Measurement of In-Vitro Subcutaneous Fat for LowCost Infant Body Fat Monitoring
Alistair McEwan1, Robert Morhard1,2 Shuning Bian1, Fatin Hamimi Mustafa1, Craig Jin1, Heather Jeffery3
1School
Motivations:
of Electrical and information Engineering
University of Sydney
2Institute for Biomedical Engineering
Swiss Federal Institute of Technology
3School of Public Health
RPA HOSPITAL
Design:
1.Malnutrition has a synergistic effect on disease mortality
-increases vulnerability to severe malaria[1]
-increases risk for acute respiratory tract infections[2]
-increases duration and frequency of diarrhoea[3]
2.Infantile malnutrition stunts cognitive[4,5] and physical[6]
development
3.Infantile malnutrition may be correlated with diabetes[7]
and coronary heart disease in adults[8]
4.No current device is cheap, easy-to-use and accurate for
use with neonates.
Approach:
Light emitting diode (LED)
based device using near
infrared interactance (NIRI)
Features:
• Low
signal
photodiode (PD)
cost
detected
by
•Non-invasive,
and simple to use
3 x NIR LED
-wavelengths to be determined with further testing
Photodiode
-distance from LEDs to be determined
Arduino microcontroller
3D printed case
Future Testing:
Two separate computational models will be used to optimize
accuracy by modulating incident wavelength, source-detector
distance and angle of incident light delivery. Additionally the
feasibility of using a separate LED to account for hydration level will
be examined. They will be confirmed experimentally with optical
phantoms.
Modeling:
1. Monte Carlo Modeling of Multi-Layered Media[9]
2. GEANT4/GAMOS Tissue Optics[10]
Phantoms:
LED
Optical phantoms are made of epoxy with black ink and TiO2 powder
one
to simulate the scattering and absorption of tissue with a layer of
animal fat of varying thickness on top. Reflectance will be measured
safe with a near-infrared spectrometer.
Figure 1: 3D rendering of device design
Preliminary Phantom Testing:
The base layer of our model consists of 12 mm thick lean
pork in a plastic container. Subcutaneous fat was
simulated using two pork fat layers, both 3mm thick.
Source-detector-distance was approximately 2mm. The
optical density ratio is a logarithmic ratio between the two
reflectance values in order to take into account the currentvoltage relationship of the diodes.
Conclusions:
Preliminary results confirm the feasibility of a low-cost malnutrition
diagnostic device based on near-infrared interactance. Further
refinement of optical phantom testing and the addition of
computational simulations will enhance device function in future
prototypes. Future testing in resource-limited settings could be
preformed using skinfold thickness measurements taken by a trained
operator as a gold standard.
0.6
Preliminary Phantom Results:
Figure 2: Optical density ratios for preliminary phantom experiments
Preliminary Clinical Testing
The device was tested on newborns at RPA Hospital
alongside a PEAPOD (an air displacement
plethysmography device intended for neonates).
Reflectance at 940 nm and 1050 nm was measured.
Preliminary Clinical Results
Preliminary clinical results are shown in Figure 3. Percent
body fat was determined via PEAPOD.
0.55
Percent Body Fat
Both fat samples have the same optical density. Placing
the fat samples on top of the lean sample produces an
optical density between that of pure fat and pure lean pork.
This indicates that the optical density can be used to infer
fat thickness.
Sample
Mean Std. Dev.
Fat-1
0.62
0.02
Fat-2
0.62
0.01
Lean
0.47
0.02
Fat-1 over Lean 0.55
0.02
0.5
R² = 0.8294
0.45
0.4
0.35
0.3
5
7
9
11
Optical Density Ratio
13
Figure 3: Results from preliminary clinical trials
Acknowledgements:
The authors would like to acknowledge the support of the Bill and
Melinda Gates Foundation Grand Challenges Scheme.
References
[1] "Malaria and malnutrition." (2005) . World Health Organization . Web. 24 Oct 2013. <http://www.who.int/malaria/publications/atoz/malaria_and_malnutrition.pdf>
[2] "Children: reducing mortality." (Sept 2013). World Health Organization . Web. 24 Oct 2013. <http://www.who.int/mediacentre/factsheets/fs178/en/>.
[3] Lima AAM, Fang G, Schorling JB, et al. Persistent diarrhea in Northeast Brazil: etiologies and interactions with malnutrition. Acta Pediatr 1992;suppl 381:39-44.
[4] Laus, M. F. , et al. “Early Postnatal Protein-Calorie Malnutrition and Cognition: A Review of Human and Animal Studies.” International Journal of Environmental Research and Public Health. 8 (2011): 590-612.
[5] Galler, J.R., C.P. Bryce , et al. "Infant Malnutrition Is Associated with Persisting Attention Deficits in Middle Adulthood." American Society for Nutrition . 142.4 (2012): 788-794
[6] Galler, J.R., F. Ramsey, and G. Solimano. "A Follow-Up Study of the Effects of Early Malnutrition on Subsequent Development. I. Physical Growth and Sexual Maturation during Adolescence ."Pediatric
Research. 19. (1985): 518-523.
[7] van Abeelen, AF., SG. Elias, et al. "Famine Exposure in the Young and the Risk of Type 2 Diabetes in Adulthood." Diabetes. 61. (2012): 2255-2260.
[8] van Abeelen AF, Elias SG, Bossuyt PM, et al. Cardiovascular consequences of famine in the young. Eur Heart J 2012;33:538–545
[9] Glaser, AK, SC Kanick, et al. "A GAMOS plug-in for GEANT4 based Monte Carlo simulation of radiation-induced light transport in biological media." Biomedical Optics Express. 4.5 (2013): 741-759.
[10] Wang, L-H, S.L. Jacques, L-Q Zheng: MCML - Monte Carlo modeling of photon transport in multi-layered tissues. Computer Methods and Programs in Biomedicine 47:131-146, 1995