Artificial Neural Networks Analysis for Estimating Bone Mineral Density in an Egyptian Population: Towards Standardization of DXA Measurements

Samir M. Abdel-Mageed; Amani M. Bayoumi; Ehab I. Mohamed

doi:doi:10.11648/j.ajnna.20150103.11

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Artificial Neural Networks Analysis for Estimating Bone Mineral Density in an Egyptian Population: Towards Standardization of DXA Measurements

Samir M. Abdel-Mageed, Amani M. Bayoumi, Ehab I. Mohamed

Published in American Journal of Neural Networks and Applications (Volume 1, Issue 3)

Received: 13 January 2016 Accepted: 17 February 2016 Published: 1 March 2016

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Abstract

An extensive amount of information is currently available to clinical specialists, ranging from detailed demographic characteristics to physical examination and various types of biochemical data. The most important concern in the medical field is to consider the interpretation of data and perform accurate diagnosis. Artificial intelligence method and especially artificial neural network (ANN) algorithms can handle diverse types of medical data and integrate them into categorized outputs. A common bone disease ‘osteoporosis’ does not depend only on bone mineral density (BMD) but also on some other factors e.g., age, weight, height, life-style etc., which play considerable role in the diagnosis of osteoporosis. In this study, we propose a decision making system using demographic variables in an Egyptian population to provide a convenient, accurate and inexpensive solution to predict segmental and total BMD and expect future fracture risk for healthy persons and those with pathologic condition known to be related to BMD. We believe the ANN is a promising tool for estimating and predicting segmental and total BMD values using simple demographic characteristics.

Published in	American Journal of Neural Networks and Applications (Volume 1, Issue 3)
DOI	10.11648/j.ajnna.20150103.11
Page(s)	52-56
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2016. Published by Science Publishing Group

Keywords

Bone Mineral Density (BMD), Dual-energy X-ray Absorptiometry (DXA), Osteoporosis, Artificial Neural Network (ANN)

References

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[2]	Francis RM, Sutcliffe AM, Scane AC. Pathogenesis of osteoporosis. In: Osteoporosis. Stevenson JC, Lindsay R, Editors. Philadelphia: Chapman & Hall Medical; Lippincott Williams and Wilkins; 1998. pp. 29-52.
[3]	Sanfelix-Genoves J, Peiro S, Sanfelix-Gimeno G, Hurtado I, Pascual de la Torre M, Trillo-Mata JL, Giner Ruiz V. Impact of a multifaceted intervention to improve the clinical management of osteoporosis. The ESOSVAL-F study. BMC Health Serv Res. 2010; doi: 10.1186/1472-6963-10-292.
[4]	Looker AC, Melton LJ 3rd, Harris T, Borrud L, Shepherd J, McGowan J. Age, gender, and race/ethnic differences in total body and subregional bone density. Osteoporos Int. 2009; 20(7): 1141-1149.
[5]	Brannon PM, Carpenter TO, Fernandez JR, Gilsanz V, Gould JB, Hall KE, Hui SL, Lupton JR, Mennella J, Miller NJ, Osganian SK, Sellmeyer DE, Suchy FJ, Wolf MA. NIH Consensus Development Conference Statement: Lactose Intolerance and Health. NIH Consens State Sci Statements. 2010; 27(2).
[6]	WHO Study Group. Assessment of fracture risk and its application to screening for postmenopausal osteoporosis. WHO Technical Report Series no. 843. Geneva: WHO, 1994; pp. 1-129.
[7]	Report of a WHO Scientific Group. Prevention and Management of Osteoporosis. WHO Technical Report Series no. 921. Geneva: WHO, 2003; pp. 1-206.
[8]	Casini A, Mohamed EI, Gandin C, Tarantino U, Di Daniele N, De Lorenzo A. Predicting bone mineral density of postmenopausal healthy and cirrhotic Italian women using anthropometric variables. Digest Liver Dis. 2003; 35: 881-887.
[9]	Mohamed EI, Maiolo C, Linder R, Pöppl SJ, De Lorenzo A. Artificial neural network analysis: A novel application for predicting site-specific bone mineral density. Acta. Diabetol. 2003; 40: S19-22.
[10]	Mohamed EI, Khalil ES. Bone densitometric analysis in Egyptian hemodialysis patients. Int J Biomed Sci. 2008; 4(2): 120-124.
[11]	Shaikh AB, Sarim M, Raffat SK, Ahsan K, Nadeem A, Siddiq M. Artificial neural network: A tool for diagnosing osteoporosis. Res J Rece Sci. 2014; 3(2): 87-91.
[12]	Jensen JEB., Sharpe PK, Caleb P, Sorensen HA. Fracture prediction using artificial neural networks. Proc. World Congress on Osteoporosis, Amsterdam, 1996; 18-23.
[13]	Sarah AR, Wen JW, Derek P. Artificial neural networks: A potential role in osteoporosis. J R Soc Med. 1999; 92: 119-122.

