Journal of Biophysics and Biomedical Sciences Journal of Biophysics and Biomedical Sciences March 2008; 1(1): 2-6
SCIENCE AND TECHNOLOGY REPORT
Artificial Neural Networks for Predicting Human Body Compartments EHAB I. MOHAMED1, ROLAND LINDER2, ANTONINO DE LORENZO3, AND ABDEL–SATTAR M. SALLAM4 1
Medical Biophysics Department, Medical Research Institute, Alexandria University, Alexandria, EGYPT 2 Institute of Medical Informatics, Medical University of Lübeck, Lübeck, GERMANY 3 Department of Neurosciences, Faculty of Medicine and Surgery, University of Tor Vergata, Rome, ITALY 4 Biophysics Unit, Physics Department, Faculty of Science, Ain Shams University, Cairo, EGYPT
ABSTRACT The measurements of intrinsic human body compartments are important because body-composition and its topology are known to be associated with certain physiologic and pathological conditions. Herein we suggest the usage of artificial neural networks for precisely estimating, and predicting, the human body water and bone compartments. The application of this methodology allows to assess the complex and non-linear relationships between body compartments and anthropometric variables. It could be applied to all human body compartments, precise estimates of which could aid both general practitioners and specialists in defining the health status of an individual and in diagnosing certain pathologies. Keywords: Artificial Neural Networks, Body Composition, Intracellular Water, Bone Mineral Density. The objective of research in human body composition has traditionally been to estimate the masses of intrinsic body compartments (e.g., fat, muscle, bone, and water). One of the reasons for which measurements of these compartments are important is that body–composition and body– composition topology are known to be associated with certain pathological conditions (e.g., obesity, AIDS, malnutrition, pathologies related to dehydration, cardiovascular disease, diabetes, certain cancers, and osteoporosis) (1–3). However, it is not always possible to measure human body compartments directly because of either technical difficulties or for ethical reasons. Researchers have thus developed methods to determine in vivo body composition on the basis of measurable radioactive, electromagnetic, and physiological properties of the human body, which are mathematically transformed into the component mass of interest using simple linear descriptive and/or mechanistic mathematical functions (4). __________________________________________________________ Corresponding author: Prof. Ehab I. Mohamed, Department of Medical Biophysics, Medical Research Institute, Alexandria University, 165 ElHorreya Avenue, 21561 Alexandria, EGYPT. Tel: (+20) 3 428 2331/ 2373/ 3543/ 5455; Fax: (+20) 3 428 3719; Mobile: (+20) 12 932 2010; E-mail:
[email protected]. Received December 17, 2007; Accepted January 5, 2008.
Nevertheless, most biological and pathophysiological processes of the human body are non-linear and manifest chaotic behavior and are thus poorly described by classical linear methods (5, 6). We show here that it is possible to apply artificial neural networks (ANN) to reliably model the complex human body and to accurately estimate the masses of its compartments. Although ANNs, which are information-processing programs designed to simulate the way the nervous system is believed to operate, have existed since the late 1950s, it was not until the mid1980s that their algorithms became sophisticated enough for general applications (7, 8). ANNs are based on neurons joined together by weighted connections in a variety of ways to form networks capable of learning, memorizing and creating relationships amongst data (8, 9). They can be used for system modeling, where the physical processes are not understood or are highly complex (e.g., human body). ANNs have been frequently applied for classifying clinical data and patients (10–12) and for diagnosing and predicting the outcome of persistent pathologies (e.g., type 2 diabetes, myocardial infarction, and cancer) (5, 9). We developed an approach for estimating and predicting the masses of intrinsic body compartments, using ANNs, as shown in Figure 1. Herein, we describe the application of this
Copyright © 9942 – 2008. Published by the Middle-East Biophysical Society—Medical Research Institute—Alexandria University.
