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Neonatal Jaundice Detection System

  • Transactional Processing Systems
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Abstract

Neonatal jaundice is a common condition that occurs in newborn infants in the first week of life. Today, techniques used for detection are required blood samples and other clinical testing with special equipment. The aim of this study is creating a non-invasive system to control and to detect the jaundice periodically and helping doctors for early diagnosis. In this work, first, a patient group which is consisted from jaundiced babies and a control group which is consisted from healthy babies are prepared, then between 24 and 48 h after birth, 40 jaundiced and 40 healthy newborns are chosen. Second, advanced image processing techniques are used on the images which are taken with a standard smartphone and the color calibration card. Segmentation, pixel similarity and white balancing methods are used as image processing techniques and RGB values and pixels’ important information are obtained exactly. Third, during feature extraction stage, with using colormap transformations and feature calculation, comparisons are done in RGB plane between color change values and the 8-color calibration card which is specially designed. Finally, in the bilirubin level estimation stage, kNN and SVR machine learning regressions are used on the dataset which are obtained from feature extraction. At the end of the process, when the control group is based on for comparisons, jaundice is succesfully detected for 40 jaundiced infants and the success rate is 85 %. Obtained bilirubin estimation results are consisted with bilirubin results which are obtained from the standard blood test and the compliance rate is 85 %.

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References

  1. Click, R., Dahl-Smith, J., Fowler, L., DuBose, J., Deneau-Saxton, M., and Herbert, J., An osteopathic approach to reduction of readmissions for neonatal jaundice. Osteopathic Family Physician. 5(1):17–23, 2013.

    Article  Google Scholar 

  2. Brown A.K., Kernicterus: Past, Present, and Future. NeoReviews. Am. Acad. Pediatrics. 4 (2), 2003.

  3. Madlon-Kay, D. J., Recognition of the presence and severity of newborn jaundice by parents, nurses, physicians, and icterometer. Pediatrics. 100 (3), 1997.

  4. Garfunkel, L.C., Kaczorowski, J., Christy, C., Mosby's pediatric clinical advisor: instant diagnosis and treatment. Elsevier Health Sciences. 2002.

  5. Arias, I.M., Gartner, L.M., Seifter, S., and Furman, M., Prolonged neonatal unconjugated hyperbilirubinemia associated with breast feeding and a steroid, pregnane-3 (alpha), 20 (beta)-diol in maternal milk that inhibits glucuronide formation in vitro. J. Clin. Invest. 43(11):2037–2047, 1964.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Murphy, J.F., Hughes, I., Verrier Jones, E.R., Gaskell, S., and Pike, A.W., Pregnanediols and breast-milk jaundice. Arch. Dis. Child. 56:474–476, 1981.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Hafkamp, A.M., Oral treatment of unconjugated hyperbilirubinemia. Ponsen & Looijen B.V, Wageningen, The Netherlands, 2006.

    Google Scholar 

  8. McDonagh, A.F., Movement of bilirubin and bilirubin conjugates across the placenta. Pediatrics. 119(5):1032–1033, 2007.

    Article  PubMed  Google Scholar 

  9. Kramer, L.I., Advancement of dermal icterus in the jaundiced newborn. Amer. J. Dis. Child. 118:454–458, 1969.

    CAS  PubMed  Google Scholar 

  10. Burke, B., Robbins, J., and Hobbs, C., American Academy of Pediatrics Subcommittee on hyperbilirubinemia. Management of hyperbilirubinemia in the newborn infant 35 or more weeks of gestation. Pediatrics. 114(1):297–316, 2004.

    Article  Google Scholar 

  11. Cremer, R.J., Perryman, P.W., and Richards, D.H., Influence of light on the hyperbilirubinaemia of infants. The Lancet, Elsevier. 271(7030):1094–1097, 1958.

    Article  Google Scholar 

  12. Cappellini, M.D., Di Montemuros, F.M., Sampietro, M., Tavazzi, D., and Fiorelli, G., The interaction between Gilbert's syndrome and G6PD deficiency influences bilirubin levels. Br. J. Haematol. 104(4):928–929, 1999.

    Article  CAS  PubMed  Google Scholar 

  13. Newman, T.B., Escobar, G.J., Gonzales, V.M., Armstrong, M.A., Gardner, M.N., Folck, B.F. Frequency of neonatal bilirubin testing and hyperbilirubinemia in a large health maintenance organization. Pediatrics. 104 (6), 1999.

  14. Stokowski, L.A., Fundamentals of phototherapy for neonatal jaundice. Adv. Neonatal. Care. 6(6):303–312, 2006.

    Article  PubMed  Google Scholar 

  15. Geifman-Holtzman, O., Wojtowycz, M., Kosmas, E., and Artal, R., Female Allo-immunization with antibodies known to cause hemolytic disease. Obstet. Gynecol. 89(2):272–275, 1997.

    Article  CAS  PubMed  Google Scholar 

  16. Mollison, P.L., Engelfriet, C.P., and Contreras, M., Blood transfusion in clinical medicine, 10th edn. Blackwell Science, Oxford, 1997.

