strategic culture, which should be raised, inter alia, through the scientific school of conflict studies. We give our recommendations on how management and training of managers should be improved in dealing with artificial intelligence as it evolves. The novelty hereof is that we trace trends in management transformation as the role of artificial intelligence evolves and growth in governance, production and social life. Conclusions and Relevance. Generic solutions are not very effective for the Russian management practice during the transition to the sixth and seventh waves of innovation. Any programming product represents artificial intelligence, which simulates a personality very well, though unable to substitute a manager in motivating, governing and interacting with people.

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

Virtual Screening Meets Deep Learning

2018 ◽  
Vol 15 (1) ◽  
pp. 6-28 ◽  
Author(s):  
Javier Pérez-Sianes ◽  
Horacio Pérez-Sánchez ◽  
Fernando Díaz
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Virtual Screening ◽  
Great Increase ◽  
New Drugs ◽  
Learning Approach ◽  
Screening Strategies ◽  
Computer Aided ◽  
Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

Autonomous Vehicles in the Mobility System

2020 ◽  
pp. 500-514
Author(s):  
Thilo von Pape
Keyword(s):  
Artificial Intelligence ◽  
Mobile Phones ◽  
Mobile Communication ◽  
Autonomous Vehicles ◽  
Communication Research ◽  
Collective Control ◽  
System P ◽  
Mobility System ◽  
And Control ◽  
Transportation Technologies

This chapter discusses how autonomous vehicles (AVs) may interact with our evolving mobility system and what they mean for mobile communication research. It juxtaposes a conceptualization of AVs as manifestations of automation and artificial intelligence with an analysis of our mobility system as a historically grown hybrid of communication and transportation technologies. Since the emergence of railroad and telegraph, this system has evolved on two layers: an underlying infrastructure to power and coordinate the movements of objects, people, and ideas in industrially scaled speeds, volumes, and complexity and an interface to seamlessly access this infrastructure and control it. AVs are poised to further enhance the seamlessness which mobile phones and cars already lent to mobility. But in assuming increasingly sophisticated control tasks, AVs also disrupt an established shift toward individual control, demanding new interfaces to enable higher levels of individual and collective control over the mobility infrastructure.

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