EHAB I. MOHAMED et al. Journal of Biophysics and Biomedical Sciences March 2008; 1(1): 2-6
Figure 1. The unknown intrinsic compartmental composition of the human body is represented by the illustrated human form. Anthropometric measurements [i.e., sex, age, weight, height, body mass index (BMI), waist-to-hip ratio (W/H), and sum of four skinfold thickness (Sum SF)] of the human body are fed to a multilayered artificial neural network ("Input Layer"), which learns associations among these variables and reference values for the compartment of interest (in this case, intracellular water compartment and total and site-specific bone mineral densities), using an adaptive propagation algorithm for automatic training. In the “Hidden Layers”, the artificial neural network constructs a predictor function in which the anthropometric variables are used to determine the corresponding value for the compartment of interest for a given subject. Quantitative estimates of intracellular water (ICW), total bone mineral density (BMDtotal), bone mineral density of the lumbar spine (BMDspine), and bone minertal density of the pelvis (BMDpelvis) are given in the “Output Layer”.
that radioactive tracers are used. To overcome this difficulty, in the 1980s mathematical formulae were developed for calculating the masses not only of TBW and ECW but also of ICW (15), based on resistance and reactance measurements of a low alternating current applied to the human body, provided by a bioelectric impedance analysis (BIA) or a bioelectric impedance spectroscopy (BIS). However, these calculations can vary greatly because of differences in the circuit design of the various models of BIA and BIS instruments, which are produced by various manufacturers, and, more importantly, because of differences in the independent variables used in the mathematical formulae, which are population-specific. Furthermore, the methods for validating these calculations against the reference values are based on peculiar approximations using linear regression methods, which have resulted in relatively low coefficients of determination for water compartments. This adds another source of error to the margin of error in the reference methods. To overcome these problems, researchers have used the whole
approach for estimating the volume of the intracellular water compartment and the total and site–specific bone mineral densities. The normal healthy human body contains approximately 50–60% water by weight (13). The total body water (TBW) is composed of the water inside the cells (intracellular water, ICW) and the water outside the cells (extracellular water, ECW). The volumes of theses three water compartments cannot be measured with the same ease and precision. The reference methods for measuring TBW and ECW, respectively, are the dilution of deuterium oxide (D2O) and of sodium bromide (NaBr). The ICW is measured by calculating the difference between TBW and ECW, which makes it subject to inaccuracies and cumulative errors (typically ± 2-3 liters) (14), which result from the use of separate dilution methods for the other two compartments. The ICW can also be measured directly using the dilution of radioactive 42K, which is, however, no longer commercially available. The major disadvantage of dilution techniques is their invasiveness, in 3
ARTIFICIAL NEURAL NETWORKS FOR PREDICTING HUMAN BODY COMPARTMENTS
body 40K counting technique to obtain accurate, precise, and reproducible estimates of the ICW compartment. However, because it is expensive and requires specific calibration skills and special infrastructure and operating conditions, this technique is only used in a few research centers worldwide. In light of these considerations, we applied ANN to quantitatively estimate the volume of the ICW compartment for a population of 100 healthy Italians on the basis of anthropometric and BIS data. For each participant, we measured anthropometric variables (i.e., sex, age, weight, height, body mass index, waist−to−hip ratio, and the sum of four skinfold thickness), BIS variables at different frequencies (model 4000B, Xitron Technologies Inc., San Diego, CA) [i.e., resistance, reactance, phase angle, and the indexes (height2 × resistance−1) (16) and (height1.60 × parallel reactance−0.50) (17)], and, as reference, ICW using the whole body 40K counting technique (Ametek Inc., PA, USA). The variables were fed to a modular ANN (produced by one of us RL), which was connected to an adaptive propagation algorithm for automatic training (18), which produced quantitative estimates of the ICW. The “Leave One Out” method was used to test the accuracy of the estimates using unknown ICW val-
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ues. This method requires deriving ANN calculations using (n–1) ICW values and determining the remaining one, repeating this procedure n times. The final output of the ANN consisted of the activities of one output neuron, which coded the continuous independent variable “ICW” within the quasi–linear section of its sigmoid activation function (Figure 1). To test the accuracy of the estimates, errors were computed as the root mean squared error algorithm, using the ICW values obtained with the whole body 40 K counting technique as the reference values. The error analysis showed that the ICW estimates obtained using 8 independent variables (i.e., anthropometric variables and the BIS index height1.60 × parallel reactance−0.50) best approximated the reference values, compared with the estimates based on 4 and 7 independent variables, as plotted in Figure 2. The estimates obtained using the ANN analysis also had a lower estimating error than those obtained with conventional BIA and BIS formulae. Based on these results, we propose producing a new generation of BIA and BIS instruments that use ANNs to accurately estimate the volume of human body water compartments (i.e., TBW, ECW, and ICW). Simply by updating the anthropometric variables, this method can be used to track and predict body water
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Figure 2. The artificial neural network learns reference values for the intracellular water, using the whole body 40K counting technique, and it develops non-linear associations with the anthropometric variables. The estimates obtained using 4 independent variables (i.e., sex, age, weight, and height) are less precise (as shown by the root mean square error analysis) than those obtained using 7 independent variables (i.e., the preceding 4 plus body mass index, waist-to-hip ratio, and the sum of four skinfold thickness). The best estimates were yielded using 8 independent variables (i.e., the last 7 plus the bioelectric impedance index height1.60 × parallel reactance-0.5).
