Artificial neural networks as classification and diagnostic tools for lymph node-negative breast cancers

2016 ◽  
Vol 33 (4) ◽  
pp. 1318-1324 ◽  
Author(s):  
Satya Eswari J. ◽  
Neha Chandrakar
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Lymph Node ◽  
Diagnostic Tools ◽  
Breast Cancers ◽  
Node Negative ◽  
Artificial Neural

scholarly journals Artificial Neural Networks for Early Prediction of Mortality in Patients with Non Variceal Upper GI Bleeding (UGIB)

2008 ◽  
Vol 1 ◽  
pp. BII.S814 ◽  
Author(s):  
Enzo Grossi ◽  
Riccardo Marmo ◽  
Marco Intraligi ◽  
Massimo Buscema
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Endoscopic Examination ◽  
Diagnostic Tools ◽  
Random Allocation ◽  
Linear Discriminant ◽  
Risk Of Death ◽  
Prediction Of Mortality ◽  
Artificial Neural ◽  
Upper Gastrointestinal

Background Mortality for non variceal upper gastrointestinal bleeding (UGIB) is clinically relevant in the first 12–24 hours of the onset of haemorrhage and therefore identification of clinical factors predictive of the risk of death before endoscopic examination may allow for early corrective therapeutic intervention. Aim 1) Identify simple and early clinical variables predictive of the risk of death in patients with non variceal UGIB; 2) assess previsional gain of a predictive model developed with conventional statistics vs. that developed with artificial neural networks (ANNs). Methods and Results Analysis was performed on 807 patients with nonvariceal UGIB (527 males, 280 females), as a part of a multicentre Italian study. The mortality was considered “bleeding-related” if occurred within 30 days from the index bleeding episode. A total of 50 independent variables were analysed, 49 of which clinico-anamnestic, all collected prior to endoscopic examination plus the haemoglobin value measured on admission in the emergency department. Death occurred in 42 (5.2%). Conventional statistical techniques (linear discriminant analysis) were compared with ANNs (Twist® system-Semeion) adopting the same result validation protocol with random allocation of the sample in training and testing subsets and subsequent cross-over. ANNs resulted to be significantly more accurate than LDA with an overall accuracy rate near to 90%. Conclusion Artificial neural networks technology is highly promising in the development of accurate diagnostic tools designed to recognize patients at high risk of death for UGIB.

scholarly journals Prediction of lymph node parasite load from clinical data in dogs with leishmaniasis: An application of radial basis artificial neural networks

2017 ◽  
Vol 234 ◽  
pp. 13-18
Author(s):  
Rafaela Beatriz Pintor Torrecilha ◽  
Yuri Tani Utsunomiya ◽  
Luís Fábio da Silva Batista ◽  
Anelise Maria Bosco ◽  
Cáris Maroni Nunes ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Lymph Node ◽  
Clinical Data ◽  
Parasite Load ◽  
Radial Basis ◽  
Artificial Neural

Artificial neural networks for prediction of lymph node status in breast cancer patients

2007 ◽  
Vol 68 (4) ◽  
pp. 922-923 ◽  
Author(s):  
Selda Tez ◽  
Ömer Yoldaş ◽  
Yusuf Alper Kılıç ◽  
Hayrettin Dizen ◽  
Mesut Tez
Keyword(s):  
Breast Cancer ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Lymph Node ◽  
Cancer Patients ◽  
Lymph Node Status ◽  
Breast Cancer Patients ◽  
Node Status ◽  
Artificial Neural

APPLICATION OF DEEP LEARNING OF ARTIFICIAL NEURAL NETWORKS IN MONITORING THE SIGHT SYSTEM STATE IN LONG-DURATION SPACE MISSIONS

2021 ◽  
Vol 55 (6) ◽  
pp. 19-22
Author(s):  
O.V. Perevedentsev ◽  
◽  
V.M. Levanov ◽  
O.I. Orlov ◽  
O.M. Manko ◽  
...  
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Space Missions ◽  
Optic Disk ◽  
Diagnostic Tools ◽  
Basic Block ◽  
Health Monitoring System ◽  
Long Duration ◽  
Artificial Neural ◽  
Sensitivity Specificity

Integration of artificial intelligence in cosmonaut's health monitoring system is a top priority trend in space medicine aimed to enhance crew safety in long-duration space missions. The cutting-edge diagnostic tools aboard the ISS Russian segment such as digital funduscopy, and optic CT of the retina and optic disk allow of piling up statistics of pre- in- and post-flight examinations for calculating the basic block for AI applications. Timely detection of visual dysfunctions in autonomous remote space missions with the help of AI algorithms will increase efficiency of prevention and therapy. Analysis of the experience with convolutional neural networks inophthalmology suggested a CNW architecture for detecting eye grounds pathologies by DF automatic analysis. A developed binary classifier demonstrated sensitivity, specificity and precision of 88.6%, 85.2% and 87%, respectively. In remote missions, the healthcare and therapy system should embody the concept of intelligence telemedicine circuit with artificial neural networks being the pivot. Digital ophthalmology with integrated CNW techniques can be useful also for early diagnostics of eye grounds pathologies within the program of exploration crews screening.

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