scholarly journals Recent Development of Artificial Intelligence Applied in Echocardiography: A Review (Preprint)

2019 ◽  
Author(s):  
Lu Liu ◽  
Ahmed Elazab ◽  
Baiying Lei ◽  
Tianfu Wang
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Cardiovascular Diseases ◽  
Disease Diagnosis ◽  
Machine Learning Techniques ◽  
Tissue Segmentation ◽  
The Past ◽  
Learning Techniques ◽  
The Future

BACKGROUND Echocardiography has a pivotal role in the diagnosis and management of cardiovascular diseases since it is real-time, cost-effective, and non-invasive. The development of artificial intelligence (AI) techniques have led to more intelligent and automatic computer-aided diagnosis (CAD) systems in echocardiography over the past few years. Automatic CAD mainly includes classification, detection of anatomical structures, tissue segmentation, and disease diagnosis, which are mainly completed by machine learning techniques and the recent developed deep learning techniques. OBJECTIVE This review aims to provide a guide for researchers and clinicians on relevant aspects of AI, machine learning, and deep learning. In addition, we review the recent applications of these methods in echocardiography and identify how echocardiography could incorporate AI in the future. METHODS This paper first summarizes the overview of machine learning and deep learning. Second, it reviews current use of AI in echocardiography by searching literature in the main databases for the past 10 years and finally discusses potential limitations and challenges in the future. RESULTS AI has showed promising improvements in analysis and interpretation of echocardiography to a new stage in the fields of standard views detection, automated analysis of chamber size and function, and assessment of cardiovascular diseases. CONCLUSIONS Compared with machine learning, deep learning methods have achieved state-of-the-art performance across different applications in echocardiography. Although there are challenges such as the required large dataset, AI can provide satisfactory results by devising various strategies. We believe AI has the potential to improve accuracy of diagnosis, reduce time consumption, and decrease the load of cardiologists.

scholarly journals LITERATURE SURVEY ON MACHINE LEARNING BASED TECHNIQUES IN MEDICAL DATAANALYSIS

2019 ◽  
pp. 1-4
Author(s):  
Lavanya Vemulapalli
Keyword(s):  
Machine Learning ◽  
Disease Diagnosis ◽  
Research Area ◽  
Machine Learning Techniques ◽  
Intelligent Algorithms ◽  
The Past ◽  
Disease Prognosis ◽  
Learning Techniques ◽  
Machine Learning Applications ◽  
Comprehensive Survey

Machine Learning plays a significant role among the areas of Artificial Intelligence (AI). During recent years, Machine Learning (ML) has been attracting many researchers, and it has been successfully applied in many fields such as medical, education, forecasting etc., Right now, the diagnosis of diseases is mostly from expert's decision. Diagnosis is a major task in clinical science as it is crucial in determining if a patient is having the disease or not. This in turn decides the suitable path of treatment for disease diagnosis. Applying machine learning techniques for disease diagnosis using intelligent algorithms has been a hot research area of computer science. This paper throws a light on the comprehensive survey on the machine learning applications in the medical disease prognosis during the past decades

scholarly journals Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases

2018 ◽  
Vol 20 (5) ◽  
pp. 1878-1912 ◽  
Author(s):  
Ahmet Sureyya Rifaioglu ◽  
Heval Atas ◽  
Maria Jesus Martin ◽  
Rengul Cetin-Atalay ◽  
Volkan Atalay ◽  
...  
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Drug Discovery ◽  
Machine Intelligence ◽  
New Drugs ◽  
Machine Learning Techniques ◽  
The Past ◽  
Screening Experiments ◽  
Learning Techniques ◽  
Computational Drug Discovery