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Samir M. Abdel-Mageed, Amani M. Bayoumi, Ehab I. Mohamed. (2016). Artificial Neural Networks Analysis for Estimating Bone Mineral Density in an Egyptian Population: Towards Standardization of DXA Measurements. American Journal of Neural Networks and Applications, 1(3), 52-56. https://doi.org/10.11648/j.ajnna.20150103.11

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ACS Style

Samir M. Abdel-Mageed; Amani M. Bayoumi; Ehab I. Mohamed. Artificial Neural Networks Analysis for Estimating Bone Mineral Density in an Egyptian Population: Towards Standardization of DXA Measurements. Am. J. Neural Netw. Appl. 2016, 1(3), 52-56. doi: 10.11648/j.ajnna.20150103.11

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AMA Style

Samir M. Abdel-Mageed, Amani M. Bayoumi, Ehab I. Mohamed. Artificial Neural Networks Analysis for Estimating Bone Mineral Density in an Egyptian Population: Towards Standardization of DXA Measurements. Am J Neural Netw Appl. 2016;1(3):52-56. doi: 10.11648/j.ajnna.20150103.11

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@article{10.11648/j.ajnna.20150103.11,
  author = {Samir M. Abdel-Mageed and Amani M. Bayoumi and Ehab I. Mohamed},
  title = {Artificial Neural Networks Analysis for Estimating Bone Mineral Density in an Egyptian Population: Towards Standardization of DXA Measurements},
  journal = {American Journal of Neural Networks and Applications},
  volume = {1},
  number = {3},
  pages = {52-56},
  doi = {10.11648/j.ajnna.20150103.11},
  url = {https://doi.org/10.11648/j.ajnna.20150103.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnna.20150103.11},
  abstract = {An extensive amount of information is currently available to clinical specialists, ranging from detailed demographic characteristics to physical examination and various types of biochemical data. The most important concern in the medical field is to consider the interpretation of data and perform accurate diagnosis. Artificial intelligence method and especially artificial neural network (ANN) algorithms can handle diverse types of medical data and integrate them into categorized outputs. A common bone disease ‘osteoporosis’ does not depend only on bone mineral density (BMD) but also on some other factors e.g., age, weight, height, life-style etc., which play considerable role in the diagnosis of osteoporosis. In this study, we propose a decision making system using demographic variables in an Egyptian population to provide a convenient, accurate and inexpensive solution to predict segmental and total BMD and expect future fracture risk for healthy persons and those with pathologic condition known to be related to BMD. We believe the ANN is a promising tool for estimating and predicting segmental and total BMD values using simple demographic characteristics.},
 year = {2016}
}

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TY  - JOUR
T1  - Artificial Neural Networks Analysis for Estimating Bone Mineral Density in an Egyptian Population: Towards Standardization of DXA Measurements
AU  - Samir M. Abdel-Mageed
AU  - Amani M. Bayoumi
AU  - Ehab I. Mohamed
Y1  - 2016/03/01
PY  - 2016
N1  - https://doi.org/10.11648/j.ajnna.20150103.11
DO  - 10.11648/j.ajnna.20150103.11
T2  - American Journal of Neural Networks and Applications
JF  - American Journal of Neural Networks and Applications
JO  - American Journal of Neural Networks and Applications
SP  - 52
EP  - 56
PB  - Science Publishing Group
SN  - 2469-7419
UR  - https://doi.org/10.11648/j.ajnna.20150103.11
AB  - An extensive amount of information is currently available to clinical specialists, ranging from detailed demographic characteristics to physical examination and various types of biochemical data. The most important concern in the medical field is to consider the interpretation of data and perform accurate diagnosis. Artificial intelligence method and especially artificial neural network (ANN) algorithms can handle diverse types of medical data and integrate them into categorized outputs. A common bone disease ‘osteoporosis’ does not depend only on bone mineral density (BMD) but also on some other factors e.g., age, weight, height, life-style etc., which play considerable role in the diagnosis of osteoporosis. In this study, we propose a decision making system using demographic variables in an Egyptian population to provide a convenient, accurate and inexpensive solution to predict segmental and total BMD and expect future fracture risk for healthy persons and those with pathologic condition known to be related to BMD. We believe the ANN is a promising tool for estimating and predicting segmental and total BMD values using simple demographic characteristics.
VL  - 1
IS  - 3
ER  -

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Author Information

Samir M. Abdel-Mageed

Physics Department, Faculty of Science, Alexandria University, Alexandria, Egypt
Amani M. Bayoumi

