Deep‐Learning‐Based Artificial Intelligence for PI‐RADS Classification to Assist Multiparametric Prostate MRI Interpretation: A Development Study

2020 ◽  
Vol 52 (5) ◽  
pp. 1499-1507 ◽  
Author(s):  
Thomas Sanford ◽  
Stephanie A. Harmon ◽  
Evrim B. Turkbey ◽  
Deepak Kesani ◽  
Sena Tuncer ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Development Study ◽  
Prostate Mri ◽  
Multiparametric Prostate Mri

Deep learning-based artificial intelligence applications in prostate MRI: brief summary

2021 ◽  
Author(s):  
Baris Turkbey ◽  
Masoom A. Haider
Keyword(s):  
Artificial Intelligence ◽  
Deep Learning ◽  
Diagnostic Performance ◽  
Lesion Detection ◽  
Western World ◽  
Complex Nature ◽  
Cancer Type ◽  
Prostate Mri ◽  
Prostate Segmentation ◽  
Established Role

Prostate cancer (PCa) is the most common cancer type in males in the Western World. MRI has an established role in diagnosis of PCa through guiding biopsies. Due to multistep complex nature of the MRI-guided PCa diagnosis pathway, diagnostic performance has a big variation. Developing artificial intelligence (AI) models using machine learning, particularly deep learning, has an expanding role in radiology. Specifically, for prostate MRI, several AI approaches have been defined in the literature for prostate segmentation, lesion detection and classification with the aim of improving diagnostic performance and interobserver agreement. In this review article, we summarize the use of radiology applications of AI in prostate MRI.

scholarly journals MP74-10 DEEP LEARNING FOR SEMI-AUTOMATED PIRADSV2 SCORING ON MULTIPARAMETRIC PROSTATE MRI

2019 ◽  
Vol 201 (Supplement 4) ◽  
Author(s):  
Tom Sanford* ◽  
Stephanie Harmon ◽  
Manuel Madariaga ◽  
Deepak Kesani ◽  
Sherif Mehralivand ◽  
...  
Keyword(s):  
Deep Learning ◽  
Prostate Mri ◽  
Multiparametric Prostate Mri

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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