Artificial intelligence, machine (deep) learning and radio(geno)mics: definitions and nuclear medicine imaging applications

2019 ◽  
Vol 46 (13) ◽  
pp. 2630-2637 ◽  
Author(s):  
Dimitris Visvikis ◽  
Catherine Cheze Le Rest ◽  
Vincent Jaouen ◽  
Mathieu Hatt
Keyword(s):  
Artificial Intelligence ◽  
Nuclear Medicine ◽  
Deep Learning ◽  
Nuclear Medicine Imaging

Patients with fever of unknown origin (FUO): diagnosis by nuclear medicine imaging

2001 ◽  
Vol 40 (03) ◽  
pp. 59-70 ◽  
Author(s):  
W. Becker ◽  
J. Meiler
Keyword(s):  
Nuclear Medicine ◽  
Fever Of Unknown Origin ◽  
Tumor Imaging ◽  
White Blood Cells ◽  
Unknown Origin ◽  
Final Diagnosis ◽  
Recurrent Fever ◽  
Nuclear Medicine Imaging

SummaryFever of unknown origin (FUO) in immunocompetent and non neutropenic patients is defined as recurrent fever of 38,3° C or greater, lasting 2-3 weeks or longer, and undiagnosed after 1 week of appropriate evaluation. The underlying diseases of FUO are numerous and infection accounts for only 20-40% of them. The majority of FUO-patients have autoimmunity and collagen vascular disease and neoplasm, which are responsible for about 50-60% of all cases. In this respect FOU in its classical definition is clearly separated from postoperative and neutropenic fever where inflammation and infection are more common. Although methods that use in-vitro or in-vivo labeled white blood cells (WBCs) have a high diagnostic accuracy in the detection and exclusion of granulocytic pathology, they are only of limited value in FUO-patients in establishing the final diagnosis due to the low prevalence of purulent processes in this collective. WBCs are more suited in evaluation of the focus in occult sepsis. Ga-67 citrate is the only commercially available gamma emitter which images acute, chronic, granulomatous and autoimmune inflammation and also various malignant diseases. Therefore Ga-67 citrate is currently considered to be the tracer of choice in the diagnostic work-up of FUO. The number of Ga-67-scans contributing to the final diagnosis was found to be higher outside Germany than it has been reported for labeled WBCs. F-l 8-2’-deoxy-2-fluoro-D-glucose (FDG) has been used extensively for tumor imaging with PET. Inflammatory processes accumulate the tracer by similar mechanisms. First results of FDG imaging demonstrated, that FDG may be superior to other nuclear medicine imaging modalities which may be explained by the preferable tracer kinetics of the small F-l 8-FDG molecule and by a better spatial resolution of coincidence imaging in comparison to a conventional gamma camera.

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

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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