Physics Department, Faculty of Science, Alexandria University, Alexandria, Egypt
Ehab I. Mohamed

Medical Biophysics Department, Medical Research Institute, Alexandria University, Alexandria, Egypt

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Plain Text BibTeX RIS

APA Style

Samir M. Abdel-Mageed, Amani M. Bayoumi, Ehab I. Mohamed. (2016). Artificial Neural Networks Analysis for Estimating Bone Mineral Density in an Egyptian Population: Towards Standardization of DXA Measurements. American Journal of Neural Networks and Applications, 1(3), 52-56. https://doi.org/10.11648/j.ajnna.20150103.11

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ACS Style

Samir M. Abdel-Mageed; Amani M. Bayoumi; Ehab I. Mohamed. Artificial Neural Networks Analysis for Estimating Bone Mineral Density in an Egyptian Population: Towards Standardization of DXA Measurements. Am. J. Neural Netw. Appl. 2016, 1(3), 52-56. doi: 10.11648/j.ajnna.20150103.11

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AMA Style

Samir M. Abdel-Mageed, Amani M. Bayoumi, Ehab I. Mohamed. Artificial Neural Networks Analysis for Estimating Bone Mineral Density in an Egyptian Population: Towards Standardization of DXA Measurements. Am J Neural Netw Appl. 2016;1(3):52-56. doi: 10.11648/j.ajnna.20150103.11

Copy | Download

@article{10.11648/j.ajnna.20150103.11,
  author = {Samir M. Abdel-Mageed and Amani M. Bayoumi and Ehab I. Mohamed},
  title = {Artificial Neural Networks Analysis for Estimating Bone Mineral Density in an Egyptian Population: Towards Standardization of DXA Measurements},
  journal = {American Journal of Neural Networks and Applications},
  volume = {1},
  number = {3},
  pages = {52-56},
  doi = {10.11648/j.ajnna.20150103.11},
  url = {https://doi.org/10.11648/j.ajnna.20150103.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajnna.20150103.11},
  abstract = {An extensive amount of information is currently available to clinical specialists, ranging from detailed demographic characteristics to physical examination and various types of biochemical data. The most important concern in the medical field is to consider the interpretation of data and perform accurate diagnosis. Artificial intelligence method and especially artificial neural network (ANN) algorithms can handle diverse types of medical data and integrate them into categorized outputs. A common bone disease ‘osteoporosis’ does not depend only on bone mineral density (BMD) but also on some other factors e.g., age, weight, height, life-style etc., which play considerable role in the diagnosis of osteoporosis. In this study, we propose a decision making system using demographic variables in an Egyptian population to provide a convenient, accurate and inexpensive solution to predict segmental and total BMD and expect future fracture risk for healthy persons and those with pathologic condition known to be related to BMD. We believe the ANN is a promising tool for estimating and predicting segmental and total BMD values using simple demographic characteristics.},
 year = {2016}
}

Copy | Download

TY  - JOUR
T1  - Artificial Neural Networks Analysis for Estimating Bone Mineral Density in an Egyptian Population: Towards Standardization of DXA Measurements
AU  - Samir M. Abdel-Mageed
AU  - Amani M. Bayoumi
AU  - Ehab I. Mohamed
Y1  - 2016/03/01
PY  - 2016
N1  - https://doi.org/10.11648/j.ajnna.20150103.11
DO  - 10.11648/j.ajnna.20150103.11
T2  - American Journal of Neural Networks and Applications
JF  - American Journal of Neural Networks and Applications
JO  - American Journal of Neural Networks and Applications
SP  - 52
EP  - 56
PB  - Science Publishing Group
SN  - 2469-7419
UR  - https://doi.org/10.11648/j.ajnna.20150103.11
AB  - An extensive amount of information is currently available to clinical specialists, ranging from detailed demographic characteristics to physical examination and various types of biochemical data. The most important concern in the medical field is to consider the interpretation of data and perform accurate diagnosis. Artificial intelligence method and especially artificial neural network (ANN) algorithms can handle diverse types of medical data and integrate them into categorized outputs. A common bone disease ‘osteoporosis’ does not depend only on bone mineral density (BMD) but also on some other factors e.g., age, weight, height, life-style etc., which play considerable role in the diagnosis of osteoporosis. In this study, we propose a decision making system using demographic variables in an Egyptian population to provide a convenient, accurate and inexpensive solution to predict segmental and total BMD and expect future fracture risk for healthy persons and those with pathologic condition known to be related to BMD. We believe the ANN is a promising tool for estimating and predicting segmental and total BMD values using simple demographic characteristics.
VL  - 1
IS  - 3
ER  -

Copy | Download