A Novel Morphological Analysis of DXA-DICOM Images by Artificial Neural Networks for Estimating Bone Mineral Density in Health and Disease

2019 ◽  
Vol 22 (3) ◽  
pp. 382-390 ◽  
Author(s):  
Ehab I. Mohamed ◽  
Radwa A. Meshref ◽  
Samir M. Abdel-Mageed ◽  
Moustafa H. Moustafa ◽  
Mohamed I. Badawi ◽  
...  
Keyword(s):  
Bone Mineral Density ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Bone Mineral ◽  
Morphological Analysis ◽  
Mineral Density ◽  
Health And Disease ◽  
Artificial Neural

scholarly journals Artificial neural networks to predict future bone mineral density and bone loss rate in Japanese postmenopausal women

2017 ◽  
Vol 10 (1) ◽  
Author(s):  
Mitsunori Shioji ◽  
Takehisa Yamamoto ◽  
Takeshi Ibata ◽  
Takayuki Tsuda ◽  
Kazushige Adachi ◽  
...  
Keyword(s):  
Bone Mineral Density ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Bone Loss ◽  
Postmenopausal Women ◽  
Bone Mineral ◽  
Loss Rate ◽  
Mineral Density ◽  
Artificial Neural ◽  
Bone Loss Rate

scholarly journals Modelling and simulation of artificial neural networks using software SIMBRAIN

2021 ◽  
Vol 7 ◽  
pp. 71-81
Author(s):  
Yoana Ivanova
Keyword(s):  
Neural Networks ◽  
Decision Making ◽  
Pattern Recognition ◽  
Artificial Neural Networks ◽  
Morphological Analysis ◽  
Critical Infrastructure ◽  
Modelling And Simulation ◽  
Advanced Technology ◽  
Artificial Neural ◽  
And Robotics

This paper is considered to be a continuation of a previous publication devoted to tendencies in the applications of advanced technology solutions to strengthen the cybersecurity of critical infrastructure (Yearbook Telecommunications, vol. 6, 2019). The specificity of the research is related to tracing the evolution of artificial neural networks (ANN) from their establishment to their modelling and simulation. The theoretical framework involves a well-supported rationale by some practical examples of advanced methods of design and simulation of ANN using SIMBRAIN. These methods are applicable in Cognitive science and Robotics because of their contribution to scientific researches related to study of perceptions and behaviors, abilities of decision making, pattern recognition and morphological analysis and etc.

scholarly journals Mouse mutant phenotyping at scale reveals novel genes controlling bone mineral density

PLoS Genetics ◽  
2020 ◽  
Vol 16 (12) ◽  
pp. e1009190
Author(s):  
Anna L. Swan ◽  
Christine Schütt ◽  
Jan Rozman ◽  
Maria del Mar Muñiz Moreno ◽  
Stefan Brandmaier ◽  
...  
Keyword(s):  
Bone Mineral Density ◽  
Bone Mineral ◽  
Mutant Mouse ◽  
Mouse Strains ◽  
Mouse Mutant ◽  
Bone Biology ◽  
Mineral Density ◽  
Genetic Landscape ◽  
Health And Disease ◽  
Insight Into

The genetic landscape of diseases associated with changes in bone mineral density (BMD), such as osteoporosis, is only partially understood. Here, we explored data from 3,823 mutant mouse strains for BMD, a measure that is frequently altered in a range of bone pathologies, including osteoporosis. A total of 200 genes were found to significantly affect BMD. This pool of BMD genes comprised 141 genes with previously unknown functions in bone biology and was complementary to pools derived from recent human studies. Nineteen of the 141 genes also caused skeletal abnormalities. Examination of the BMD genes in osteoclasts and osteoblasts underscored BMD pathways, including vesicle transport, in these cells and together with in silico bone turnover studies resulted in the prioritization of candidate genes for further investigation. Overall, the results add novel pathophysiological and molecular insight into bone health and disease.

scholarly journals Optimization the Initial Weights of Artificial Neural Networks via Genetic Algorithm Applied to Hip Bone Fracture Prediction

2012 ◽  
Vol 2012 ◽  
pp. 1-9 ◽  
Author(s):  
Yu-Tzu Chang ◽  
Jinn Lin ◽  
Jiann-Shing Shieh ◽  
Maysam F. Abbod
Keyword(s):  
Genetic Algorithm ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Hip Fracture ◽  
Back Propagation ◽  
Training Algorithms ◽  
Mineral Density ◽  
Study Results ◽  
Artificial Neural ◽  
Hidden Layer

This paper aims to find the optimal set of initial weights to enhance the accuracy of artificial neural networks (ANNs) by using genetic algorithms (GA). The sample in this study included 228 patients with first low-trauma hip fracture and 215 patients without hip fracture, both of them were interviewed with 78 questions. We used logistic regression to select 5 important factors (i.e., bone mineral density, experience of fracture, average hand grip strength, intake of coffee, and peak expiratory flow rate) for building artificial neural networks to predict the probabilities of hip fractures. Three-layer (one hidden layer) ANNs models with back-propagation training algorithms were adopted. The purpose in this paper is to find the optimal initial weights of neural networks via genetic algorithm to improve the predictability. Area under the ROC curve (AUC) was used to assess the performance of neural networks. The study results showed the genetic algorithm obtained an AUC of0.858±0.00493on modeling data and0.802±0.03318on testing data. They were slightly better than the results of our previous study (0.868±0.00387and0.796±0.02559, resp.). Thus, the preliminary study for only using simple GA has been proved to be effective for improving the accuracy of artificial neural networks.

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