Abstract The identification of interactions between drugs/compounds and their targets is crucial for the development of new drugs. In vitro screening experiments (i.e. bioassays) are frequently used for this purpose; however, experimental approaches are insufficient to explore novel drug-target interactions, mainly because of feasibility problems, as they are labour intensive, costly and time consuming. A computational field known as ‘virtual screening’ (VS) has emerged in the past decades to aid experimental drug discovery studies by statistically estimating unknown bio-interactions between compounds and biological targets. These methods use the physico-chemical and structural properties of compounds and/or target proteins along with the experimentally verified bio-interaction information to generate predictive models. Lately, sophisticated machine learning techniques are applied in VS to elevate the predictive performance. The objective of this study is to examine and discuss the recent applications of machine learning techniques in VS, including deep learning, which became highly popular after giving rise to epochal developments in the fields of computer vision and natural language processing. The past 3 years have witnessed an unprecedented amount of research studies considering the application of deep learning in biomedicine, including computational drug discovery. In this review, we first describe the main instruments of VS methods, including compound and protein features (i.e. representations and descriptors), frequently used libraries and toolkits for VS, bioactivity databases and gold-standard data sets for system training and benchmarking. We subsequently review recent VS studies with a strong emphasis on deep learning applications. Finally, we discuss the present state of the field, including the current challenges and suggest future directions. We believe that this survey will provide insight to the researchers working in the field of computational drug discovery in terms of comprehending and developing novel bio-prediction methods.

scholarly journals Scope of Artificial Intelligence in Law

2018 ◽  
Author(s):  
Jyoti Dabass ◽  
Bhupender Singh Dabass
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Deep Learning ◽  
Natural Language Processing ◽  
Language Processing ◽  
Machine Learning Techniques ◽  
Law Firms ◽  
Legal Services ◽  
Human Beings ◽  
Learning Techniques

Over the years, artificial intelligence (AI) is spreading its roots in different areas by utilizing the concept of making the computers learn and handle complex tasks that previously require substantial laborious tasks by human beings. With better accuracy and speed, AI is helping lawyers to streamline work processing. New legal AI software tools like Catalyst, Ross intelligence, and Matlab along with natural language processing provide effective quarrel resolution, better legal clearness, and superior admittance to justice and fresh challenges to conventional law firms providing legal services using leveraged cohort correlate model. This paper discusses current applications of legal AI and suggests deep learning and machine learning techniques that can be applied in future to simplify the cumbersome legal tasks.

scholarly journals A Review on Machine Learning Techniques to Predict Diseases

2019 ◽  
pp. 525-529
Author(s):  
Rohit A Nitnaware ◽  
Prof. Vijaya Kamble
Keyword(s):  
Machine Learning ◽  
Disease Diagnosis ◽  
Disease Classification ◽  
Machine Learning Techniques ◽  
High Dimensional ◽  
The Past ◽  
Course Of Action ◽  
Learning Techniques ◽  
Past Data ◽  
Game Plan

In Disease Diagnosis, affirmation of models is so basic for perceiving the disease exactly. Machine learning is the field, which is used for building the models that can predict the yield relies upon the wellsprings of data, which are connected subject to the past data. Disease unmistakable verification is the most essential task for treating any disease. Classification computations are used for orchestrating the disease. There are a couple of classification computations and dimensionality decline counts used. Machine Learning empowers the PCs to learn without being changed remotely. By using the Classification Algorithm, a hypothesis can be looked over the course of action of decisions the best fits a game plan of recognition. Machine Learning is used for the high dimensional and the multi-dimensional data. Better and modified computations can be made using Machine Learning.

scholarly journals Use of Artificial Intelligence/Machine Learning in Cancer Research During the COVID-19 Pandemic

2020 ◽  
Vol 5 (S1) ◽  
pp. 251-253
Author(s):  
Vineet Kumar Kamal ◽  
Dolly Kumari
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Cancer Patients ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques ◽  
Imaging Data ◽  
The Past ◽  
Learning Techniques ◽  
Increased Risk ◽  
The Right

