Patient Benefactor Linker

Patient Benefactor Linker


  • Hira Zahid Biomedical Engineering Department,, Ziauddin University
  • Sidra Abid Syed Biomedical Engineering Department,, Ziauddin University
  • Marissa Jerome Biomedical Engineering Department, Ziauddin University
  • Rida Batool Biomedical Engineering Department, Ziauddin University
  • Sarmad Shams Biomedical Engineering Department, Sir Syed University of Engg. and Technology


Artificial Neural Network, RGB color sensor module, infrared spectroscopy, MATLAB Neural Network Toolbox, , Optimal benefactor-donor match


This paper discusses a new evolution in the healthcare sector through a device by investigating
the principle application of Artificial Neural Networks (ANN) for the selection of an optimal
benefactor-donor match in organ transplantation. The device aims to correlate ABO blood type,
age and bone density of healthy subjects. Firstly, linker phase integrates a light intensity(lux)
meter and an RGB Color Sensor module to perform an experimental observation of agglutination
of RBC's which is measured through a halogen illumination source that measures the light
intensity which is displayed on a screen through the microprocessor interface. Secondly, we
aim to study the possibility of calcium quantification via near-infrared spectroscopy to estimate
bone density which involves the use of an emitting source and a photodiode as a detector/
receiver. At last the device involves designing an Artificial Neural Network (ANN) model through
the Neural Network Toolbox of MATLAB software to get the optimal network architecture
suitable for the analysis. This architecture is achieved by simulating different Artificial Neural
Network (ANN) configurations. We used a non-linear ANN which can predict benefactor and
patient organ matches, while measuring ABO blood typing and calcium density of the donors
in real time and for recognizing mapping functions for which there is no requirement for a
particular basis of functions. A database was created through an intensive survey of benefactor
profiles. The results generated by ANN are promising for identifying optimal benefactor and
patient matches. This approach has potential benefits as an increase in the number of input and
parameters will provide better matches and risk associated with human error are reduced. The
network can further be modelled to predict survival rates.


Simon, DM.Levin, S., 2001. Infectious Complications of Solid Organ Transplantations.

Infectious Disease Clinics of North America, 15(2), 521-549

Nilsson, J. Ohlsson, M. Höglund, P. Ekmehag, B. Koul, B. Andersson, B., 2015. The

International Heart Transplant Survival Algorithm (IHTSA): A New Model to Improve

Organ Sharing and Survival. PLoS ONE-Public Library of Science, [online] Available at

<> [Accessed 10 October 2017]

Opelz, G. Wujciak, T. 1994. The influence of HLA compatibility on graft survival after

heart transplantation. The Collaborative Transplant Study. The New England Journal of

Medicine, 330(12), 816-819.

Costanzo, MR. Costanzo, MR. Dipchand, A. Starling, R. Anderson, A. Chan, M et al .2010.The

International Society of Heart and Lung Transplantation Guidelines for the care of heart

transplant recipients. J Heart Lung Transplant, 29(8), 914–956

Russo, MJ. Iribarne, A. Hong, KN. Ramlawi, B. Chen, JM. Takayama, H et al., 2010. Factors

associated with primary graft failure after heart transplantation. Transplantation, 90(4),


West, LJ.Karamlou, T. Dipchand, A. Pollock-Barziv, SM. Coles, JG. McCrindle, BW. 2006.

Impact on outcomes after listing and transplantation, of a strategy to accept ABO blood

group-incompatible donor hearts for neonates and infants. The Journal of Thoracic and

Cardiovascular Surgery, 131(2), 455-461

Cooper, D.K, 1990. Clinical survey of heart transplantation between ABO blood groupincompatible

recipients and donors. The Journal of Heart Transplantation, 9(4), 376-381.

West, LJ. Pollock-Barziv, SM. Dipchand, AI. Lee, KJ. Cardella, CJ.Benson, LN et al. 2001. ABO-incompatible heart transplantation in infants. The New England Journal of Medicine, 344(11), 793-800.