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EHAB I. MOHAMED et al. Journal of Biophysics and Biomedical Sciences March 2008; 1(1): 2-6
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Figure 3. The figure shows the average (± SE) site-specific bone mineral density (lumbar spine and pelvis) (panels A and B) and the total bone mineral density (panel C) for healthy persons and for persons with various conditions known to affect bone health. Bone mineral densities were measured using the dual−energy X−ray absorptiometry (DXA) technique. The estimates of site-specific and total bone mineral density for healthy persons, obtained using 4 independent variables (i.e., sex, age, weight, and height) were comparable with reference values. The accuracy of the estimates did not improve when using 7 independent variables (i.e., the preceding 4 plus body mass index, waist−to−hip ratio, and the sum of four skinfold thickness); this was also seen for persons with type 2 diabetes and menopause. The introduction of the condition as an additional independent variable in the ANN, in the form of 7 dummy variables (“1 out of C” coding because of the categorical character of this variable), which resulted in a total of 14 independent variables, produced estimates that were comparable with reference values for hepatic cirrhosis, post-menopause, osteoporosis, and acromegaly. Twelve features selected from the independent 14 variables for lumbar spine bone mineral density (panel A) and 8 for pelvis bone mineral density (panel B) had the same predictive power as the 14 independent variables. Three selected features for total bone mineral density (panel C) had a predictive power comparable to the reference values of the total BMD, except for hepatic cirrhosis, post-menopause, and osteoporosis.
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ARTIFICIAL NEURAL NETWORKS FOR PREDICTING HUMAN BODY COMPARTMENTS
features (i.e., the optimal set of variables obtained using neural net clamping technique). We believe that the ANN analysis is a promising tool for diagnosing bone health and for predicting, years in advance, fractures in patients at risk (i.e., patients with osteoporosis). Moreover, a new generation of DXA instruments equipped with multilayered ANNs based on population–specific reference databases (or universal databases reached via the worldwide web) could yield precise and reproducible estimates for body-composition compartments in general and BMD in particular.
compartments, which for certain pathologies could prove critical to human health. We also applied the ANN analysis to estimate the total and site–specific bone mineral densities. Bone mineral density (BMD) is an indicator of bone health and the most accurate predictor of the risk of fracture, which increases exponentially as BMD decreases, and it is fundamental in clinically diagnosing osteopenia and osteoporosis (19). Monitoring BMD is particularly important when considering that pharmacological treatment for osteoporosis, although effective in maintaining bone mass, does not considerably increase BMD, so that prevention is of utmost importance. Dual-energy X–ray Absorptiometry (DXA) is rapidly gaining wide acceptance as the gold-standard for measuring BMD at the lumbar spine, femur, and forearm (CV = 1%) (19). However, DXA scans cannot be used for the large-scale monitoring of total body or site-specific BMD for various reasons, such as: 1) exposure to X−rays (although as low as 5–10 mSv) (14), which is especially hazardous for children and pregnant women; 2) extremely high costs; and 3) the impossibility of its use for certain human categories (e.g., morbidly obese persons). We applied the ANN analysis to estimate BMD for 600 healthy individuals and 400 individuals with pathologic and altered conditions (i.e., hepatic cirrhosis, type 2 diabetes, menopause, post–menpause, osteoporosis, and acromegaly), who ranged in age from 13 to 79 years. For