    Google Scholar 

  17. Shapiro, L.G., and Stockman, G.C., Computer vision. Prentice-Hall, New Jersey, pp. 279–325, 2001.

    Google Scholar 

  18. Rosenfeld, A., and Kak, A.C., Digital picture processing. Academic Press, San Diego, 1982.

    Google Scholar 

  19. Hsien-Che, L., Introduction to color imaging science. Cambridge University Press. pp 450, 2005.

  20. Sharma, G., and Bala, R., Digital color imaging handbook. CRC Press, Boca Raton, 2002.

    Book  Google Scholar 

  21. De Greef, L., Goel, M., Seo, M.J., Larson, E.C., Stout, J.W., Taylor, J.A., Patel, S.N., Bilicam: using mobile phones to monitor newborn jaundice. UbiComp '14 Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing. 331–342, 2014.

  22. Forcade, N., Le Guyader, C., and Gout, C., Generalized fast marching method: applications to image segmentation. Numerical Algorithms. 48(1):189–211, 2008.

    Article  Google Scholar 

  23. Horowitz, S.L., Pavlidis, T., Picture segmentation by a directed split and merge procedure. Proc. ICPR. Denmark. 424–433, 1974.

  24. Haddad, R.A., and Akansu, A.N., A class of fast gaussian binomial filters for speech and image processing. IEEE Trans. Acoust. Speech Signal Process. 39(3):723–727, 1991.

    Article  Google Scholar 

  25. Haindl, M., Mikes, S., Texture segmentation benchmark. Proc. of the 19th Int. conference on pattern recognition. IEEE Computer Society. 1–4, 2008.

  26. Chan, T.F., and Vese, L., Active contours without edges. IEEE Trans. Image Process. 10(2):266–277, 2001.

    Article  CAS  PubMed  Google Scholar 

  27. Pratt, W.K., Digital image processing, 4th edn. Hoboken, John Willey & Sons, 2007.

    Book  Google Scholar 

  28. Demirci, R., Rule-based automatic segmentation of color images. AEU Int. J. Electron. Commun. 60(6):435–442, 2006.

    Article  Google Scholar 

  29. Funt, B., Cardei, V., Barnard, K., Learning color constancy. Proceedings of the Fourth IS&T/SID Color Imaging Conference, 58–60, 1996.

  30. Yule, J.A.C., Principles of color reproduction. Wiley, New York, 1967.

    Google Scholar 

  31. Viggiano, J.A.S., Comparison of the accuracy of different white balancing options as quantified by their color constancy. Sensors and camera Systems for Scientific, industrial, and digital photography applications V: proceedings of the SPIE. Bellingham. WA: SPIE: the International Society for Optical Engineering. 5301:323–333, 2004.

    Google Scholar 

  32. Petschnigg, G., Szeliski, R., Agrawala, M., Cohen, M., Hoppe, H., and Toyama, K., Digital photography with flash and no-flash image pairs. ACM Trans. Graph. 23(3):664–672, 2004.

    Article  Google Scholar 

  33. Schwarz, M.W., Cowan, W.B., and Beatty, J.C., An experimental comparison of RGB, YIQ, LAB, HSV, and opponent color models. ACM Trans. Graph. 6(2):123–158, 1987.

    Article  Google Scholar 

  34. Florack, L., and Kuijper, A., The topological structure of scale-space images. J. Math. Imaging Vision. 12(1):65–79, 2000.

    Article  Google Scholar 

  35. Liu, D.Y., Chen, H.L., Yang, B., Lv, X.E., Li, L.N., and Liu, J., Design of an Enhanced Fuzzy k-nearest neighbor classifier based computer aided diagnostic system for thyroid disease. J. Med. Syst. 36(5):3243–3254, 2012.

    Article  PubMed  Google Scholar 

  36. Coomans, D., and Massart, D.L., Alternative k-nearest neighbour rules in supervised pattern recognition: Part 1. k-Nearest neighbour classification by using alternative voting rules. Anal. Chim. Acta. 136:15–27, 1982.

    Article  CAS  Google Scholar 

  37. Nouira, K., and Trabelsi, A., Intelligent monitoring system for intensive care units. J. Med. Syst. 36(4):2309–2318, 2012.

    Article  PubMed  Google Scholar 

  38. Berikol, G.B., Yildiz, O., and Özcan, İ.T., Diagnosis of acute coronary syndrome with a support vector machine. J. Med. Syst. 40:84, 2016.

    Article  PubMed  Google Scholar 

  39. Cortes, C., and Vapnik, V., Support-vector networks. Mach. Learn. 20(3):273, 1995.

    Google Scholar 

  40. Leartveravat, S., Transcutaneous bilirubin measurement in full term neonate by digital camera. Medical Journal of Srisaket Surin Buriram Hospitals. 24(1):105–118, 2009.

    Google Scholar 

  41. Nagar, G., Vandermeer, B., Campbell, S., and Kumar, M., Reliability of transcutaneous bilirubin devices in preterm infants: a systematic review. Pediatrics. 132(5):871–881, 2013.

    Article  PubMed  Google Scholar 

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Authors and Affiliations

  1. Pediatrics-Neonatology, Fırat University, Elazig, Turkey

    Mustafa Aydın

  2. Electrical Electronics Engineering, Gazi University, Ankara, Turkey

    Fırat Hardalaç, Berkan Ural & Serhat Karap

Authors
  1. Mustafa Aydın
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  2. Fırat Hardalaç
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  3. Berkan Ural
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  4. Serhat Karap
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Correspondence to Fırat Hardalaç.

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This article is part of the Topical Collection on Transactional Processing Systems

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Aydın, M., Hardalaç, F., Ural, B. et al. Neonatal Jaundice Detection System. J Med Syst 40, 166 (2016). https://doi.org/10.1007/s10916-016-0523-4

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