Literature DB >> 8800133

Artificial neural networks: current status in cardiovascular medicine.

D Itchhaporia1, P B Snow, R J Almassy, W J Oetgen.   

Abstract

Artificial neural networks are a form of artificial computer intelligence that have been the subject of renewed research interest in the last 10 years. Although they have been used extensively for problems in engineering, they have only recently been applied to medical problems, particularly in the fields of radiology, urology, laboratory medicine and cardiology. An artificial neural network is a distributed network of computing elements that is modeled after a biologic neural system and may be implemented as a computer software program. It is capable of identifying relations in input data that are not easily apparent with current common analytic techniques. The functioning artificial neural network's knowledge is built on learning and experience from previous input data. On the basis of this prior knowledge, the artificial neural network can predict relations found in newly presented data sets. In cardiology, artificial neural networks have been successfully applied to problems in the diagnosis and treatment of coronary artery disease and myocardial infarction, in electrocardiographic interpretation and detection of arrhythmias and in image analysis in cardiac radiography and sonography. This report focuses on the current status of artificial neural network technology in cardiovascular medical research.

Entities:  

Mesh:

Year:  1996        PMID: 8800133     DOI: 10.1016/0735-1097(96)00174-X

Source DB:  PubMed          Journal:  J Am Coll Cardiol        ISSN: 0735-1097            Impact factor:   24.094


  16 in total

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Review 6.  Artificial intelligence in medicine and cardiac imaging: harnessing big data and advanced computing to provide personalized medical diagnosis and treatment.

Authors:  Steven E Dilsizian; Eliot L Siegel
Journal:  Curr Cardiol Rep       Date:  2014-01       Impact factor: 2.931

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Journal:  Hepatol Int       Date:  2007-11-27       Impact factor: 6.047

8.  Can neural network able to estimate the prognosis of epilepsy patients according to risk factors?

Authors:  Kezban Aslan; Hacer Bozdemir; Cenk Sahin; S Noyan Ogulata
Journal:  J Med Syst       Date:  2009-03-28       Impact factor: 4.460

9.  Application of intelligent systems in asthma disease: designing a fuzzy rule-based system for evaluating level of asthma exacerbation.

Authors:  Maryam Zolnoori; Mohammad Hossein Fazel Zarandi; Mostafa Moin
Journal:  J Med Syst       Date:  2011-03-12       Impact factor: 4.460

10.  Automated bony region identification using artificial neural networks: reliability and validation measurements.

Authors:  Esther E Gassman; Stephanie M Powell; Nicole A Kallemeyn; Nicole A Devries; Kiran H Shivanna; Vincent A Magnotta; Austin J Ramme; Brian D Adams; Nicole M Grosland
Journal:  Skeletal Radiol       Date:  2008-01-03       Impact factor: 2.199

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