each participant, we measured anthropometric variables (i.e., sex, age, weight, height, body mass index, waist-tohip ratio, and sum of four skinfold thickness). To obtain BMD reference values for the lumbar spine (BMDspine), pelvis (BMDpelvis), and total body (BMDtotal), each participant underwent a total–body scan using DXA (DPX Pro, GE Medical Systems, Milwaukee, WI, USA). As performed for estimating the mass of the ICW compartment, anthropometric variables were fed to a modular network architecture (Figure 1) connected to an adaptive propagation algorithm, which produced quantitative BMD estimates. The “Leave One Out” method was used to test the accuracy of the estimates obtained with the ANN using unknown BMD values (both total and site−specific). Moreover, we used a 10−fold cross− validation method for training and testing the ANN, based on BMD reference values. The final output of the ANN consisted of the activities of one output neuron, which coded the continuous independent variable “BMD” within the quasi–linear section of its sigmoid activation function. The BMD values estimated using the ANN analysis were compared with reference values, and the errors generated by the ANN were calculated as the root mean squared error algorithm. As shown in Figure 3, the comparison with the reference values showed that the estimates of site-specific and total BMD obtained using 14 independent variables were more accurate than those obtained using 4 and 7 variables and selected
The application of this methodology, which allows the complex and non–linear relationships between body compartments and anthropometric variables to be assessed, need not be limited to the ICW compartment and total and site–specific BMD. It could be applied to all human body compartments, precise estimates of which could aid both general practitioners and specialists in defining the health status of an individual and in diagnosing certain pathologies. REFERENCES 1. G. W. Welch, M. R. Sowers, J. Nutr. 130, 2371 (2000). 2. A. De Lorenzo, C. Maiolo, E. I. Mohamed et al., Chest 119, 1409 (2001). 3. C. Maiolo, E. I. Mohamed, et al., Diabetes Care 24, 961 (2001). 4. S. B. Heymsfield, Z. Wang, R. N. Baumgartner et al., Annu. Rev. Nutr. 17, 527 (1997). 5. W. G. Baxt, Lancet 346, 1135 (1995). 6. S. H. Strogatz, Nature 410, 268-276 (2001) 7. J. Knight, Nature 419, 244 (2002). 8. Battelle Memorial Institute, Pacific Northwest National Laboratory, Artificial Neural Netwroks. http:// www.emsl.pnl.gov:2080/proj/neuron/neural/what.html (28 November 2002). 9. E. I. Mohamed et al., Diabetes Nutr. Metab. 15, 215 (2002) 10. N. A. Ignat'ev, F. T. Adilova, G. R. Matlatipov, P. P. Chernysh, Medinfo. 10, 1354 (2001). 11. M. Juhola, J. Laurikkala, K.Viikki, E. Kentala, I. Pyykko, Acta Otolaryngol. Suppl. 545, 50 (2001). 12. J. V. Tu, J. Clin. Epidemiol. 49, 1225 (1996). 13. G. B. Forbes, Ed. Human Body Composition: Growth, Aging, Nutrition, and Activity (Springer-Verlag New York Inc., 1987). 14. K. J. Ellis, Physiol. Rev. 80, 649 (2000). 15. C. P. Earthman et al., J. Appl. Physiol. 88, 944 (2000). 16. A. De Lorenzo, N. Candeloro, A. Andreoli, P. Deurenberg, Ann. Nutr. Metab. 39, 177 (1995). 17. D. P.Kotler, S. Burastero, J. Wang, R. N. Pierson, Am. J. Clin Nutr. 63, 489S (1996). 18. R. Linder, S. J. Pöppl, Lect. Notes Comput. Sc. 2199, 168 (2001). 19. E. S. Siris et al., JAMA 286, 2815 (2001). 20. The authors would like to express their deepest gratitude to Dr. C. Maiolo and Dr. R. Martinoli for their invaluable assistance with DXA measurements. The authors are indebted to the medical staff of the Center for the Cure and Prevention of Diabetes Mellitus of the “Tor Vergata” University (Rome, Italy) and the medical staff of the 2nd Gastroentrology Division of the University of Rome “La Sapienza” (Rome, Italy). Last but not least, the authors also would like to thank Mark Kanieff for editorial assistance.
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