Mutimer, DJ. Gunson, B. Chen, J. Berenguer, J. Neuhaus, P. Castaing, D. Garcia-Valdecasas,

J C. Salizzoni, M M. G E. Mirza, D., 2006.Impact of Donor Age and Year of Transplantation

on Graft and Patient Survival Following Liver Transplantation for Hepatitis C Virus.

Transplantation, 81(1), 7-14.

Torres, A. Lorenzo, V. Salido, E. 2002.Calcium Metabolism and Skeletal Problems after

Transplantation. Journal of the American Society of Nephrology, 13(2), 551-558.

Foundation, NK. 2003. K/DOQI clinical practice guidelines for bone metabolism and

disease in chronic kidney disease. American Journal of Kidney Diseases, 42(4 Suppl 3):


Bishop, C.M., 1995. Neural Networks for Pattern Recognition. Oxford: Clarendon

Heden B, Ohlin H, Rittner R, Edenbrandt L.1997. Acute myocardial infarction detected in

the 12-lead ECG by artificial neural networks. Circulation. 96(6), 1798-1802

Silipo R, Gori M, Taddei A, Varanini M, Marchesi C.1995. Classification of arrhythmic

events in ambulatory electrocardiogram, using artificial neural networks. Computers and

Biomedical Research. 28,305–318.

Ashizawa K, ET al.1999. Artificial neural networks in chest radiography: application to the

differential diagnosis of interstitial lung disease. Academic Radiology, 6, 2–9.

Abdolmaleki P, ET al.1997. Neural network analysis of breast cancer from MRI findings.

Radiation Medicine.15, 283–293.

Chang, YJ. Chang, W. Lin, YT.2014. Detection of RBC agglutination in blood typing test using

integrated Light-Eye-Technology (iLeyeT).International Symposium on Bioelectronics

and Bioinformatics, IEEE.[e-journal] 10.1109/ISBB.2014.6820952

Niemann, C. Divol, L. Froula, D. Glanzer, S. Gregori, G. Kirkwood, R. Mackinnon, A. Meezan,

N. Moody, J. Source, C. Bahr, R. Seka, W.2005.IEEE International Conference on Plasma


Weiss ET al.2009.Impact of recipient body mass index on organ allocation and mortality

in orthotopic heart transplantation. Journal of Heart and Lung Transplantation. 28(11),


Russo, M. J. et al. 2007. The effect of ischemic time on survival after heart transplantation

varies by donor age: an analysis of the United Network for Organ Sharing database. The

Journal of Thoracic and Cardiovascular Surgery.133, 554-559.

Smith, J. D., Rose, M. L., Pomerance, A., Burke, M. Yacoub, M.H.1995. Reduction of cellular

rejection and increase in longer-term survival after heart transplantation after HLA-DR

matching. Lancet 346, 1318–1322

Kilic, A. et al. 2012. What predicts long-term survival after heart transplantation? An

analysis of 9,400 ten-year survivors. The Annals of Thoracic Surgery.93, 699–704

Winslow, R. L., Trayanova, N., Geman, D. Miller, M. I.2012. Computational medicine:

translating models to clinical care. Science Translational Medicine, 4(158) [e-journal] 10.1126/scitranslmed.3003528.

Albanese, CV. Diessel, E. Genant, HK.2003. Clinical applications of body composition

measurements using DXA. Journal of Clinical Densitometry.6 (2), 75-85.

Chaichanakol, S. Tanaka, SM. Khantachawana, A. 2016. Quantitative Detection of Calcium

using Near Infrared Spectroscopy for apply in Bone Densitometry. International Journal of

Mechanical and Production Engineering.4 (4).

Padalkara, M.V. Pleshkoa, N. 2015. Wavelength-Dependent Penetration Depth of Near

Infrared Radiation into Cartilage.Analyst.140 (7), 2093-2100.

Branch, D.R. “Anti-A and anti-B: what are they and where do they come from?” Transfusion,

vol. 55, pp.S74-S79, July 2015.