The cancer patients are more vulnerable and are at increased risk of COVID-19 and related outcomes due to their weakened immune systems, specially patients with lung cancer. Amid pandemic, the diagnosis, treatment, and care of cancer patients are very difficult and challenging due to several factors. In such situations, the latest technology in artificial intelligence (AI) or machine learning algorithms (ML) have potential to provide better diagnosis, treatments and cares of cancer patients. For example, the researches may use clinical and imaging data with machine learning techniques to make differences between coronavirus-related lung changes and those caused by immunotherapy and radiotherapy. During this pandemic, AI can be used to ensure we are getting the right patients enrolled speedily and more efficiently than the traditional, and complex ways in the past in cancer clinical trials. This is the appropriate time to go beyond the “research as usual” approach and update our research via AI and ML tools to care the cancer patients and discover new and more effective treatments.

DeepLearning for Computer Vision Problems

2021 ◽  
pp. 92-109
Author(s):  
Khadidja Zairi
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Vision ◽  
Deep Learning ◽  
Machine Learning Techniques ◽  
Nonlinear Transformations ◽  
Learning Techniques ◽  
Large Databases ◽  
Previous State

Deep learning is a combined area between neural network and machine learning. Over the last years, deep learning methods have been shown to outperform previous state-of-the-art machine learning techniques in several fields. With computer vision being one of the most prominent cases, the deep learning methodology applies nonlinear transformations and model abstractions of high levels in large databases. Therefore, an overview of DL methodology is provided along with its major modal principals and its hierarchy, which are presented and compared with the more conventional algorithms. Likewise, its popularity and usefulness in the artificial intelligence world are discussed, and some important techniques that increase DL performance are highlighted.

scholarly journals Artificial intelligence in reproductive medicine

Reproduction ◽  
2019 ◽  
Vol 158 (4) ◽  
pp. R139-R154 ◽  
Author(s):  
Renjie Wang ◽  
Wei Pan ◽  
Lei Jin ◽  
Yuehan Li ◽  
Yudi Geng ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Reproductive Medicine ◽  
Large Datasets ◽  
Machine Learning Techniques ◽  
Future Directions ◽  
Computing Power ◽  
Implementation Phase ◽  
The Past ◽  
Learning Techniques

Artificial intelligence (AI) has experienced rapid growth over the past few years, moving from the experimental to the implementation phase in various fields, including medicine. Advances in learning algorithms and theories, the availability of large datasets and improvements in computing power have contributed to breakthroughs in current AI applications. Machine learning (ML), a subset of AI, allows computers to detect patterns from large complex datasets automatically and uses these patterns to make predictions. AI is proving to be increasingly applicable to healthcare, and multiple machine learning techniques have been used to improve the performance of assisted reproductive technology (ART). Despite various challenges, the integration of AI and reproductive medicine is bound to give an essential direction to medical development in the future. In this review, we discuss the basic aspects of AI and machine learning, and we address the applications, potential limitations and challenges of AI. We also highlight the prospects and future directions in the context of reproductive medicine.

scholarly journals Predicting the Performance of Contestants in Competitive Programming Using Machine Learning Techniques

2020 ◽  
pp. 3-20
Author(s):  
Ammar ALNAHHAS ◽  
Nour MOURTADA
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Learning Model ◽  
Machine Learning Techniques ◽  
Training Experience ◽  
Future Performance ◽  
Learning Techniques ◽  
Public Data ◽  
The Future ◽  
Deep Learning Model

Training is an important task in competitive programming, coaches can improve the training experience if they can get information about the performance of contestants. In this paper, we study the possibility of using machine learning techniques to build a system that is able to predict the future performance of a contestant by analyzing their historical rating list. This system learns from a dataset of contestant ratings. We propose to apply five different baseline machine learning techniques, then we propose a new deep learning model. We conduct an experiment using public data from the Codeforces website. We show that most techniques achieve acceptable results. In addition, the proposed deep learning model outperformed all baseline methods and achieved results that proved its efficiency in predicting the future performance of contestants. This paper confirms the possibility of using machine learning techniques to help in the process of preparing contestants in competitive programming.

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