Ensembled artificial neural networks to predict the fitness score for body composition analysis

2010 ◽  
Vol 15 (5) ◽  
pp. 341-348 ◽  
Author(s):  
X. R. Cui ◽  
M. F. Abbod ◽  
Q. Liu ◽  
Jiann-Shing Shieh ◽  
T. Y. Chao ◽  
...  
Keyword(s):  
Neural Networks ◽  
Body Composition ◽  
Artificial Neural Networks ◽  
Composition Analysis ◽  
Body Composition Analysis ◽  
Fitness Score ◽  
Artificial Neural

scholarly journals Correction to: Fully automated body composition analysis in routine CT imaging using 3D semantic segmentation convolutional neural networks

2020 ◽  
Author(s):  
Sven Koitka ◽  
Lennard Kroll ◽  
Eugen Malamutmann ◽  
Arzu Oezcelik ◽  
Felix Nensa
Keyword(s):  
Neural Networks ◽  
Body Composition ◽  
Convolutional Neural Networks ◽  
Semantic Segmentation ◽  
Ct Imaging ◽  
Original Version ◽  
Composition Analysis ◽  
Body Composition Analysis

The original version of this article, published on 18 September 2020, unfortunately contained a mistake.

scholarly journals Use of artificial neural networks in biosensor signal classification

2008 ◽  
Vol 56 (2) ◽  
pp. 73-80
Author(s):  
Vlastimil Dohnal ◽  
Lenka Podloucká ◽  
Zuzana Grosmanová ◽  
Jiří Krejčí
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
New Method ◽  
Classification Model ◽  
Signal Classification ◽  
Composition Analysis ◽  
Chemical Information ◽  
Measured Signal ◽  
Artificial Neural ◽  
Substrate Addition

Biosensors are analytical devices that transforms chemical information, ranging from the concentration of a specific sample component to total composition analysis, into an analytical signal and that utilizes a biochemical mechanism for the chemical recognition. The complexity of biosensor construction and generation of measured signal requires the development of new method for signal eva­luation and its possible defects recognition. A new method based on artificial neural networks (ANN) was developed for recognition of characteristic behavior of signals joined with malfunction of sensor. New algorithm uses unsupervised Kohonen self-organizing neural networks. The work with ANN has two phases – adaptation and prediction. During the adaptation step the classification model is build. Measured data form groups after projection into two-dimensional space based on theirs similarity. After identification of these groups and establishing the connection with signal disorders ANN can be used for evaluation of newly measured signals. This algorithm was successfully applied for 540 signal classification obtained from immobilized acetylcholinesterase biosensor measurement of organophosphate and carbamate pesticides in vegetables, fruits, spices, potatoes and soil samples. From six different signal defects were successfully classified four – low response after substrate addition, equilibration at high values, slow equilibration after substrate addition respectively low sensitivity on syntostigmine.

OR35: Automated Body Composition Analysis of Computed Tomography Scans Using Neural Networks

2019 ◽  
Vol 38 ◽  
pp. S17
Author(s):  
M.T. Paris ◽  
D.K. Heyland ◽  
P. Tandon ◽  
H.F. Furberg ◽  
T. Premji ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Body Composition ◽  
Composition Analysis ◽  
Body Composition Analysis ◽  
Computed Tomography Scans

scholarly journals Automated body composition analysis of clinically acquired computed tomography scans using neural networks

2020 ◽  
Vol 39 (10) ◽  
pp. 3049-3055 ◽  
Author(s):  
Michael T. Paris ◽  
Puneeta Tandon ◽  
Daren K. Heyland ◽  
Helena Furberg ◽  
Tahira Premji ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Body Composition ◽  
Composition Analysis ◽  
Body Composition Analysis ◽  
Computed Tomography Scans

The capabilities of artificial neural networks in body composition research

2003 ◽  
Vol 40 (S1) ◽  
pp. s9-s14 ◽  
Author(s):  
R. Linder ◽  
E. I. Mohamed ◽  
A. De Lorenzo ◽  
S. J. Pöppl
Keyword(s):  
Neural Networks ◽  
Body Composition ◽  
Artificial Neural Networks ◽  
Artificial Neural

scholarly journals Fully automated body composition analysis in routine CT imaging using 3D semantic segmentation convolutional neural networks

2020 ◽  
Author(s):  
Sven Koitka ◽  
Lennard Kroll ◽  
Eugen Malamutmann ◽  
Arzu Oezcelik ◽  
Felix Nensa
Keyword(s):  
Neural Networks ◽  
Body Composition ◽  
Correlation Coefficients ◽  
Ct Imaging ◽  
Body Tissue ◽  
Composition Analysis ◽  
Tissue Composition ◽  
Intra Class Correlation ◽  
Body Composition Analysis ◽  
Body Regions

Abstract Objectives Body tissue composition is a long-known biomarker with high diagnostic and prognostic value not only in cardiovascular, oncological, and orthopedic diseases but also in rehabilitation medicine or drug dosage. In this study, the aim was to develop a fully automated, reproducible, and quantitative 3D volumetry of body tissue composition from standard CT examinations of the abdomen in order to be able to offer such valuable biomarkers as part of routine clinical imaging. Methods Therefore, an in-house dataset of 40 CTs for training and 10 CTs for testing were fully annotated on every fifth axial slice with five different semantic body regions: abdominal cavity, bones, muscle, subcutaneous tissue, and thoracic cavity. Multi-resolution U-Net 3D neural networks were employed for segmenting these body regions, followed by subclassifying adipose tissue and muscle using known Hounsfield unit limits. Results The Sørensen Dice scores averaged over all semantic regions was 0.9553 and the intra-class correlation coefficients for subclassified tissues were above 0.99. Conclusions Our results show that fully automated body composition analysis on routine CT imaging can provide stable biomarkers across the whole abdomen and not just on L3 slices, which is historically the reference location for analyzing body composition in the clinical routine. Key Points • Our study enables fully automated body composition analysis on routine abdomen CT scans. • The best segmentation models for semantic body region segmentation achieved an averaged Sørensen Dice score of 0.9553. • Subclassified tissue volumes achieved intra-class correlation coefficients over 0.99.

Determination of S-N curves with the application of artificial neural networks

1999 ◽  
Vol 22 (8) ◽  
pp. 723-728 ◽  
Author(s):  
Artymiak ◽  
Bukowski ◽  
Feliks ◽  
Narberhaus ◽  
Zenner
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Artificial Neural
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