Literature DB >> 12767358

Artificial neural networks for decision-making in urologic oncology.

Theodore Anagnostou1, Mesut Remzi, Michael Lykourinas, Bob Djavan.   

Abstract

The authors are presenting a thorough introduction in Artificial Neural Networks (ANNs) and their contribution to modern Urologic Oncology. The article covers a description of Artificial Neural Network methodology and points out the differences of Artificial Intelligence to traditional statistic models in terms of serving patients and clinicians, in a different way than current statistical analysis. Since Artificial Intelligence is not yet fully understood by many practicing clinicians, the authors have reviewed a careful selection of articles in order to explore the clinical benefit of Artificial Intelligence applications in modern Urology questions and decision-making. The data are from real patients and reflect attempts to achieve more accurate diagnosis and prognosis, especially in prostate cancer that stands as a good example of difficult decision-making in everyday practice. Experience from current use of Artificial Intelligence is also being discussed, and the authors address future developments as well as potential problems such as medical record quality, precautions in using ANNs or resistance to system use, in an attempt to point out future demands and the need for common standards. The authors conclude that both methods should continue to be used in a complementary manner. ANNs still do not prove always better as to replace standard statistical analysis as the method of choice in interpreting medical data.

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Year:  2003        PMID: 12767358     DOI: 10.1016/s0302-2838(03)00133-7

Source DB:  PubMed          Journal:  Eur Urol        ISSN: 0302-2838            Impact factor:   20.096


  15 in total

Review 1.  Modeling paradigms for medical diagnostic decision support: a survey and future directions.

Authors:  Kavishwar B Wagholikar; Vijayraghavan Sundararajan; Ashok W Deshpande
Journal:  J Med Syst       Date:  2011-10-01       Impact factor: 4.460

2.  Identification of Marker Genes for Cancer Based on Microarrays Using a Computational Biology Approach.

Authors:  Xiaosheng Wang
Journal:  Curr Bioinform       Date:  2014-04-01       Impact factor: 3.543

Review 3.  Artificial neural networks and prostate cancer--tools for diagnosis and management.

Authors:  Xinhai Hu; Henning Cammann; Hellmuth-A Meyer; Kurt Miller; Klaus Jung; Carsten Stephan
Journal:  Nat Rev Urol       Date:  2013-02-12       Impact factor: 14.432

4.  Artificial neural networks in prediction of bone density among post-menopausal women.

Authors:  M Sadatsafavi; A Moayyeri; A Soltani; B Larijani; M Nouraie; S Akhondzadeh
Journal:  J Endocrinol Invest       Date:  2005-05       Impact factor: 4.256

5.  Artificial neural network to predict skeletal metastasis in patients with prostate cancer.

Authors:  Jainn-Shiun Chiu; Yuh-Feng Wang; Yu-Cheih Su; Ling-Huei Wei; Jian-Guo Liao; Yu-Chuan Li
Journal:  J Med Syst       Date:  2009-04       Impact factor: 4.460

6.  Development of a method based on surface enhanced laser desorption and ionization time of flight mass spectrometry for rapid identification of Klebsiella pneumoniae.

Authors:  Daiwen Xiao; Yongchang Yang; Hua Liu; Hua Yu; Yingjun Yan; Wenfang Huang; Wei Jiang; Weijin Liao; Qi Hu; Bo Huang
Journal:  J Microbiol       Date:  2009-10-24       Impact factor: 3.422

7.  Comparing Three Data Mining Methods to Predict Kidney Transplant Survival.

Authors:  Leila Shahmoradi; Mostafa Langarizadeh; Gholamreza Pourmand; Ziba Aghsaei Fard; Alireza Borhani
Journal:  Acta Inform Med       Date:  2016-11-01

8.  Multiproject-multicenter evaluation of automatic brain tumor classification by magnetic resonance spectroscopy.

Authors:  Juan M García-Gómez; Jan Luts; Margarida Julià-Sapé; Patrick Krooshof; Salvador Tortajada; Javier Vicente Robledo; Willem Melssen; Elies Fuster-García; Iván Olier; Geert Postma; Daniel Monleón; Angel Moreno-Torres; Jesús Pujol; Ana-Paula Candiota; M Carmen Martínez-Bisbal; Johan Suykens; Lutgarde Buydens; Bernardo Celda; Sabine Van Huffel; Carles Arús; Montserrat Robles
Journal:  MAGMA       Date:  2008-11-07       Impact factor: 2.310

9.  Pain point system scale (PPSS): a method for postoperative pain estimation in retrospective studies.

Authors:  Anastasia Gkotsi; Dimosthenis Petsas; Vasilios Sakalis; Asterios Fotas; Argyrios Triantafyllidis; Ioannis Vouros; Evangelos Saridakis; Georgios Salpiggidis; Athanasios Papathanasiou
Journal:  J Pain Res       Date:  2012-11-07       Impact factor: 3.133

Review 10.  Big Data Analytics for Prostate Radiotherapy.

Authors:  James Coates; Luis Souhami; Issam El Naqa
Journal:  Front Oncol       Date:  2016-06-14       Impact factor: 6.244

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