Improving Ischemic Stroke Care With MRI and Deep Learning Artificial Intelligence

2021 ◽  
Vol 30 (4) ◽  
pp. 187-195
Author(s):  
Yannan Yu ◽  
Jeremy J. Heit ◽  
Greg Zaharchuk
Keyword(s):  
Artificial Intelligence ◽  
Ischemic Stroke ◽  
Deep Learning ◽  
Stroke Care

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.

scholarly journals An overview of artificial intelligence/deep learning

Pathology ◽  
2021 ◽  
Vol 53 ◽  
pp. S6
Author(s):  
Jack Garland ◽  
Mindy Hu ◽  
Kilak Kesha ◽  
Charley Glenn ◽  
Michael Duffy ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning

In-Hospital Stroke Care: A Six-Year Community-Based Primary Stroke Center Experience

2021 ◽  
pp. 194187442110070
Author(s):  
Felix Ejike Chukwudelunzu ◽  
Bart M Demaerschalk ◽  
Leonardo Fugoso ◽  
Emeka Amadi ◽  
Donn Dexter ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Hospital Mortality ◽  
Statistical Significance ◽  
Intravenous Thrombolysis ◽  
Stroke Care ◽  
Community Based ◽  
Chi Square ◽  
Stroke Team ◽  
Primary Stroke Center ◽  
Stroke Center

Background and purpose: In-hospital stroke-onset assessment and management present numerous challenges, especially in community hospitals. Comprehensive analysis of key stroke care metrics in community-based primary stroke centers is under-studied. Methods: Medical records were reviewed for patients admitted to a community hospital for non-cerebrovascular indications and for whom a stroke alert was activated between 2013 and 2019. Demographic, clinical, radiologic and laboratory information were collected for each incident stroke. Descriptive statistical analysis was employed. When applicable, Kruskal-Wallis and Chi-Square tests were used to compare median values and categorical data between pre-specified groups. Statistical significance was set at alpha = 0.05. Results: There were 192 patients with in-hospital stroke-alert activation; mean age (SD) was 71.0 years (15.0), 49.5% female. 51.6% (99/192) had in-hospital ischemic and hemorrhagic stroke. The most frequent mechanism of stroke was cardioembolism. Upon stroke activation, 45.8% had ischemic stroke while 40.1% had stroke mimics. Stroke team response time from activation was 26 minutes for all in-hospital activations. Intravenous thrombolysis was utilized in 8% of those with ischemic stroke; 3.4% were transferred for consideration of endovascular thrombectomy. In-hospital mortality was 17.7%, and the proportion of patients discharged to home was 34.4% for all activations. Conclusion: The in-hospital stroke mortality was high, and the proportions of patients who either received or were considered for acute intervention were low. Quality improvement targeting increased use of acute stroke intervention in eligible patients and reducing hospital mortality in this patient cohort is needed.

scholarly journals Expert opinion paper on cardiac imaging after ischemic stroke

2021 ◽  
Author(s):  
Renate B. Schnabel ◽  
Stephan Camen ◽  
Fabian Knebel ◽  
Andreas Hagendorff ◽  
Udo Bavendiek ◽  
...  
Keyword(s):  
Ischemic Stroke ◽  
Expert Opinion ◽  
Cardiac Imaging ◽  
Controlled Trial ◽  
Case Series ◽  
Stroke Care ◽  
Imaging Method ◽  
Cardiac Evaluation ◽  
Magnetic Resonance Imaging Mri ◽  
Ischemic Attack

AbstractThis expert opinion paper on cardiac imaging after acute ischemic stroke or transient ischemic attack (TIA) includes a statement of the “Heart and Brain” consortium of the German Cardiac Society and the German Stroke Society. The Stroke Unit-Commission of the German Stroke Society and the German Atrial Fibrillation NETwork (AFNET) endorsed this paper. Cardiac imaging is a key component of etiological work-up after stroke. Enhanced echocardiographic tools, constantly improving cardiac computer tomography (CT) as well as cardiac magnetic resonance imaging (MRI) offer comprehensive non- or less-invasive cardiac evaluation at the expense of increased costs and/or radiation exposure. Certain imaging findings usually lead to a change in medical secondary stroke prevention or may influence medical treatment. However, there is no proof from a randomized controlled trial (RCT) that the choice of the imaging method influences the prognosis of stroke patients. Summarizing present knowledge, the German Heart and Brain consortium proposes an interdisciplinary, staged standard diagnostic scheme for the detection of risk factors of cardio-embolic stroke. This expert opinion paper aims to give practical advice to physicians who are involved in stroke care. In line with the nature of an expert opinion paper, labeling of classes of recommendations is not provided, since many statements are based on expert opinion, reported case series, and clinical experience.

Artificial Intelligence and Human Rights: Four Realms of Discussion: Summary of Remarks

2020 ◽  
Vol 114 ◽  
pp. 242-245
Author(s):  
Jootaek Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Language Processing ◽  
Human Reasoning ◽  
The Past ◽  
Making Choices ◽  
Data Capturing ◽  
Intensive Procedures

The term, Artificial Intelligence (AI), has changed since it was first coined by John MacCarthy in 1956. AI, believed to have been created with Kurt Gödel's unprovable computational statements in 1931, is now called deep learning or machine learning. AI is defined as a computer machine with the ability to make predictions about the future and solve complex tasks, using algorithms. The AI algorithms are enhanced and become effective with big data capturing the present and the past while still necessarily reflecting human biases into models and equations. AI is also capable of making choices like humans, mirroring human reasoning. AI can help robots to efficiently repeat the same labor intensive procedures in factories and can analyze historic and present data efficiently through deep learning, natural language processing, and anomaly detection. Thus, AI covers a spectrum of augmented intelligence relating to prediction, autonomous intelligence relating to decision making, automated intelligence for labor robots, and assisted intelligence for data analysis.

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